nature and nurture the study of twins from educational aus

Decoding Nature and Nurture: Insights from Twin Studies

Article in the Press by William A. Hasetline

Published: April 20, 2024 Publisher: Psychology Today Read original article on Psychology Today

• Twin study using brain imaging reveals genetic influence on cognition, with less impact on emotion processing.
• One notable finding suggests genetics play a role in distinguishing between disgust and fear.

The nature versus nurture conundrum is an eternal debate. A recent study of 175 identical and 88 fraternal adult twins explores some of the questions of how genes and the environment determine the fundamental aspects of the emotional and rational life of humans.

The study, led by Haeme Park at Neuroscience Research Australia and recently published in the Human Brain Mapping journal, used advanced brain imaging techniques to investigate cognitive and emotional processes. By examining data from twins, the researchers sought to unravel the relative influence of genes and the environment on brain function.

What Are Twin Studies?

Using structural equation modeling, it is possible to break down the total variability observed in a specific trait, like conscious emotion recognition or sustained attention , into different components that contribute to this variability. These components include genetic factors, shared environmental effects, and individual environmental effects. By understanding how much of the variance in a trait is due to genetics (heritability) versus environmental factors, researchers can gain insights into the underlying mechanisms influencing human behavior.

Twin studies involve comparing data from identical and fraternal twins to understand the role of genetics and environment on various traits. Identical twins share close to all of their genetic material, while fraternal twins only share about 50 percent, allowing for a comparison that highlights genetic influences. Because twins often grow up in the same place, there is also less variance in environmental factors.

The recent study by Park used data from the large TWIN-E cohort study, which includes 1,669 healthy Australian twin adults split almost evenly between identical and fraternal and male and female twin pairs. The goal of the ongoing TWIN-E study is to identify biomarkers that influence emotional brain health over time.

The researchers collected extensive data, including online assessments, electroencephalograms, functional magnetic resonance imaging (fMRI), and cognitive tasks. The fMRI data was obtained from a subset of 263 participants which were then included in Park’s study.

Cognitive and Emotional Tasks

Previous studies have determined that genetics play a significant role in the structural development of different regions of the brain, but few have looked at genetics and brain function using brain imaging while participants actively complete a task.

For the Park study, the twins completed five tasks while having their brains scanned using functional magnetic resonance imaging. Two tasks measured their emotional responses: a nonconscious processing of emotional faces task and a conscious processing of emotional faces task. The participants were shown standardized faces depicting anger , fear , sadness, disgust, happiness , or neutral expressions. For the nonconscious version, the emotional faces were shown for only ten milliseconds before being masked by a neutral face so that there would not be a conscious processing of the emotion. For the conscious version, the emotional face was presented for 500 milliseconds. The participants were asked at the end how many different emotions they observed for each task.

The other three tasks measured cognition : a working memory and sustained attention task, a response inhibition task, and a selective attention and novelty processing task.

The N-back test measured working memory and sustained attention by showing a letter on a screen for 200 milliseconds and asking participants to remember which letters were yellow.

The Go-NoGo task measured response inhibition and involved participants pressing on a green “go” stimulus but ignoring the red “NoGo” stimulus.

The Oddball task measured selective attention and novelty processing by asking participants to respond to audible tones presented at 1000 Hertz and ignoring the tones presented at 50 Hertz.

While participants worked on the tasks, the functional magnetic resonance imaging would light up, revealing which parts of the brain were activated. The researchers then measured the brain activation and compared them across participants.

Study Results

In order to quantify the associations of heritability and brain activity, the researchers used two different methods: a multivariate independent component analysis (ICA) approach and a univariate brain region-of-interest (ROI) approach.

Independent component analysis is a statistical analysis that involves separating data into independent components that represent different sources of information. Researchers can detect local functional connectivity networks within the brain and identify distinct patterns and structures within the data. The univariate region of interest approach allows researchers to focus on specific brain regions known to be involved in cognitive and emotional functions. This method involves analyzing the activity of these predefined brain regions to assess their heritability.

For the working memory, sustained attention, nonconscious processing of positive and negative emotional faces, and selective attention tasks, the participants’ brain function all showed a small to moderate genetic influence, while conscious processing of emotion and response inhibition showed no evidence of heritability. Overall, the functional networks related to executive functions showed the most prominent evidence of genetic influence.

The independent component analysis results showed that the heritability of brain function depended on the particular task. For subconscious emotion recognition, the brain network involving the superior temporal gyrus and insula showed a significant genetic influence when individuals were exposed to nonconscious disgust compared to neutral stimuli (26 percent) and nonconscious fear compared to happy stimuli (23 percent). For the working memory networks, including the fronto-parietal region and the inferior parietal lobule, a significant heritability estimate was found (27 percent). The sustained attention networks, including the superior temporal and precentral gyri, insula, pre- and post-central gyri, and the inferior parietal lobule, showed significant heritability (33 percent). Novelty processing networks had significant heritability in the superior and middle temporal gyri (33 percent) and the frontoparietal-temporal network (32 percent).

The brain region of interest approach had varying results. The ventral striatum showed 20 percent heritability for conscious facial emotion stimuli. The bilateral amygdala revealed a significant heritability contribution (right: 33 percent, left: 34 percent) elicited by nonconscious facial emotion stimuli. The selective attention and novelty processing task showed a significant contribution of heritability in the medial superior prefrontal cortex (29 percent). The working memory, sustained attention, and response inhibition tasks showed no significant contribution of heritability in the brain regions of interest.

One notable finding is that the results suggest genetics play a role in distinguishing between disgust and fear more so than positive emotions. The researchers state that this may be due to an evolutionary adaptation, as identifying threats is key to survival. In general, however, they speculate that environmental factors have a greater influence on the perception of emotional expressions since “the intentional (conscious) and accurate perception of others’ emotional expressions within a particular environmental context is a paramount skill for successful social interactions.” Because social expectations vary so widely across cultures, it follows that the environment and external influences play a greater role in shaping social and emotional interactions compared to genetics.

Future Directions

The study is one of the first to analyze the shared genetic and environmental correlations across heritable brain networks/regions across multiple tasks. The researchers used advanced technology and research methods to investigate the extent to which brain function elicited by executive function and emotion processing may be heritable.

The results are interesting, yet they do not provide definitive answers to the complex nature versus nurture debate. Twin studies can provide interesting new information and allow researchers to unravel genetic mysteries, but there are limitations to using twin models, including assumptions of equal environments and random mating . While uncommon, it is also possible for twins to have different biological fathers and share only 25 percent of their DNA. These limitations in twin research methods keep us from arriving at definitive conclusions regarding the influence of genetics on behavior.

Researchers continue to seek answers that may provide groundbreaking revelations. A recent twin study published in JAMA Psychiatry demonstrated the significant impact of the environment on mental health outcomes, studying twins who had adverse childhood experiences . Longitudinal studies tracking brain development over time could provide more insights into how genetic and environmental factors interact to influence cognitive and emotional processes. Additionally, advances in imaging technology and computational methods offer exciting opportunities to explore the neural mechanisms underlying genetic influences on brain function.

Understanding the genetic basis of cognitive and emotional processes could also have various practical implications, informing mental health treatment and intervention. Insights into the genetic underpinnings of emotional processing could inform therapeutic strategies for conditions such as anxiety and depression . By recognizing the role of both genetics and the environment in shaping brain function, clinicians can tailor interventions to meet the specific needs of each patient.

The study represents a significant step forward in our understanding of the genetic and environmental influences on brain function. By uncovering the complex relationship between genes, brain networks, and cognitive processes, the research opens new avenues for personalized approaches to mental health care and intervention. As we continue to unravel the mysteries of the human brain, studies like this provide valuable insights into the influence of biology on human behavior.

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Twin Study Sheds Light on Nature vs Nurture Debate

A twin study has revealed the complex interplay between genetics and environment in how our brains navigate..

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The way our brain processes different emotional and cognitive tasks may be underpinned by common factors, find scientists from UNSW and  Neuroscience Research Australia (NeuRA) .

In this latest study, recently published in the journal  Human Brain Mapping , Dr Haeme Park and Associate Professor Justine Gatt, who hold joint positions at  UNSW Psychology  and NeuRA, looked at how both emotion and cognition are influenced by the environment and genetics, using functional MRI (fMRI) scans on twins.

“There has been quite a lot of research looking at genetic versus environmental influences on brain structure,” says Dr Park, lead author of the study. “But it’s a lot harder to understand the function of our brains.”

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The results revealed that the picture is extremely complex. Some emotional and cognitive tasks were partly associated with genetics, and others exclusively with environment.

But they also found that some of the same genetic and environmental factors can play a role in the brain reacting to two different tasks. For example, the analysis showed that some of the same genetic factors are influencing how we process fear and happiness and also how we sustain our attention.

“This study is interesting because we have further insight into how much of our life experiences modulate certain brain processes, which to a certain degree we have more control over, versus your biology, which you can’t change,” says A/Prof. Gatt, Director of the  Centre for Wellbeing, Resilience and Recovery . “Knowing what areas of our brain function are linked strongly to our environment can help us develop personalised intervention approaches to promote higher mental wellbeing.” 

The importance of twin studies

The so called ‘nature vs nurture’ debate isn’t new.

In fact, twin studies have become a unique research tool used by geneticists and psychologists to evaluate the influence of genetics and the effect of a person's shared environment (family) and unique environment (the individual events that shape a life) on a particular trait.

“With twin studies, it’s important to recruit both identical and non-identical twins,” says A/Prof. Gatt. “Identical twins share 100 per cent of their genetics and if they're grown up together, they share the same environment. Whereas with the non-identical twins, they only have 50 per cent shared genetics, but they also have that common environment.”

“In this study, we wanted to bridge lots of gaps in the literature and provide a more robust and thorough picture of how our genetics and environmental factors impact the expression of brain activity during emotional and cognitive tasks, by analysing twins,” says Dr Park.

Cognitive and emotional tasks

The most recent paper is  one   of   many  from the  TWIN-E study , which recruited 1600 identical and non-identical twins from across the country in 2009 and is led by A/Prof. Gatt.

A subset of the original cohort participated in this particular study, with a total of 270 adult twins taking part.

“We get participants set up on the fMRI scanner bed which is fitted with goggles that enable them to see the tasks in front of them. The functional tasks involve them viewing different images, different stimuli, through the goggles,” says A/Prof. Gatt.

While the participants were completing the tasks, the fMRI machine was scanning their brain to measure its activity.

The twins completed a total of five tasks. Two were linked to emotional responses, such as reactions to various expressions of different faces, and the other three were associated with cognition, such as the ability to sustain attention and short-term memory.

Processing the fMRI scans show you which part of the brain light up for different processes, and how strongly the brain is activated can be measured on a scale.

“So individuals who show a lot of activation in that region have a higher number, whereas those with lower activation have a smaller number. We then use these figures to carry out what we call ‘twin modeling’ processes,” says Dr Park. “This is where we use statistics to break down how much of a role genetics and environment contributes to that number.”

Twin modelling results

Twin modelling methods revealed two key findings in their analysis of the results.

Firstly, the researchers looked at the genetic versus environmental influence on each individual task. “We know that we use different brain networks for different processes – for example, processing either a crying face or a happy face is going to use different regions in the brain compared to trying to remember someone’s phone number,” says A/Prof. Gatt. “But we found that for some of these networks, genetics plays a small to moderate, but significant role. And for other processes, it’s only the environment that determines brain function.”

The second part of the analysis found that there were similarities in the genetic and environmental factors that underpinned different tasks.

"For example, we discovered that how the brain processes fear and happiness (which was measured in the emotional tasks) and our ability to sustain attention (which was measured in the cognitive tasks), have some shared genetic factors,” says Dr Park. “This suggests that some common genetic features may underpin these very different processes.”

In contrast, the team also found that our ability to sustain our attention and our working memory have some of the same environmental contributions, suggesting that life experiences – which come from your environment – play a significant role in how brain activity is expressed for these two processes.

Mental wellbeing and resilience

While it’s clear that both our genetics and life experiences are important in determining how our brain functions, the puzzle is far from solved.

“There’s still so much more to find out!” says Dr Park. The current participants have already been followed up more recently and have performed the same tasks again after 10 years. A/Prof. Gatt, Dr Park and their team will be reassessing the results to see how the influences of genetics and environment on these brain processes change over time.

“All these results paint a complex picture of the relationship between genes and environment that give rise to the brain activity underlying our cognition and emotion,” says A/Prof. Gatt. But knowing more precise details may help to develop personalised intervention approaches in order to promote, for instance, higher mental wellbeing, or reduced psychological distress.

In fact, the ongoing TWIN-E study focuses more broadly on mental wellbeing and resilience. “So, what we're using this data for, beyond looking at genes and environment, is actually predicting mental wellbeing and resilience trajectories over time, and seeing how differences in markers like brain function and structure might profile people who are a bit more resilient or at more risk to a mental health problem,” says A/Prof. Gatt.

Understanding how much of our life experiences influences certain processes versus the influence of genetics is important when knowing what factors we can change and control, which is particularly significant for people with mood and anxiety disorders, explains A/Prof. Gatt. “If someone has a tendency to attend to negative stimuli more than positive, and we know that there's an element of environment contributing to that, with intervention or training, it’s potentially something we can target and improve for the better.”

Reference:  Park HRP, Chilver MR, Quidé Y, et al. Heritability of cognitive and emotion processing during functional MRI in a twin sample. Human Brain Mapping . 2024;45(1):e26557. doi:  10.1002/hbm.26557

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Nature, nurture, and conservatism in the Australian Twin Study

Affiliation.

• 1 Department of Psychology, University of Texas, Austin 78712.
• PMID: 8352724
• DOI: 10.1007/BF01082468

Church attendance, educational level, and six conservatism scales were the subject of a multivariate behavior-genetic analysis by Truett et al. (Behav. Genet. 22, 43-62, 1992), based on responses from a large sample of adult Australian twins. These data are here analyzed in a different way to elicit general conservatism factors in the genetic, shared environmental, and unshared environmental covariation. The general genetic factor appears mainly to reflect intellectual sophistication; the general environmental factors, religious affiliation. These factors are similar, although not identical, for men and women.

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What Twins Can Tell Us About Who We Are

Shankar Vedantam

Laura Kwerel

What Twins Tell Us

In March of 2017, the two sets of Bogotá twins, Jorge, William, Carlos and Wilber (left to right), gathered to celebrate Carlos's graduation. Diana Carolina/St. Martin's Press hide caption

In March of 2017, the two sets of Bogotá twins, Jorge, William, Carlos and Wilber (left to right), gathered to celebrate Carlos's graduation.

In December 1988, two sets of identical twins in Bogotá became test subjects in a study for which they had never volunteered. It was an experiment that could never be performed in a lab, and had never before been documented. And it became a testament to the eternal tug between nature and nurture in shaping who we are.

The brothers as children. From left to right: Carlos and Jorge at age 5, and Wilber and William at age 6. Courtesy of Jorge and Carlos; William and Wilber/St. Martin's Press hide caption

The brothers as children. From left to right: Carlos and Jorge at age 5, and Wilber and William at age 6.

This week, psychologist Nancy Segal tells the story of the Bogotá twins, which was a tragedy, a soap opera, and a science experiment, all rolled into one. And she explains why twin studies aren't just for twins. They can serve as a paradigm to understand age-old questions that affect us all: Is our fate written in our genes? And how powerful is upbringing in shaping who we become?

Insights — and provocations — from twin studies, this week on Hidden Brain .

Additional Resources:

" Accidental Brothers: The Story of Twins Exchanged at Birth and the Power of Nature and Nurture ," by Nancy Segal and Yesika Montoya, 2018

"Born Together—Reared Apart: The Landmark Minnesota Twin Study," by Nancy Segal, 2012

" Pairs of Genetically Unrelated Look-Alikes: Further Tests of Personality Similarity and Social Affiliation," by Nancy Segal and colleagues, 2018

"Socioeconomic Status Modifies Heritability of IQ in Young Children," by Eric Turkheimer and colleagues, 2003

" Personality Similarity in Twins Reared Apart and Together," by Thomas Bouchard and colleagues, 1988

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• Review Article
• Published: 15 May 2023

Maximizing the value of twin studies in health and behaviour

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• Jana S. Hirzinger   ORCID: orcid.org/0000-0002-0149-2575 1 , 3   na1 ,
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In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene–environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

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Behavioural scientists are often interested in understanding how human traits, such as intelligence, educational attainment, depression and anxiety, are influenced by inherited and environmental factors. Since long before the advent of human genomics, researchers have relied on twin studies to answer these questions. The classical twin design compares trait resemblances in monozygotic (MZ) twins to those in dizygotic (DZ) twins. Because MZ twins are genetically identical and DZ twins share 50% of their genes on average, any additional similarity between identical twins should be related to genes, provided that the twins share the same environments 1 , 2 .

Researchers can apply the classical twin design to estimate the influence of genes for any trait they can measure. They can also extend the classical twin design to model causal relations between exposures and traits, to describe differences within discordant twin pairs, to estimate genetic and cultural components of inheritance, and to account for measured genetic variables and environmental exposures.

In this Review, we examine the use of twin studies as researchers become interested in new phenotypes, including those based on omics technologies. We reflect on methods for achieving progress in fields that have traditionally valued twin studies of individual differences, and we explore which fields offer new opportunities for twin research and how insights from other disciplines can inform twin studies. We begin by introducing the classical twin design. We then summarize concordance and discordance in twin pairs for major psychiatric and somatic disorders, arguing that no other design is as informative for studying penetrance and genetic prediction of disease risk. Next, we turn to assumptions and advances in research methods and the study of twinning as a phenotype, which is important for the mothers of twins and for female fertility and may inform developmental biology. We conclude by discussing existing knowledge gaps in twin studies.

Classic twin design and twin concordance and discordance

Classic twin design.

The classical twin design compares the resemblance of MZ and DZ twins for univariate or multivariate traits to estimate heritability and genetic correlations among phenotypes (a glossary of key terms is provided in Box 1 ). It decomposes phenotypic variance and covariance among traits into genetic and non-genetic components, on the basis of the biometrical model P  =  G  +  E , where P , G and E represent univariate or multivariate phenotypes, individual genetic values and environmental deviations, respectively 3 . P is measured, whereas G and E need not be observed, but their influence is estimated by comparing MZ and DZ twins, as illustrated in Fig. 1 . The influence of the genotype can be summarized as the heritability of a trait: the proportion of variance due to genetic factors.

The circles denote latent (that is, unmeasured) variables, and the squares denote measured variables. To ease the presentation, variances for latent variables that are constrained at 1 are not drawn. The model as depicted is for DZ twins.

Twin resemblance can be quantified in concordance or correlations. Concordance and discordance refer to the degree of similarity or difference between twins in terms of their phenotypes. Concordance is typically applied to dichotomous phenotypes, such as the existence of a disorder, while correlations are used to summarize twin resemblance for continuous traits. Similar concordance rates in MZ and DZ twin pairs suggest a larger role of environmental factors, while a higher concordance in MZ than DZ twin pairs suggests stronger genetic effects. For continuous traits, we double the difference between the MZ and DZ correlations to obtain a first estimate of heritability—that is, the proportion of variance explained by genetic factors. However, the relation between twin concordance and trait heritability is more complex because it depends on the prevalence of a disorder in the population.

Box 1 Glossary

Amnion . A membrane that contains the amniotic fluid and provides a protective environment for an embryo by closely covering it. Forms the amniotic sac together with the chorion.

Assortative mating . Individuals with similar phenotypic characteristics, such as height or intelligence, are more likely to mate with each other than would be expected by chance. This impacts heritability estimation in the classical twin design.

Chimerism . A phenomenon in which an individual carries some genetic material originating from another individual (for example, from their co‑twin or mother).

Chorion . The outermost fetal membrane around the embryo. Forms the amniotic sac together with the amnion.

Classical twin design . The comparison of the resemblance of MZ and DZ twins for a phenotype or phenotypes to estimate the contribution of genetic and non-genetic (environmental) sources of (co)variance.

Concordance, pairwise . Defined as C /( C  +  D ), where C is the number of concordant pairs (that is, where both twins of a pair are affected) and D is the number of discordant pairs (that is, where only one twin of a pair is affected).

Concordance, proband-wise . Defined as 2 C /(2 C  +  D ), in which C is the number of concordant pairs and D is the number of discordant pairs. Proband-wise concordance is preferred over pairwise concordance as it reduces the impact of non-genetic factors that can affect both twins in a pair.

Cultural transmission . A type of phenotypic transmission from parent to offspring that is independent of genetic transmission and gives rise to r GE .

De novo mutation . A mutation that is not inherited from either parent and that can occur during the formation of the egg or sperm (germ cells) or during early embryonic development.

Direction of causation (DOC) modelling . Modelling based on cross-twin cross-trait correlations to deduce causal relationships between two variables with different inheritance patterns.

Discordance . When two twins within a pair do not exhibit the same trait—for example, when one twin develops a disease, whereas the other does not.

Environmental correlation . The extent to which the same environmental factors contribute to variation in two phenotypes. Can be estimated via multivariate twin models.

Epigenome . All chemical modifications that occur to the DNA molecule and the proteins associated with it that can affect gene expression without changing the underlying DNA sequence.

Gene–environment correlation ( r GE ) . Correlation between genotype and environment, which occurs when genotypes are not randomly distributed over environments. Multiple mechanisms (passive, reactive and active) can lead to r GE .

Gene–environment interaction ( G × E ) . Present when genetic effects on a phenotype depend on the individual’s environment or when environmental effects on a phenotype depend on an individual’s genotype.

Genetic correlation . The extent to which the same genetic factors contribute to variation in two phenotypes. Can be estimated via multivariate twin models or be based on results from GWASs.

Genetic relationship matrix (GRM) . A genetic covariance matrix between persons calculated from genotype data of the individuals.

Genome-based restricted maximum likelihood . A statistical method for variance component estimation in genetics that quantifies the total narrow-sense contribution to a trait’s heritability of a particular subset of genetic variants (for example, SNPs).

Genome-wide association study (GWAS) . A regression-based approach to identify genomic variants (for example, SNPs) that are statistically associated with a disease or trait.

Heritability . The proportion of phenotypic variance that can be explained by differences in genetic make-up. In twin studies, we can differentiate between variance due to all genetic effects (broad-sense heritability) and due to additive genetic effects (narrow-sense heritability). Heritability derived from measured SNPs is referred to as SNP heritability.

Heteroscedasticity . A statistical phenomenon in a regression model where the variance of the residual (error) term is not the same for different values of the independent variable.

Locus . The chromosomal location of a genetic variant or gene.

Liability . A continuous variable that underlies human diseases or dichotomous traits. Variation of liability can be both genetic and environmental. Such diseases or traits are sometimes referred to as threshold characters, because a threshold divides the population into affected and unaffected persons.

Maximum likelihood estimation . A statistical approach to estimate the parameters (for example, heritability) of complex models on the basis of observed data by finding the values for parameters that maximize the likelihood function—that is, a function that measures how likely the observed data are to have been generated by the model.

Mendelian randomization (MR) . An analytical approach that uses random segregation of genetic variants strongly associated with an exposure as an instrument to investigate the putative causal effect of the exposure on an outcome, allowing confounders and reverse causation to be reduced.

Metabolome . The entire range of small molecules (that is, lipids and sugars) in a cell, tissue or organism that are involved in cellular metabolism.

MR–DOC . A combination of DOC modelling and MR to test causal relationships while accounting for confounding factors.

Multilevel twin model . A reparameterized form of the classical twin model where variance is decomposed into levels: (1) within-pair variance, (2) between-family variance and (3) the variance of a higher-level clustering variable.

Multivariate twin models . Models in which the outcome consists of more than one phenotype per person to estimate the relative contributions of genetic and non-genetic (environmental) sources of covariance.

Network parameters . Descriptions of the connectivity of brain structures or psychological concepts as determined by (statistical) models.

New phenotype . A phenotype newly present in a population due to technological and scientific advancements or global phenomena (such as COVID-19).

Omics . Biological sciences that aim to study an organism’s entire collection of molecules.

Penetrance . The probability that a disorder will occur if an individual is a carrier of a particular genotype.

Phenotype . Any measured or observed trait.

Pleiotropy . When a single gene (or a set of genes) affects multiple phenotypes. It is one the of mechanisms leading to genetic correlations among traits.

Polygenic score (PGS) . A value that characterizes an individual’s genetic value for a disease or trait, which reflects prediction relative to the general population. It estimates the combined effects of multiple DNA variants in determining the probability of an individual having specific phenotypes or traits.

Proteome . The entire range of proteins, the vital molecules directly involved in cellular function, in a cell, tissue or organism.

S ingle nucleotide polymorphisms (SNPs) . Substitutions of a single nucleotide at known genomic locations.

S ingleton, twin, triplet, quadruplet, quintuplet (and so on) . An individual from a single pregnancy that resulted in one, two, three, four or five (or more) offspring.

Statistical power . The probability of detecting an effect when it is there—in other words, the likelihood of correctly rejecting the null hypothesis.

Structural equation model . A statistical method to estimate the relationship between unobserved (latent) and observed variables.

Tetrachoric correlation . A special case of the polychoric correlation that represents a correlation between two, unobserved, continuous variables (‘liabilities’) from two observed variables that are assessed on an ordinal scale. Tetrachoric refers to the case when both observed variables are dichotomous.

Transcriptome . All RNA transcripts in a cell from protein-coding to non-coding RNA. It refers to the set of RNA molecules such as mRNA, tRNA, rRNA and others.

Transmitted/non-transmitted genotype design . A design in which transmission from parents to offspring is analysed via two types of genetic information: (1) genetic variants transmitted to offspring (this constitutes the offspring’s genotype) and (2) genetic variants that are not passed down from parents to offspring (the non-transmitted genotype). The non-transmitted genotype can influence an offspring’s phenotype; this is referred to as indirect genetic effects, genetic nurture or cultural inheritance.

Twinning . The process in which two or more offspring result from a single pregnancy.

X-inactivation . The process by which one of the copies of the X chromosome is inactivated (silenced).

Zygosity . Twins are either MZ (sometimes called ‘identical’) or DZ (‘fraternal’). MZ twins develop from a single fertilized egg and, except for somatic mutations, share the entire DNA sequence. DZ twins develop from two separate eggs that are fertilized by two separate sperm. They share approximately 50% of their genetic material, like non-twin siblings. Triplets can be MZ, DZ or trizygotic.

Twin concordance and discordance

Figure 2a presents an overview of estimates of twin proband-wise concordance rates (or case-wise with full ascertainment) 4 in complex diseases, either directly obtained from publications by computing proband-wise concordance from the published data or obtained by contacting the authors. To estimate heritability, researchers cannot only look at the differences in concordance rates between MZ and DZ twins; their resemblance must be obtained on the underlying liability scale by tetrachoric correlations 5 , 6 . Figure 2b (based on Smith 7 ) shows the relation between concordance, prevalence of a disorder in the population and heritability on the liability scale 7 . The figure specifies this relation for prevalences ranging from 0.01% for rare traits to 80% for common ones. It is especially striking that for traits with a low prevalence and a high heritability, most MZ twins will be discordant.

a , Proband-wise MZ and DZ twin concordance rates. References for the studies and the non-rounded concordance rates are provided in Supplementary Table 2 . b , Expected concordance rates in MZ twins based on Smith 7 detailing the relation between twin concordance, disorder prevalence and disorder heritability on the liability scale. The expected MZ twin concordance, prevalence and heritability for the highlighted disorders are provided in Supplementary Table 3 .

These results provide valuable information for research and society. While genetic effects are evident from the higher concordance in MZ twins than in DZ pairs, concordance in MZ twins does not approach 100%, even for highly heritable traits such as schizophrenia 8 . Explaining why one person becomes affected and their twin with an identical genotype is protected involves identifying environmental factors, epigenetic mechanisms and DNA mutations that occurred in one twin but not in the co-twin.

The extent to which the concordance rates in MZ twins are lower than 100% conveys how predictive the genotype will be for individual outcomes. An individual’s genetic liability to a disorder can be seen as providing a baseline, with other factors influencing whether that liability ends up passing the threshold for disease penetrance. Thus, twin concordance rates powerfully illustrate that even a strong genetic predisposition does not have to result in the development of a disease.

The genome is not deterministic. Processes such as epigenetics (including X-inactivation, in utero exposure and post-natal environment) may lead to changes in DNA expression within MZ pairs. Epigenetic and DNA expression differences were found in MZ twins discordant for inflammation-related diseases (such as lupus 9 ) and for psychiatric and neurodegenerative disorders (including schizophrenia 10 and Alzheimer’s disease 11 ). Differences in discordant MZ twin pairs have also been observed for proteomic 12 , 13 and metabolomic profiles 14 , 15 and in multi-omics studies 16 , 17 .

For biomarkers, including omics profiles, it remains challenging to establish whether twin discordance is a cause or consequence of the disease. Here MZ twin pairs are valuable to examine causality 18 , especially when combined with the longitudinal data in twin registries. In one example addressing the causes versus consequences of smoking behaviour 19 , longitudinal phenotype data were combined with gene expression profiles, and 132 differentially expressed genes were identified in current, former and never smokers. Nearly all genes (125) had reversible effects on gene expression levels. In 56 MZ pairs discordant for current smoking, only six genes were differentially expressed, but the effects for 75% of the genes in the discordant MZ twin pairs were in the same direction as in the total population.

For diseases that are more common at an older age, one needs to consider that the disease can still manifest at later follow-up. Careful consideration of the age composition of the sample is therefore required, as illustrated by studies investigating twin concordance rates for Parkinson’s disease 20 , 21 . When twins were studied at an average age of 74 years, researchers found similar concordance rates for late-onset Parkinson’s disease (>50 years of age) in MZ and DZ pairs. When they later searched the US National Death Index for all twin pairs with Parkinson’s disease in at least one twin, they found higher concordance among MZ than DZ twins at follow-up, and heritability was now estimated at 20% for late-onset Parkinson’s disease.

In an era of genetic testing services and polygenic scores (PGSs), the upper limit of genomic predictions must be defined and acknowledged. Researchers and clinicians may currently see risk prediction tools as having an upper limit set by trait heritability, but in fact predictions can go only as far as the MZ twin concordance rates.

Assumptions of the classical twin design

In its most basic application, the classical twin design assumes random mating in the parents of twins, and that environmental factors that influence a trait are similar for MZ and DZ twins so that any differences in the similarity of the twins’ traits can be attributed to genetic factors. This so-called equal environment assumption has been tested numerous times and holds for most phenotypes 22 . Researchers can test assumptions regarding equality of means and variances in MZ and DZ twins and the absence of interactions or correlations between genes and environment 23 with appropriate data and methods, and deviations from these assumptions may provide new insights, as we discuss below.

Another assumption entails that twins are representative of the population to which heritability and other estimates apply. Twins and singletons are similar in most respects 24 , 25 , even though twins are born earlier on average and grow up with a sibling of the same age. Twins and non-twins have similar biomarker profiles and diseases 26 , cognitive functions 27 , health behaviours 28 , personality 29 and psychopathology 30 . Despite early hypotheses that twins from DZ pairs are more often chimeric than singletons, a large study in twin-family pedigrees found this not to be the case 31 . Although twins are born earlier on average and have lower birth weight 32 and body mass index (BMI) 33 , 34 , genetic correlations of twin and singleton birth weights are not significantly different from each other 35 , meaning that the same genes influence birth weight in twins and singletons. Consequently, data from twins can be combined with other population-based results or included in population-based genome-wide association studies (GWASs) to increase the sample size. As David Lykken stated in his 1982 presidential address to the Society for Psychophysiological Research, “Twins are probably more representative of the general population than any other group … This representativeness is even more true of the families of twins” 36 .

DNA sequence differences within MZ twin pairs

Post-zygotic (that is, after fertilization) mutations occurring in one twin of an identical pair can lead to genetic and phenotypic differences between them. However, if such mutations occur early in embryonic development—that is, before a split occurs—both twins will have the mutation. In a DNA sequencing project of 381 Icelandic twin pairs and two triplet sets, 39 pairs differed by over 100 mutations, while 38 pairs did not differ at all 37 . The median number of post-zygotic mutations differing within twin pairs was 14, with a higher estimate of 48 for high-coverage pairs. In 78 parent–offspring trios from the same population, 4,933 de novo mutations were reported—that is, an average rate of 1.2 × 10 −8 per nucleotide per generation 38 . A review also put 1.18 × 10 −8 per position as the best estimate of the average human germline mutation rate, corresponding to 74 novel single nucleotide polymorphisms (SNPs) and approximately three novel structural variants 39 per genome per generation. De novo mutations are enriched in coding and regulatory regions of the genome 40 . Thus, it is not surprising that de novo mutations account for a substantial component of some rare genetic syndromes and that mutations in one twin but not the other can lead to discordance between them. For highly polygenic traits, individual SNP effects are very small. If only a few SNPs are discordant per pair, the effects of these are negligible. While it is likely that de novo mutations do not impact twin-based heritability estimates of polygenic traits, we should be cautious when investigating rare genetic disorders that involve only a small number of genes or disorders that involve de novo mutations particularly.

DNA sequence differences between MZ twins can also inform on the timing of developmental mutations. Germ cells are specified around the third week after fertilization, a process referred to as primordial germ cell specification (PGCS). If post-zygotic de novo mutations are present in both the soma and germline of a twin, we can classify them as pre-PGCS. Both twins can share pre-PGCS mutations, or these can be present in just one twin, indicating whether mutations occurred before or after the twinning split. When post-zygotic mutations are present in the soma or the germline of both a proband twin and their offspring, we can classify them as post-PGCS mutations.

Methodological developments in twin studies

The classical twin design can be extended to explore gene–environment interactions and correlations. Gene–environment interaction ( G × E ) can be conceptualized as moderation, where genes moderate the environmental effects or where the environment moderates or controls genetic effects. Gene–environment correlation ( r GE ) describes a correlation between G and E in the model P  =  G  +  E and is sometimes viewed as genetic control over exposure to different environments. There is ample theoretical work on gene–environment interactions but relatively little empirical work. In contrast, several developments have stimulated the study of r GE . These involve adding measured G or E to the design, extensions to longitudinal data and the addition of family members, such as the parents or the offspring of twins.

Gene–environment correlation

Detecting r GE in the twin design can be achieved by including measured genetic or environmental variables. For instance, if the phenotype is depression, marital status may be a relevant measured environment, and a PGS for depression a relevant genetic variable. If genetic factors influence depression and marital status, this gives rise to r GE . Obviously, this approach requires a prior expectation about which environmental variable to assess. By including a measured genetic variable—for example, a PGS for depression—we can estimate the correlation between the shared family environment and all genetic factors that influence depression 41 . This approach is exploratory, as it can detect r GE but does not require prior hypotheses concerning the environmental influences involved in the r GE .

An alternative approach to studying r GE in the classical twin design is by analysing longitudinal data 42 . Given repeated measures, r GE can be modelled by estimating the regression of the latent environmental variable at time t ( E t ) on the phenotype at time t  − 1 ( P t −1 ). This regression implies r GE by the following chain of regression relationships: the genotype at t  − 1 influences the phenotype at t  − 1, which influences E t . So, P t −1 mediates the relationship between G t −1 and E t , thus giving rise to r GE (Fig. 3 ). This influence of the phenotype on the environment may arise if children actively seek out or create environmental circumstances, which match their genotype. Given the own role of the child, this is called active r GE or ‘niche-picking’. This may apply to intelligence and musicality, for example. Alternatively, the behaviour of the child may elicit responses from persons in the environment, called evocative r GE . Examples are highly outgoing children, who encourage social interaction, or aggressive children, who may discourage social interaction and elicit corrective parenting.

The model is shown for a pair of twins, whose longitudinal phenotypes are influenced by their genotype and the shared and unshared environment. The latent genotype and the shared and unshared environment can be influenced by earlier times (for example, stable genetic influences) and by innovations (for example, new expressed genes). The red arrows show phenotype-to-environment transmission, which leads to GE correlation.

Simultaneous environmental and genetic transmission across generations also leads to correlations between genotype and environment in offspring, as their genotype depends on the parental genotypes (genetic transmission), and their environment (partly) depends on the parental genotypes, as mediated by the parental phenotype (cultural transmission). This form of r GE is called passive r GE , as the twin offspring are the (passive) recipients of parental genes and an environment (partly) created by parents. The environment that parents create depends on the genotype they transmit to their offspring and on the part that is not transmitted. The parent–twin design can incorporate the effects of the non-transmitted genotypes on offspring outcomes 43 (depicted in Fig. 1 ).

In the children-of-twins design, which involves MZ and DZ twin pairs as parents, researchers compare the resemblance between parents and their own children to the resemblance between offspring and their parent’s co-twin 44 , 45 , 46 . In both the children-of-twins design and the parents-of-twins design, researchers estimate the effect that remains after accounting for direct genetic transmission, which induces a correlation between genotype and the shared environment.

A new way of modelling parental transmitted and non-transmitted genotypes is structural equation modelling combined with PGSs (SEM–PGS), which disentangles parental genetic and environmental effects on offspring traits while controlling for assortative mating, thereby providing unbiased estimates of genetic transmission 47 . Researchers can use SEM–PGS with trio data but also with twin pairs 48 . This way, they can study parental effects by combining both GWAS and twin-based designs.

The outcomes of intergenerational twin studies can help to inform intervention approaches 49 and avoid parent-blaming 50 . An example can be seen in cases in which a child is struggling in school, and the teacher blames its parents for not providing a suitable home environment or not being involved in their child’s education. In a parent–twin study of offspring years of education, there was a direct effect of parental genes that were inherited by the twin offspring 51 , making it difficult to hold the parents responsible for passing on their genes. The indirect (genetic nurturing) effect of parental genes that were not transmitted to offspring weakened after controlling for parental IQ or socio-economic status, implying that the genetic nurturing effect on education reflects family socio-economic status rather than any deliberate parenting behaviour 51 . Genetic nurture effects are often interpreted as parenting effects but may also reflect the broader family environment.

Gene–environment interaction

Researchers can investigate G × E by comparing heritability estimates across different environments. One recent example is before and during exposure to the COVID-19 pandemic. A comparison of heritability before and during the first lockdown in the Netherlands (gene-by-crisis interaction) showed a slight increase in heritability estimates for optimism and meaning of life during the first months of the pandemic and lower-than-unity genetic correlations across time (0.75 and 0.63), implying G × E , with both quantitative and qualitative differences in genetic influences 52 .

Purcell suggested a broad method to measure changes in heritability on the basis of traits that could potentially have a heritable factor 53 . Some caution is needed if such traits, or moderators, are themselves correlated between twins, but several solutions exist 54 . Molenaar et al. developed an alternative approach that does not require a measured moderator to estimate G × E 55 . Their method exploits the fact that G × E will introduce heteroscedasticity 56 , 57 . By modelling environmental variance conditional on the genetic variance, they test for departures from bivariate normality in twin data. This method can explicitly estimate G × E without a priori hypotheses about specific interacting genotypes or environments.

Interaction among family members

Individuals can contribute to the environment of their family members, including twins and their co-twins 44 , 58 , and these effects can be either competitive or cooperative 59 . Under competition, the higher the expression of a trait in one sibling, the less it will be exhibited in the other sibling. Under cooperation, a trait expressed more strongly in one sibling will also be exhibited more in the other sibling. The presence of sibling interaction may be optimally detected in twin designs. Here, violation of the equal variance assumption in the two zygosity groups implies either cooperation or competition: inflated phenotypic variances and increased twin correlations imply cooperation, while deflated variances and decreased DZ correlations relative to MZ correlations suggest competition. Both inflation and deflation act more on the variance of MZ twins than that of DZ twins, thereby making the twin design a powerful tool to detect and distinguish competitive and collaborative interactions.

Multilevel twin models

When working with data from twins, natural clusters occur that are created by the fact that we include individuals from the same family unit. These family units are further nested in higher-level clusters such as geographic region, neighbourhood or school. If both twins in a pair share higher-level clustering variables, their effects will be reflected in the estimate for the common environment in the classic twin design. In multilevel twin models, variance is decomposed into within-pair variance on the first level (that is, representing differences between twins within a pair), between-family variance on the second level (that is, differences between pairs or families) and the variance of a higher-level clustering variable on the third level (for example, differences between geographic regions) 60 . For instance, multilevel twin modelling can clarify the relationship between geographic regional clustering and ancestry, as seen in regional clustering for height in seven-year-old Dutch twins, where, after adjusting for ancestry, the variation in height was no longer explained by region 61 . Hence, regional clustering effects may mask genetic ancestry effects, which can be disentangled in multilevel twin models.

Causal modelling in twin designs

Randomized controlled trials are considered the gold standard for establishing causality, but these are often not feasible or ethical in health and behavioural research. In such cases, researchers can turn to genetic designs such as Mendelian randomization (MR) and the direction of causation (DOC) twin models to study causality. MR uses an observed genetic variable (for example, SNP or PGS) as an instrumental variable that is assumed to correlate with the exposure variable, but not with confounding variables or the outcome 62 . However, MR analysis with SNPs has low statistical power, and PGSs will often be unfeasible due to genetic pleiotropy. The bivariate DOC twin model was proposed for twin data to test causal relationships between two variables. Depending on whether trait A causes trait B or vice versa (that is, the direction of causation), the model predicts different cross-twin cross-trait correlations. The DOC model works only in situations in which two variables have different modes of inheritance—that is, one trait is influenced by the shared environment, while the other is mainly influenced by genes 63 . Combining MR with the DOC twin approach in an MR–DOC model yields a causal test that does not require the mode of inheritance to differ for the two variables and relaxes several critical MR assumptions concerning pleiotropy. If PGSs of twins are used as instrumental variables in a bivariate classical twin design, the PGSs can correlate with both predictor and outcome 64 , 65 . Combining DNA-based methods with twin-based designs can thus overcome the limitations of individual methods. While no single approach is free of bias, triangulation can help to address the limitations of methods and facilitate replicable and reproducible results.

The added value of twins when decomposing heritability

The classical twin design can be combined with genome-based restricted maximum likelihood methods to simultaneously estimate SNP heritability and pedigree-based heritability 66 , 67 . SNP heritability refers to the contribution of measured SNPs to trait heritability. Pedigree-based heritability captures heritability on the basis of the known relationships among not-too-distantly related family members. Here, closely related family members are typically defined as cousins two or three times removed or closer. This approach is referred to as threshold genetic relationship matrix (GRM) and relies on two GRMs: one GRM including all genetic relationships, and one where the genetic relationship among distantly related pairs of individuals is set to zero to capture pedigree-associated variation. The total heritability is obtained by summing the estimates as obtained from both GRMs (Fig. 4 ). By including additional GRMs that comprise specific SNPs, researchers can extend the threshold GRM approach to capture the contributions of functional or biologically relevant elements 68 . For example, a four-GRM approach was used to obtain heritability estimates for known metabolite loci and distinguish between metabolite classes 69 . Besides the GRMs in the threshold approach, from which all metabolite loci were removed, this study included a third GRM to capture the metabolite loci of a specific metabolite class and a fourth GRM to include all metabolite loci for all other metabolite classes.

Two GRMs (orange boxes) are the basis to obtain two heritability estimates: SNP heritability and pedigree-based heritability. The two estimates sum to the total heritability. In the GRM giving rise to the pedigree-based heritability, only relations among third or fourth cousins (who share 2.5% of their genetic material—that is, 0.025) or among second cousins (0.05) or more closely related family members are retained.

Advances in health and behavioural phenotypes

DZ and MZ twinning have different biological mechanisms. DZ twins result from two separate fertilizations and have different placentas and fetal membranes. MZ twins, in contrast, come from a single sperm and egg and separate within two weeks of fertilization. MZ twins may have shared or separate amnions and chorions 70 . DZ twinning has a genetic component 71 , 72 , 73 , but estimates of heritability for being a DZ twin mother are remarkably scarce. Duffy and Martin estimated the heritability of twinning in historical pedigrees to lie between 8% and 20% 74 . They could not distinguish between MZ and DZ twins, potentially underestimating the true heritability, but their estimate was consistent across time (8–19 generations) and ancestries (West Africa, Europe and Canada).

The first GWAS for being a mother of natural DZ twins identified two genes, which replicated in the deCODE Icelandic database 75 —namely, follicle-stimulating hormone beta subunit ( FSHB ) and SMAD family member 3 ( SMAD3 )—with risk alleles increasing the frequency of twin births in Iceland by 18% and 9%, respectively. Whereas FSH genes are strong candidates for DZ twinning, the finding for SMAD3 , which is expressed in the human ovaries, was new. SNPs associated with DZ twinning have an impact on several reproductive traits, including increased fertility (having more children), decreased risk of polycystic ovary syndrome, earlier onset of menstruation, earlier natural menopause and giving birth to the first child at a younger age.

By contrast, the genetic aetiology for MZ twinning remains unclear, but van Dongen et al. discovered a strong epigenetic signature for MZ twinning comprising 834 differentially methylated positions in adult somatic tissues 76 . The loci were enriched for putative metastable epi-alleles, which are epigenetic marks or modifications that occur during early development. This lifelong signature opens up new avenues to investigate the vanishing MZ twin syndrome (that is, the disappearance of an embryo during early pregnancy 77 ) and congenital disorders that have an overrepresentation of MZ twins 78 .

Phenotypes of increasing interest

Twin studies are of interest not only to psychology, biology and medicine but also to fields such as political science, sociology and economics 79 , 80 . In economics, twin studies have demonstrated genetic effects in classic economic paradigms such as the trust game, which manipulates participants’ willingness to invest money and to reciprocate others’ trust 81 . In political science, twin studies have shown that genetic contributions to political ideology are largely independent of the chosen measurement, time or population 82 . Inside and outside of these fields, topics previously studied become relevant again, and new ones arise. For example, during the global COVID-19 pandemic, a study in twins estimated a heritability of 31% for COVID-19 on the basis of self-reported symptoms 83 .

The outcomes of twin studies can benefit interventions. As genetic influences can change over a lifetime, the longitudinal nature of twin registers can inform policy decisions aimed at a specific age group 84 . For example, as obesity often starts in childhood 85 and twin studies show that the heritability of BMI increases throughout childhood 86 , this suggests that public health interventions might be most beneficial earlier in childhood rather than later in life. We may see this process as a recursive loop 87 where policies can inspire new questions for researchers, and results from research can lead to new policies. Society and its political, economic and social agents influence funding decisions, thus making twin studies relevant to a wide audience. This includes economists looking at the costs and benefits of policy decisions, sociologists studying the effects of policy decisions in society and political scientists studying the entire process of policy development.

Scientists develop interests in phenotypes that become accessible because of our technological advancement. Digital fingerprinting, the unique trace of a person’s activity on the internet 88 , 89 , is one example of such a phenotype. Language used on Facebook can predict depression symptoms 90 , and more complete digital fingerprints may serve as predictors for other health and behavioural outcomes. However, researchers do not know whether digital fingerprints in MZ twins are as similar as their physical fingerprints. One study found that genetic effects can fully explain the familial resemblance in the frequency of internet use and that genetic and environmental factors account for different aspects of use 91 . It seems plausible that variance in digital fingerprints will also have a heritable component. If MZ twins have highly similar digital fingerprints (more so than DZ twins or siblings), this also creates options for the application of the classical twin design to nationwide register data, as these registers contain information on the entire population of twins but often lack information on zygosity for same-sex pairs.

An approach to handle unknown zygosity was suggested by combining twin and sibling data in register data 92 . Male–female pairs are DZ, but same-sex pairs are MZ or DZ. As there are equal numbers of opposite- and same-sex DZ twin pairs, we know the proportion of DZ pairs. As same-sex DZ twins and same-sex siblings share the same genetic similarity, we can utilize the ratios of concordant and discordant pairs of the latter as weighting factors to determine the number of concordant and discordant pairs for same-sex DZ and MZ twin pairs. This approach has been used to estimate the heritability for attention deficit hyperactivity disorder in German health insurance data (0.77 for females and 0.88 for males) as inferred from ICD-10 diagnoses and drug prescriptions. These estimates closely resemble those obtained from classical twin studies. Thus, with only sex and diagnostic status of twin and sibling pairs, researchers could determine the contribution of genetics.

Neural and symptom networks

Twin studies can shed light on the genetic influence on the development, prognosis and comorbidity of neuropsychological and psychopathological disorders by analysing the network parameters of these disorders 93 , 94 . Researchers investigate the genetics of nodes and edges in graph-theory-based networks or in network analyses of psychopathological phenotypes. Network parameters, such as the efficiency of the structural brain network (derived from magnetic resonance imaging data), have strong genetic correlations with intelligence 95 , 96 and schizophrenia 97 . In networks of structural and functional brain connectivity, the heritability of network parameters ranges from 0.05 to 0.74 depending on participants’ age and the analytical method used (Supplementary Table 1 ). In networks for psychopathological disorders, central nodes (nodes with a relatively large number of connections to other nodes in the network) generally have higher heritability 98 .

Applications of network analyses face numerous challenges, including a lack of criteria for identifying the completeness of networks, and the heterogeneity and replicability of networks 99 , 100 . Network configuration can vary for different patients and across time, challenging the generalizability and reliability of network approaches 98 . Twin studies can help to resolve such issues by considering twin resemblance for network parameters, where high correlations in MZ twins imply high reliability 36 .

Omics traits

Recent technological advancements in measuring genetic variation, transcriptomes, epigenomes, proteomes and metabolomes have enabled researchers to study heritability in twin cohorts through classical twin designs or a combination of classical twin designs and genome-based restricted maximum likelihood. Heritability estimates for gene expression from twin studies that characterize RNA transcripts in a cell by microarrays or sequencing 101 differ by tissue. For example, adipose tissue has a higher average heritability (26%) than lymphoblastoid cell lines (21%) and skin tissue (16%) 102 . The average heritability of peripheral blood gene expression is between 10% and 20%, with mean heritability estimates significantly higher in RNA sequencing studies than those from microarray-expression data 68 , 103 .

Twin-based heritability estimates for DNA methylation vary by sex and age. DNA methylation, the most commonly measured epigenetic mechanism in epidemiological studies, involves the addition of a methyl group to the C5 position of cytosines in the DNA, which decreases the genome accessibility for transcription, thereby regulating gene expression 104 . On the basis of adult twin data, the heritability of blood DNA methylation across the genome was 19% on average, with common SNPs explaining on average 7% of the variance—that is, 37% of the total twin-based heritability 105 . Heritability for DNA methylation decreased with age, driven by an increase in environmental variance. A combination of multiple twin datasets across the lifespan (0–92 years) demonstrated an increase in familial correlations in MZ and DZ twins from birth to adolescence and a decrease after young adulthood 106 . The rate of change in familial correlations was similar in MZ and DZ pairs, which suggests that age affected the influence of environmental rather than genetic effects on DNA methylation across the lifespan.

Twin studies of proteomics and metabolomics generally report higher heritability estimates than those found for transcriptome and epigenome variables. One study in female twins estimated that genes accounted for 13.6% of the variance in plasma protein levels 107 . Post-translational modifications of proteins, particularly glycosylation (attachment of a glycan to a protein), are also heritable, with many N -glycans showing high heritability (>50%) in plasma 108 , 109 . Metabolites comprise a diverse set of small molecules (<1.5 kDa) involved in cellular metabolism, including amino acids, sugars and lipids 110 . Twin-based heritability estimates of blood metabolite levels cluster around 50% but can differ among metabolite classes 111 , 112 . Similar heritability estimates have been observed for urinary metabolites 113 , 114 .

To fully understand biological processes, we need to collect and analyse data from multiple omics domains together 115 . Simultaneous multi-omics modelling can complement single-omics analyses by accounting for the relationships among omics domains 116 . We anticipate that the fusion of twin designs with data from multiple omics domains will enhance the categorization or differentiation of diseases and the forecasting of biomarkers.

Concluding remarks

In today’s globalized and increasingly interconnected research world, collaboration and cooperation are key. Over 60 twin registers are in place worldwide, across multiple continents in 26 countries 117 , with many collaborating in the Collaborative Project of Development of Anthropometrical Measures in Twins (CODATwins) 118 , which amasses and shares data on height, BMI and size at birth from 54 twin projects from 24 countries. The results for height and BMI are remarkable, as the differences in heritability estimates were minor across twin cohorts from different cultural–geographic regions and in individuals from different birth cohorts or for whom height and BMI were obtained at different ages.

Existing registries cannot always answer novel research questions, and the replication of findings in understudied twin populations warrants the development of new twin registries 119 . Particularly, African, Arab, Hispanic and other non-European populations are still underrepresented in twin research. Also, some age groups are still underrepresented in twin studies. With the lengthening of average life expectancies and the rapid rise of aging populations, understanding the causes and effects of aging and age-related declines in health is vital 120 . While twin studies can make valuable contributions to these research topics, few current registries include twins of advanced age. Eventually, longitudinal registries may aid here by following their participants into advanced age, and establishing geriatric cohorts will accelerate research into aging. Another solution, which also tackles the attrition often observed in longitudinal studies, involves linking existing twin registers to electronic health records and other national registers.

Periconceptional and prenatal variables remain understudied 121 . In a large meta-analysis of heritability estimates in over 14 million twin pairs for over 17,000 traits 122 , less than 5% of the studies published between 1958 and 2012 investigated early life traits or childhood-onset disorders. The low heritability estimates and substantial shared environmental effects reported for several of these traits might make them especially interesting to study 123 , 124 . Researchers should ideally follow mothers of twins and twins from the pre-pregnancy period. This would seem difficult, as predicting a twin pregnancy is hardly possible, but many fertility clinics hold medical records on the conception of twins and have the potential to create such research resources.

For all phenotypes, we need to consider sample sizes when applying twin methods. Is larger always better, or can ‘better’ also involve phenotypes with improved reliability or designs that profit from unique events? An example would be the National Aeronautics and Space Administration twin study that enabled the investigation of the effect of long-duration spaceflight on the human body in one pair of identical twins 125 . While astronaut Mark Kelly stayed on Earth, his identical twin Scott spent a year in space. Spaceflight affected some bodily functions even six months after returning to Earth, including gene expression changes. Other changes, such as body weight and metabolite levels, were less persistent and returned to the pre-flight levels after a shorter time.

Twin studies have helped in understanding the influence of the environment by analysing traits that tend to be labelled as ‘environmental’, such as social environment, leisure-time activities and life events, indicating that with an average heritability of 49%, these ‘environmental’ variables are partially under genetic control 126 . The classical twin design is still one of the most powerful to distinguish between unique and shared environmental influences. While GWAS approaches are increasingly powerful to estimate (besides heritability) the bivariate genetic correlations between traits, in contrast to twin studies, they do not inform on the phenotypic or environmental correlations between phenotypes and do not account for the total heritability of the traits. Moreover, traits that show evidence of shared environment are predicted to show evidence of genetic nurture in designs with non-transmitted PGSs 127 . The results from twin studies can thus generate new hypotheses about genetic nurture.

Twin registers often have multigenerational genotype data and a wide variety of phenotypes and will continue to be essential contributors of data to GWASs, within-family designs, causality modelling, intergenerational transmission and longitudinal studies. Twin researchers mostly collect twin data, though interest in twin research by other disciplines is beneficial, as every discipline has its way of measuring constructs 128 . Diverse disciplines should collaborate to ensure clarity about the meaning of results, such as when a trait is reported to have high heritability 129 . Despite rapid technological advances enabling increasingly large-scale omics investigations for complex human traits and the renewed interest in family-based methods, twin designs have contributed to the major discoveries in behavioural genomics and will continue to do so 130 .

Code availability

The code to reproduce Fig. 2b is available on GitHub: https://github.com/KuznetsovDima/NATHUMBEHAV-22123354/ .

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Acknowledgements

We thank N. G. Martin, C. V. Dolan, J. Couvée and J. van Dongen for their valuable contributions to the draft versions of this manuscript. The current work is supported by the Royal Netherlands Academy of Science Professor Award (no. PAH/6635) to D.I.B. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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These authors contributed equally: Fiona A. Hagenbeek, Jana S. Hirzinger, Sophie Breunig.

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Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Fiona A. Hagenbeek, Jana S. Hirzinger, Sophie Breunig, Susanne Bruins, Dmitry V. Kuznetsov, Kirsten Schut, Veronika V. Odintsova & Dorret I. Boomsma

Amsterdam Public Health Research Institute, Amsterdam, the Netherlands

Fiona A. Hagenbeek, Susanne Bruins & Dorret I. Boomsma

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Jana S. Hirzinger

Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, CO, USA

Sophie Breunig

Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

Faculty of Sociology, Bielefeld University, Bielefeld, Germany

Dmitry V. Kuznetsov

Nightingale Health Plc, Helsinki, Finland

Kirsten Schut

Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands

Veronika V. Odintsova & Dorret I. Boomsma

Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands

Veronika V. Odintsova

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Conceptualization: D.I.B. Funding acquisition: D.I.B. Project administration: F.A.H. and D.I.B. Supervision: D.I.B. Visualization: F.A.H., S. Bruins, D.V.K. and V.V.O. Writing—original draft: all authors. Writing—review and editing: all authors. All authors have read and agreed to the published version of the manuscript.

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Hagenbeek, F.A., Hirzinger, J.S., Breunig, S. et al. Maximizing the value of twin studies in health and behaviour. Nat Hum Behav 7 , 849–860 (2023). https://doi.org/10.1038/s41562-023-01609-6

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Received : 20 December 2022

Accepted : 19 April 2023

Published : 15 May 2023

Issue Date : June 2023

DOI : https://doi.org/10.1038/s41562-023-01609-6

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NeuRA research reveals complex interplay of nature and nurture in emotion and cognition

The way our brain processes different emotional and cognitive tasks may be underpinned by common factors, find scientists from Neuroscience Research Australia (NeuRA) and UNSW Sydney .

In their latest study, published in the journal  Human Brain Mapping , Dr Haeme Park and Associate Professor Justine Gatt , who hold joint positions at NeuRA and  UNSW Psychology , looked at how both emotion and cognition are influenced by the environment and genetics, using functional MRI (fMRI) scans on twins. 

“ There has been quite a lot of research looking at genetic versus environmental influences on brain structure,” says Dr Park, lead author of the study. ​ “ But it’s a lot harder to understand the function of our brains.”

The results revealed that the picture is extremely complex. Some emotional and cognitive tasks were partly associated with genetics, and others exclusively with environment. 

But they also found that some of the same genetic and environmental factors can play a role in the brain reacting to two different tasks. For example, the analysis showed that some of the same genetic factors are influencing how we process fear and happiness and also how we sustain our attention.

“ This study is interesting because we have further insight into how much of our life experiences modulate certain brain processes, which to a certain degree we have more control over, versus your biology, which you can’t change,” says A/​Prof. Gatt, Director of the  Centre for Wellbeing, Resilience and Recovery . ​ “ Knowing what areas of our brain function are linked strongly to our environment can help us develop personalised intervention approaches to promote higher mental wellbeing.” 

The importance of twin studies

The so called ​ ‘ nature vs nurture’ debate isn’t new. 

In fact, twin studies have become a unique research tool used by geneticists and psychologists to evaluate the influence of genetics and the effect of a person’s shared environment (family) and unique environment (the individual events that shape a life) on a particular trait. 

“ With twin studies, it’s important to recruit both identical and non-identical twins,” says A/​Prof. Gatt. ​ “ Identical twins share 100 per cent of their genetics and if they’re grown up together, they share the same environment. Whereas with the non-identical twins, they only have 50 per cent shared genetics, but they also have that common environment.”

“ In this study, we wanted to bridge lots of gaps in the literature and provide a more robust and thorough picture of how our genetics and environmental factors impact the expression of brain activity during emotional and cognitive tasks, by analysing twins,” says Dr Park.

Cognitive and emotional tasks

The most recent paper is  one   of   many  from the  TWIN‑E study , which recruited 1600 identical and non-identical twins from across the country in 2009 and is led by A/​Prof. Gatt. 

A subset of the original cohort participated in this particular study, with a total of 270 adult twins taking part. 

“ We get participants set up on the fMRI scanner bed which is fitted with goggles that enable them to see the tasks in front of them. The functional tasks involve them viewing different images, different stimuli, through the goggles,” says A/​Prof. Gatt. 

While the participants were completing the tasks, the fMRI machine was scanning their brain to measure its activity. 

The twins completed a total of five tasks. Two were linked to emotional responses, such as reactions to various expressions of different faces, and the other three were associated with cognition, such as the ability to sustain attention and short-term memory. 

Processing the fMRI scans show you which part of the brain light up for different processes, and how strongly the brain is activated can be measured on a scale. 

“ So individuals who show a lot of activation in that region have a higher number, whereas those with lower activation have a smaller number. We then use these figures to carry out what we call ​ ‘ twin modelling’ processes,” says Dr Park. ​ “ This is where we use statistics to break down how much of a role genetics and environment contributes to that number.”

Twin modelling results

Twin modelling methods revealed two key findings in their analysis of the results. 

Firstly, the researchers looked at the genetic versus environmental influence on each individual task. ​ “ We know that we use different brain networks for different processes – for example, processing either a crying face or a happy face is going to use different regions in the brain compared to trying to remember someone’s phone number,” says A/​Prof. Gatt. ​ “ But we found that for some of these networks, genetics plays a small to moderate, but significant role. And for other processes, it’s only the environment that determines brain function.”

The second part of the analysis found that there were similarities in the genetic and environmental factors that underpinned different tasks. 

“ For example, we discovered that how the brain processes fear and happiness (which was measured in the emotional tasks) and our ability to sustain attention (which was measured in the cognitive tasks), have some shared genetic factors,” says Dr Park. ​ “ This suggests that some common genetic features may underpin these very different processes.”

In contrast, the team also found that our ability to sustain our attention and our working memory have some of the same environmental contributions, suggesting that life experiences – which come from your environment – play a significant role in how brain activity is expressed for these two processes.

Mental wellbeing and resilience

While it’s clear that both our genetics and life experiences are important in determining how our brain functions, the puzzle is far from solved.

“ There’s still so much more to find out!” says Dr Park. The current participants have already been followed up more recently and have performed the same tasks again after 10 years. A/​Prof. Gatt, Dr Park and their team will be reassessing the results to see how the influences of genetics and environment on these brain processes change over time.

“ All these results paint a complex picture of the relationship between genes and environment that give rise to the brain activity underlying our cognition and emotion,” says A/​Prof. Gatt. But knowing more precise details may help to develop personalised intervention approaches in order to promote, for instance, higher mental wellbeing, or reduced psychological distress.

In fact, the ongoing TWIN‑E study focuses more broadly on mental wellbeing and resilience. ​ “ So, what we’re using this data for, beyond looking at genes and environment, is actually predicting mental wellbeing and resilience trajectories over time, and seeing how differences in markers like brain function and structure might profile people who are a bit more resilient or at more risk to a mental health problem,” says A/​Prof. Gatt. 

Understanding how much of our life experiences influences certain processes versus the influence of genetics is important when knowing what factors we can change and control, which is particularly significant for people with mood and anxiety disorders, explains A/​Prof. Gatt. ​ “ If someone has a tendency to attend to negative stimuli more than positive, and we know that there’s an element of environment contributing to that, with intervention or training, it’s potentially something we can target and improve for the better.

15 February 2024

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History of twin studies, how can twins be used to study nature and nurture.

Posted January 28, 2024 | Reviewed by Jessica Schrader

• Twin studies remain an insightful methodology for studying human behavior.
• Identical twins share 100% of genes, while fraternal share 50% of genes.
• Nature and nurture are inextricably connected when trying to determine the reasons behind behavior or disease.

It is one of the enduring debates in psychology, stemming all the way back to our philosophical origins: What is the relationship between our biology, or nature, and that of the environment , or nurture, in shaping human behavior? In early philosophical times, this “ nature versus nurture ” debate was presented as an either/or phenomenon—scholars generally advocated for one side as being more influential on human behavior and outcomes than the other.

Today, however, our ability to more closely study the human body and brain in particular reveals a far messier truth: human behavior is shaped by both nature and nurture. In fact, the two are so inextricably linked that it only makes sense to separate the concepts of biology and environment for conceptual purposes. In other words, nature and nurture intersect and are virtually impossible to neatly separate, with regard to the way they impact human behavior.

Rather than asking the question of whether a given quality, personality disposition, or disease an individual develops is biologically- or environmentally-based, a more apt question is: how much of a given disposition or disorder reflects biology, and how much environment? Or, how do biology and environment interact to produce a given disposition or disease that is being studied?

How do scientists then systematically study the biological and environmental influence on human behaviors? Traditionally, twin studies have been the method of choice. Commonly referred to as the perfect natural way to experiment on humans, twin studies typically study pairs of identical, fraternal, or a combination of both twin pairs, since they already share a significant amount of genetic material.

Specifically, identical twins —also referred to as monozygotic, “mono,” denoting they develop from one fertilized egg during reproduction, then later split into two—share 100% of their genes . In contrast, fraternal twins, also referred to as dizygotic, denoting “two,” reflect two separate eggs that are fertilized by two separate sperm. Unlike identical twins, fraternal twin pairs can be opposite sex, and share 50% of their genes. In fact, fraternal twins are no more genetically similar than any other biological siblings with the same parents. However, they do share the unique feature of being paired with their sibling since conception.

Twin studies are a valuable source for studying humans because of how much of their genes are shared. In experiments where twins are recruited as participants, researchers can manipulate environmental factors to measure their effect on the pairs, while their shared genes remain the same. In such a way, nature-versus-nurture influence can be quantified and scientifically investigated. Results from such studies have far-reaching implications regarding how the body and mind respond to environmental factors.

There are a number of current pop cultural depictions of how twin studies are being used to better understand important influences on human behavior. For instance, Netflix recently released a series entitled You Are What You Eat: A Twin Experiment, which manipulates diet for an eight-week span among identical twin pairs in an effort to determine what role it plays on a number of markers of health, including weight, energy levels, sexual performance, etc.

The question of how much behaviors reflect biology versus how much what we do or develop are byproducts of the environment, or a combination of both, will never entirely be answerable. There are so many complications and unique ways that individuals respond to identical exposures in the environment, for instance. However, twin studies remain an insightful and integral methodology that enables researchers to shed light on a question that has persisted for hundreds of years.

Copyright Azadeh Aalai 2024.

Azadeh Aalai, Ph.D., is an assistant professor of Psychology at Queensborough Community College in New York.

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The Role of Nature and Nurture for Individual Differences in Primary Emotional Systems: Evidence from a Twin Study

Christian montag.

1 Institute of Psychology and Education, Ulm University, Ulm, Germany

Elisabeth Hahn

2 Department of Psychology, University of Saarbrücken, Saarbrücken, Germany

Martin Reuter

3 Department of Psychology, University of Bonn, Bonn, Germany

4 Center for Economics and Neuroscience, University of Bonn, Bonn, Germany

Frank M. Spinath

5 Department of Psychology, The University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States of America

Jaak Panksepp

6 College of Veterinary Medicine, Washington State University, Washington 99164, United States of America

Conceived and designed the experiments: CM EH MR FS. Performed the experiments: EH. Analyzed the data: EH. Wrote the paper: CM EH KD JP. Conceptualized the ANPS questionnaire, on which this study is based, so they provided additional advice of tremendous importance for the study: KD JP. Collected the data: EH.

Associated Data

Ethical restrictions prevent public sharing of data. Parties interested in obtaining the data may contact the authors.

The present study investigated for the first time the relative importance of genetics and environment on individual differences in primary emotionality as measured with the Affective Neuroscience Personality Scales (ANPS) by means of a twin-sibling study design. In N = 795 participants (n = 303 monozygotic twins, n = 172 dizygotic twins and n = 267 non-twin full siblings), moderate to strong influences of genetics on individual differences in these emotional systems are observed. Lowest heritability estimates are presented for the SEEKING system (33%) and highest for the PLAY system (69%). Further, multivariate genetic modeling was applied to the data showing that associations among the six ANPS scales were influences by both, a genetic as well as an environmental overlap between them. In sum, the study underlines the usefulness of the ANPS for biologically oriented personality psychology research.

Introduction

The study of primary emotional systems represents an important research endeavor to better understand psychological well-being and psychopathologies such as affective disorders in humans [ 1 ]. Specifically, it has been put forward that imbalances in these ancient emotional brain systems go along with psychopathologies, e. g. that a lack of PLAY behavior in childhood might be linked to Attention Deficit Hyperactivity Disorder (ADHD) later on or that an overactivation of the SADNESS (separation-distress, psychological-pain) system and the subsequence reduction of SEEKING urges are major cause for depression (for full discussion, see [ 2 , 3 ]). (Primary emotional systems are printed in capital letters, as a formal designation for primal emotional systems of mammalian brains, partly intended to distinguish them from the vernacular emotional terms commonly used in emotional and other psychological research. The need for scientifically clear designators for primary-process (i.e., evolved) brain emotional and motivational systems is essential, and the formal designators should help avoid mereological fallacies (part-whole confusions) which are abundant in neuropsychological discourse (see [ 4 ]). A major goal of Panksepp’s Affective Neuroscience perspective has been dedicated to elucidating how primal (i.e., evolved) neuropsychobiological emotional networks underlie core affective processes (using animal models to illuminate foundational human affects), and how their upward influence in the brain shape diverse higher-order psychological and behavioral processes. By applying techniques such as deep (subcortical) electrical stimulation of the mammalian brain and pharmacological challenges his group has provided evidence for seven distinct primary emotional systems (SEEKING, RAGE, FEAR, LUST, CARE, PANIC and PLAY) anchored in phylogenetically old brain areas which not only instigate instinctual emotional behaviors, but also influence and control the secondary processes of learning and memory and tertiary-process such as cognitive decision making [ 1 ]. These primal emotions are survival systems, which with various sensory and homeostatic (e.g., HUNGER and THIRST) affects constitute the primal value (reward and punishment) systems of the brain. These subcortical systems are foundational for higher mental processes in all animals since extensive damage to such systems compromise consciousness, and they are envisioned to guide the development of higher mental processes, including personality dimensions which, with maturation, gradually provide higher reciprocal-regulatory cortical control over lower affective processes.

The mammalian (especially human) prefrontal cortex and other neocortical regions can control emotional outbursts from subcortical areas (providing top-down behavioral and psychological regulation). But in extreme situations—such as in high danger—our brains often respond with stereotypic genetically-anchored affective response patterns (instigating bottom–up arousal of higher-order brain processes) such as strategies for fight, flight or freezing (e. g. [ 5 ]), which helped our ancestors to not only escape various hazardous situations but to develop cognitive skills to avoid them in the future (see also a new questionnaire measuring these distinct fear tendencies [ 6 ]). So different primary/basic emotions have different functions with respect to survival and reproductive behaviors. In the end a better understanding of the functioning and interplay of these emotional systems should facilitate development of new therapeutics to better treat a wide range of psychiatric disorders [ 2 , 3 ].

The seven primary emotional systems of Panksepp’s primary-process affective neuroscience can be divided into two larger groups of positive and negative emotions. The emotional systems belonging to the first group of positive emotions are called SEEKING, LUST, CARE and PLAY (in presumed evolutionary order), whereas the latter group representing negative emotions comprises RAGE (also labeled ANGER in discussions of human personality), FEAR (or “anxiety” in the vernacular), and PANIC (namely primary-process separation distress, or higher-order SADNESS, which we deemed a more clear and appropriate designator for human personality profiling). The SEEKING system energizes human beings and helps them not only to be energized with “enthusiasm” and “interest”, in explorative/investigative way in everyday life. The PLAY system has been best characterized not only by the instinctual nature of rough and tumble play in most mammals–a very bodily evolved form of play–best observed in all young mammals, including human childhood, with the brain mapping providing clarification of brain regions where Deep Brain Stimulation (DBS) evokes laughter-type play vocalizations in animal models [ 7 ]. The function of the PLAY system probably relates to learning about social structures/hierarchies (e.g., eventual social dominance), learning to cope with losing or being defeated, shaping social-appetitive motoric skills and from a psychological perspective, simply having fun (which may promote bodily and mental health). The LUST and CARE system are of high importance for reproductive success and social bonding and are deeply entwined. The PLAY system is probably evolutionary the youngest with LUST reproduction circuits evolving earlier than the genetic programs for CARE—nurturing other individuals especially one’s own offspring. The FEAR system has been already mentioned above and helps mammals to free themselves from danger. The RAGE/ANGER system facilitates acquiring and holding-on to resources, and can be activated by frustrations (that can arise from higher-order encoding of desires). Finally, the PANIC/SADNESS system reflects arousal of what has traditionally been called “separation distress” the chronic overactivity of which is associated with depression [ 2 , 3 , 8 ]. For cross-mammalian brain research purposes, this system has been formally designated the PANIC system, which is illustrated by typical panic behaviors and feeling (i.e., separation distress calls, commonly called “crying”) when children get lost and are out of sight of their parents or other caregivers.

Besides the importance of neuroscientific techniques, especially DBS, to study primary emotional systems, Davis et al. [ 9 ], published a self-report inventory called Affective Neuroscience Personality Scales (ANPS), updated and refined in Davis & Panksepp [ 10 ], aimed at measuring individual differences in these primary emotional systems. The publication of these scales represents an important addition to the toolbox of biologically/behaviorally oriented personality psychologists, because Panksepp’s primary emotional systems could be viewed as being among the evolutionary oldest contributors to human personality (influencing human personality bottom-up development as reflected by their neuroanatomical foundations in the “old-mammalian” and “reptilian” areas of Paul MacLean’s Triune Brain Concept; see also [ 11 ]). The ANPS contrasts to classic questionnaires reflecting the Five Factor Model of Personality (e. g. [ 12 ]) and may be more appropriate for guiding in the investigation of the biological underpinnings of individual differences in primary sources of temperament, namely one’s genetically controlled emotional strengths and weaknesses. For instance, Montag & Reuter [ 13 ] highlight the potential importance of these scales in the context of disentangling the molecular genetics of primary emotional systems and personality. As the Five Factor Model of Personality is based on a lexical (adjective-based) approach it does not help in hypothesizing about diverse neurobiological affect-engendering brain systems that are critical brain substrates underlying human personality. The usefulness of the ANPS for biologically-oriented personality psychology can be best explained by a small example. If animal models show that PLAY behavior in rodents is modulated by opioids (as it is, see [ 14 ]), the dynamics of brain opioid systems should also be of relevance for human ludic activities, because these ancient brain systems are highly conserved across species.

As postulated by Turkheimer [ 15 ] and newly confirmed within a meta-analysis [ 16 ], all human traits are heritable. For the Big Five personality traits, several studies in the past 50 years of research revealed a strong genetic basis for all five personality factors in the range of about 40–60% (e.g. [ 17 ]). In terms of environmental contributions, comparable amounts of personality variation can be explained by non-shared environmental experiences. This has also been underlined in a recent meta-analysis [ 18 ]. For the ANPS scales, Davis et al. [ 9 ] investigated the extent to which self-reports derived from the ANPS questionnaire were related to self-report measures of the Big Five personality traits, i.e. how closely core emotional systems were associated with basic personality traits. Each of the six ANPS scales was found to be closely related to at least one of the Big Five personality scales. The authors concluded that the six core emotional systems assessed by the ANPS scales constituted the roots of adult personality structures, and developmentally contributed to the construction of higher-order emotional traits. Given these findings and the theoretical concept behind the ANPS, one would postulate a strong genetic basis of all the basic emotional systems. With respect to associations among the ANPS scales, which can be depicted by a higher-order positive and negative system, one would further expect a common genetic basis underlying these emotional systems. Please note that LUST was intentionally dropped from the ANPS, because it overlaps greatly with homeostatic affects (e.g., peripheral hormonally-controlled core affects) and because of social reticence or lack of frankness in responding to questions concerning one’s sexuality. Also, such affective responses to one set of questions could potentially create spill-over problems for people responding to other trait questions frankly, but as discussed later, a Spirituality scale was added to evaluate therapeutically-important existential dimensions of existence.

To the best of our knowledge—there are currently no scientific-empirical studies showing the relative contribution of genetic influences on individual differences in these primal emotional foundations of human personality. Hence, the genetic and environmental etiology of individual differences in these traits as well as the etiology of associations among these systems remains poorly understood. Given this fact, the present study aimed to quantify for the first time, the relative influence of both nature and nurture on individual differences in primary emotional systems by means of identical (monozygotic) and fraternal (dizygotic) twin study. Univariate and multivariate genetic modeling was applied to investigate the extent of genetic sources on each emotional system and covariations among them to explore the structural nature of primary emotionality.

Participants

The sample was drawn from the Twin Study on Internet- and Online-Game Behavior (TwinGame), a study of adult twins and non-twin sibling pairs reared together. To realize the twin sample, we reverted to contact information from twins who had participated in previous voluntary German twin studies (e.g. SOEP twin study, ChronoS; for details see [ 19 ]. In addition, we invited twins and non-twin sibling pairs (with a maximum age difference of three years) via public announcements to participate in the study. Twins with previous contact information were contacted via telephone and invited to complete an online or paper-pencil version of our questionnaire addressing different areas, such as Internet consumption behavior, personality, health, subjective well-being, empathy and several attitudes. The resulting data set for the present study contained a total of 795 individuals (56% overall participation rate) including n = 303 monozygotic twins (149 complete pairs), n = 172 dizygotic twins (85 complete pairs), n = 267 non-twin siblings (122 complete pairs) and 53 individuals with unknown zygosity. Information on age and the gender distribution is presented in Table 1 .

Note . M age = Mean; SD age = Standard deviation; N = 53 individuals with unknown zygosity.

All participants filled in the Affective Neuroscience Personality Scales (ANPS), as described in the next section. Zygosity was determined through self-reports assessing physical similarity (e.g., eye color, hair structure, skin color) as well as the frequency of twin confusion by different relatives, teachers, and peers across the life span (accuracies in the magnitude of 95%; for details, see [ 20 , 21 ]). The study was approved by the research ethics’ committee of the University of Bonn, Germany.

Questionnaire

We administered the German version of the ANPS (Reuter, Panksepp, Davis & Montag, test manual to be published at Hogrefe Publishers, soon) containing 110 items ranging from strongly agree to strongly disagree (four-point Likert scale) and reflecting a German translation of the ANPS as published by Davis et al. [ 9 ]. The ANPS measures individual differences in all mentioned primary emotional systems with the exception of LUST for the reasons mentioned above. To reiterate, the questionnaire contains one additional scale called Spirituality, which reflects no known primary emotional system, but is included due to its potential psychotherapeutic relevance, (e. g. in the treatment of alcohol addiction). In the present sample, internal consistencies of the German version of the ANPS were satisfying and ranged from .69 (SEEK) to .87 (FEAR) which was in line with the psychometric characteristics reported by Davis et a. [ 9 ]. All these parameters are summarized in Table 2 . Bivariate phenotypic correlations among the six ANPS scales are presented in Table 3 .

Note . M = Mean total sample; SD = Standard deviation; M MZ = Mean MZ twins; M DZ = Mean DZ twins; M SIB = Mean siblings

Note . ANPS scales were corrected for age and sex effects

* = p < .05

** = p < .01.

Statistical Analyses

First, ANPS scale scores were computed by taking the sum of the corresponding items (in part reverse coded) for each ANPS factor as described by Davis et al. [ 9 ]. Prior to behavior genetic modelling, age and sex effects as well as prerequisites for structural equation modelling were inspected for each scale. The perfect correlation for age and sex in same-sex twins can inflate twin similarities [ 22 , 23 ]. To address this potential confounding, raw scores of the ANPS scales were corrected for linear and quadratic sex and age effects as well as interaction effects between sex and age prior to behavior genetic analyses by using multiple regression analyses. Following standard practice, genetic analyses were based on residual scores. Further, we basically used the standard model for twins reared together to decompose the phenotypic variation into its genetic and environmental variance components. The standard twin design is based on several assumptions: First, the equal environment assumption (EEA) assumes that MZ twins share environmental influences to the same degree as DZ twins (e.g., Borkenau, Riemann, Angleitner, & Spinath, 2002). Second, no assortative mating is assumed. Third, there is no gene-environment correlation or interaction (Purcell, 2002). In general, different sources of variance can be considered to explain why individuals differ with respect to certain characteristics and behaviors. On the one hand, individuals can differ because of genetic differences between them or vice versa family members (e.g., twins, siblings) can be similar to each other because they share a certain amount of genetic similarity. The genetic variance indicated as overall heritability can be subdivided into additive genetic influences (commonly denoted as A ) and non-additive genetic influences, modeled as genetic dominance (commonly denoted as D ). On the other hand, resemblance between family members can be due to shared environmental experiences contributing to similarity while differences between family members can be explained by different environmental experiences that are specific to each individual and contribute to dissimilarity. Hence, the environmental variance comprises shared (commonly denoted as C ) and non-shared environmental influences (commonly denoted as E ). Non-shared environmental influences are usually modeled as residual variance that includes measurement error [ 24 ].

In the basic twin model, analyses are based on the comparison of the MZ and DZ twin similarities that is being traced back to the difference in the proportion of segregating genes shared between MZ twins and DZ twins. More specifically, different patterns of MZ and DZ resemblance suggest which influences should be expected to be important. For instance, higher MZ twin correlations than DZ twin correlations are indicative of genetic influences in general because of the higher genetic similarity of MZ twins. MZ twins share 100% of their additive genetic background, while DZ twins (and non-twin siblings) share on average only 50% of additive genetic influences. If the MZ twin correlation is more than twice the DZ twin correlation, there is also evidence of genetic effects due to dominance over additive genetic influences because MZ twins share 100% D influences, while for DZ twins, the dominance component should be about .25. Less than perfect MZ twin correlations (rMZ< 1) suggest non-shared environmental influences, not only developmental-learning but also post-natal epigenetic ones, contributing to this dissimilarity. Comparable high correlations for both MZ and DZ are indicative of shared environmental influences. In the twins reared together model, however, genetic dominance and shared environmental influences are confounded and cannot be estimated simultaneously [ 25 ]. Whether shared environment or genetic dominance can be expected in a particular model depends on the pattern of MZ and DZ twin similarities. In the present design, we included a third group of non-twin siblings. Just as DZ twins, non-twin siblings share on average half of their segregating genes (A) and 25% D influences. However, twin and non-twin siblings may differ concerning the impact of shared environment. DZ twins share the same prenatal environment, belong to the same cohort of children and because they are twins there could be something like a “specific twin environment”. Therefore, sources of variation unique to twins might be valid if DZ twins remain more alike than non-twin siblings after genetic effects are accounted for. To investigate twin specific environmental influences, we first specified different shared and non-shared environmental influences for twins (MZ and DZ twins) and non-twins siblings. After fitting this model, we equated twin and non-twin environmental influences and compared the fit statistics to determine the importance of twin-specific environmental influences.

MZ and DZ as well as non-twin sibling variance–covariance matrices were calculated as intra-class correlations (ICCs) and analyzed by fitting genetically informative structural equation models via maximum likelihood using OpenMx [ 26 ]. To test for the assumptions of mean and variance homogeneity in the CTD, first, a fully saturated model was tested against a saturated model where means and variances were equated within twin and sibling pairs and across the groups (i.e., MZ, DZ, siblings) for each of the ANPS scales. The same procedure was performed prior to multivariate modeling. We then fitted univariate genetic models for each ANPS scale separately including the test whether twins differ significantly from non-twin siblings as described above. To gain a first insight into possible underlying sources of covariance among the six ANPS scales, multivariate cholesky decompositions [ 27 , 28 ] were fit to the data. This approach can be used (a) to determine the importance of genetic and environmental influences on associations between variables independent of their influence on other variables and (b) to analyze the extent to which genetic as well as environmental influences on the variables overlap. Further, more restricted and more theoretically driven models, such as different independent and common pathway models, were fit to the data to test for a possible distinction between for example a positive and negative component of basic emotional systems. Within an independent pathway model [ 29 ], common genetic and environmental factors can be specified representing shared variance between all ANPS scales or alternately representing a positive and a negative component of emotionality. These common genetic and environmental factors influence the observed variables directly, without an intermediate higher order factor. In addition, scale specific factors are specified. Since the evidence for the existence of a clear distinction between a negative and positive emotional system is scare, additional common pathway models [ 30 ] were investigated. The first common pathway model assumes that the phenotypic covariance between all six scales can be explained by a single ‘phenotypic’ latent variable that can be decomposed into genetic and environmental factors. The second common pathway model specifies two phenotypic latent variables, one for SEEK, CARE and PLAY as positive component and one for FEAR, ANGER and SADNESS as negative component. Also, combined independent pathway and cholesky specifications were applied to the data (for similar implementations of these models, see [ 31 ]). Given that the cholesky decomposition model is fully parameterized, it can be used as a reference model to evaluate the fits of the more restricted models.

Overall model fit was evaluated by using the χ 2 -test as well as the Akaike’s information criterion (AIC; [ 32 ]). The lower the AIC, the better the fit of the model to the observed data. Due to the limited sample size and hence power considerations, we focus on the results for the full models (ADE and ACE models), instead of reduced models (e.g. AE model without shared environmental influences), given that the exclusion of any genetic or environmental effect may result in biased estimates of the remaining factors in the model, even if the removed factor was not significant [ 25 ]. With respect to multivariate model fitting, nested submodels were compared by hierarchic χ 2 -test. The χ 2 -statistic is computed by subtracting -2LL (log-likelihood) for the full model from that for a reduced model. We performed model fit comparisons for each multivariate submodel with respect to the full cholesky model as well as the respective full model within the specific type of multivariate model (e.g. within the group of independent pathway models). Given the complexity of the multivariate models, we also observed reduced models (e.g. dropping common or specific D influences) here.

Descriptive statistics for each dimension of the ANPS for the total sample as well as separately for each group are provided in Table 2 . Mean and variance differences among twin and sibling groups were inspected given that they can affect overall model fit [ 33 ]. For each ANPS factor the normal distribution could be assumed according to visual inspection, skewness and kurtosis statistics and the results of the Kolmogorov-Smirnov goodness-of-fit test (p-values between .13 and .95). Correlations between age and ANPS scales ranged between -.02 (for Spirituality) and .24 ( p < .01; for PLAY). For FEAR ( t (793) = 6.20; p < .01), CARE ( t (793) = 9.27; p < .01), ANGER ( t (793) = 3.41; p < .01), SADNESS ( t (793) = 9.67; p < .01) and Spirituality ( t (793) = 3.16; p < .01), females scored slightly to modestly higher than males. (For these ANPS scales, we also inspected twin and sibling resemblances for male and female pairs separately to see if the relative importance of genetic and environmental influences differs for male and female. For all scales, patterns of resemblances were comparable to those derived from to total twin and sibling groups indicating no meaningful gender differences with respect to the relative contribution of genetic and environmental influences. Therefore heritability was estimates based on the total sample.) After correction for age and sex, there were no statistically significant differences between group means and variances as determined by one-way ANOVAs and Levene’s tests for the residual scores of all ANPS dimensions. As can be seen in Table 3 , correlations ranged between .00 and .66 for twin 1 as well as .01 and .62 for twin 2 indicating a large overlap between specific ANPS scales. Table 4 shows twin and non-twin sibling ICCs as well as p-value differences for ICCs between DZ twins and non-twin siblings. As can be seen, MZ twin correlations exceeded those of the DZ twin and non-twin sibling pairs in all cases. For FEAR, ANGER, PLAY and SADNESS, MZ twin correlations were over double the DZ correlations suggesting genetic dominance influences to be especially important. Regarding SEEK CARE and Spirituality, twin correlations rather pointed to shared environmental influences. Apart from the pattern of twin similarities, relatively high resemblances within non-twin siblings rather indicated shared environmental influences for all ANPS dimension except SEEK and SADNESS. Moreover, sibling resemblances were significantly different from the corresponding DZ twin resemblance for CARE. As described above, both genetic dominance and shared environment cannot be estimated simultaneously. Because of these somewhat ambiguous patterns of similarities, we compared models including shared environment or genetic dominance (based on AIC) for all ANPS dimensions. Model fit statistics for the full and best-fitting models as well as parameter estimates are shown in Table 5 . Model fitting results showed good model fits for all univariate models compared to the saturated model.

Note : MZ = Monozygotic twins; DZ = Dizygotic twins; a Correlations between DZ twins and siblings were tested for significant differences, two-tailed testing

* p < .05

** p < .01. The numbers in brackets refer to the confidence intervals.

Note : All parameter estimates are presented squared and fully standardized. A = additive genetic influences; D = non-additive genetic influences; C = shared environmental influence; E = non-shared environmental influence; AIC = Akaike’s information criterion

* p < .05; the preferred model is boldfaced.

First of all, models with different environmental estimates for twin and non-twin sibling pairs (i.e. assuming specific twin influences) did not fit the data significantly better than either of the models equating these influences. The final models for all ANPS scales favored equal environmental estimates across all groups. For ANGER and Spirituality an ACE model including additive genetic, shared and non-shared environmental influences yielded the best model fit while for the remaining dimensions an ADE model including additive and non-additive genetic as well as non-shared environmental influences fitted the data best. Fully standardized, heritability estimates (including additive and non-additive genetic influences) ranged from 33% for SEEK up to 69% for PLAY. Regarding FEAR, PLAY and SADNESS these genetic influences were in large part of a non-additive nature while SEEK and CARE showed only small proportions of non-additivity (between 3% for CARE and 68% for PLAY). With respect to the ACE models for ANGER and Spirituality, shared environmental influences were not significant and explained only 9%, respectively 5% of the variance. In comparison, non-shared environmental influences ranged between 31% (PLAY) and 67% (SEEK). Although internal consistencies for the ANPS scales were all no less than acceptable, any random measurement error affects estimates of genetic and environmental influences that typically lead to an underestimation of heritability [ 22 ]. Therefore, we further corrected heritability estimates (including additive and non-additive influences) for the corresponding reliabilities of the scales to get a more appropriate basis to compare them. (Heritability estimates from the model were standardized based on a variance of 1. To get estimates for the true variance corrected for measurement error, we used the following formula: h 2 corr = h 2 /α) After correction, lowest heritability estimates were found for ANGER (37%) and SEEK (48%), followed by SADNESS (56%), FEAR (60%), and Spirituality (64%). Highest estimates appeared for CARE and PLAY (82% and 88%).

The model fitting results for the multivariate genetic models are presented in Table 6 . All multivariate genetic models were tested compared to the multivariate saturated model and showed no significant differences in overall model fit statistics. The full cholesky decomposition model including specific and common additive and non-additive genetic as well as non-shared environmental influences for each of the six ANPS scales and among them provided a good fit to the data. Compared to this ‘baseline’ model, common pathway models (Model 13 and 14) with phenotypic latent factors (one or two factors) did not describe the data well. Within the independent pathway models (Model 4–12), the best fitting model (Model 9; see Fig 1 for an illustration) included an independent pathway specification for additive genetic influences and a cholesky decomposition for non-additive genetic influences as well as for non-shared environmental influences.

The best fitting model, with an independent pathway specification for additive genetic influences (A), and a cholesky decomposition for non-additive genetic influences (D) and non-shared environmental influences (E). For a better illustration, the model only shows A and E influences. D influences are not shown in the Figure, but were modeled the same way as E influences using a cholesky decomposition. For simplicity, the model is shown only for one member of a pair.

Note . A = additive genetic influences; D = non-additive genetic influences; E = non-shared environmental influence; -2LL = -2 times Log-likelihood of data; df = degrees of freedom; AIC = Akaike’s information criterion; the preferred model is boldfaced.

Table 7 provides standardized coefficients of additive genetic, non-additive genetic and non-shared environmental influences on the variance of each scale as well as the covariation among the scales based on the best fitting model. The results showed that the additive genetic variance (between 1% and 19%) in each ANPS scale was common to all six scales and that there was no specific additive genetic variance for a specific ANPS scale. With respect to non-additive genetic influences, genetic correlations between the scales were small to moderate and ranged between -.55 and .52. This means that only a part of the non-additive genetic variation was common to the specific scales. The same pattern can be seen for the non-shared environmental influences. Environmental correlations ranged between -.16 and .51.

Note . A = additive genetic influences; D = non-additive genetic influences; E = non-shared environmental influence; Standardized estimates for A, D and E influences are boldfaced; Correlations for non-additive genetic and non-shared environmental influences between the six scales are pictured below the diagonal; As modeled by a single independent factor, additive genetic correlation is 1 between all ANPS scales.

The present study aimed to investigate the influence of genetics and the environment on individual differences in ANPS-estimated primary emotional systems by means of a twin study. Our results show that every scale of the ANPS is influenced by genetics, but to varying degrees. The lowest heritability estimates are observed for the SEEK, ANGER and SADNESS system ranging between 31 and 40% (corrected 42 and 58%). Highest heritability estimates are observed for FEAR, CARE and PLAY going beyond .50. The genetic influence on individual differences in the PLAY system is especially pronounced (about .67; corrected .86). Thereby the results were comparable to findings of other twin studies using different personality inventories such as the Five Factor Model (see [ 17 ] for a review). Previous studies on the Big Five personality traits reported substantial genetic influences to a comparable degree. Moreover, for most of the Big Five personality traits, especially Neuroticism, Extraversion, Openness as well as Conscientiousness, there is substantial evidence for non-additive genetic influences [ 4 , 34 ]. Therefore, one explanation for the relation between PLAY and Extraversion and the connection of FEAR, ANGER and SADNESS with Neuroticism [ 9 ] could be that there is a genetic link including non-additivity between these constructs.

Moreover, a comparison of a variety of different multivariate genetic models provided first insights into genetic and environmental causes of phenotypic relations among the ANPS scales. The best fitting model showed an independent pathway specification for additive genetic influences and a cholesky composition for non-additive genetic as well as non-shared environmental influences. The finding of a single additive genetic component indicates that different primary emotional systems are not distinct at the level of additive genetic influences because all six scales loaded on one genetic factor. One explanation for this common genetic factor could be a similar set of genes. However, for non-additive genetic influences as well as non-shared environmental influences, correlations were small to moderate suggesting independent influences on specific emotional systems. So, although it can be assumed that genetic influences—mainly represented by a common genetic factor for all scales—are important, influences of non-shared environmental factors unique to each ANPS scale explain the remaining part of the variance. As the primary emotional systems could be seen as the basis of the Five Factor Model (e.g. PLAY underlying Extraversion or SEEK underlying Openness to Experience), similar non-environmental factors could play a role as observed in twin studies on the Five Factor Model. Such non-environmental variables being responsible for differences of the investigated persons of the same family could be “family composition, parental treatment, sibling interactions and extra-familial influences such as peers in addition to non-systematic factors. “([ 35 ]; p. 584)

Following from these findings the administration of the scales is of special value for molecular genetic studies (e. g. [ 36 , 37 , 38 ] These studies show that (an interaction of) dopaminergic genetic markers, but also an interaction of serotonergic and oxytocinergic markers are associated with individual differences in the primary emotional systems as measured with the ANPS), because a) an influence of genetics on individual differences of all primary emotional systems is demonstrated and b) the ANPS along with Panksepp’s cross species Affective Neuroscience approach to understanding primal emotions [ 1 ] now represents a genetically substantiated guide to test different brain transmitter systems and neuroanatomical structures (e. g. with MRI, [ 39 ]) in the context of each of the distinct primary emotional systems.

There are also limitations. Clearly, the questionnaire represents a cognitive approach to one’s own emotional experience; therefore it does not grasp emotional tendencies in a neuroscientifically direct way, e. g. as by directly observing human emotional behavioral and concurrent brain activities. This is put by Davis & Panksepp ([ 10 ], page 1952) as follows: “Although ANPS items attempt to address primary affects directly, since all self-report assessments must include cognitive reflection, we interpret the ANPS scales as tertiary (thought-mediated) approximations of the influence of the various primary emotional systems in people’s lives.”A second limitation concerns the sample size of the present twin study, which is relatively small. Previous studies have shown that some influences (e.g. shared environment) were often found to be non-significant due to small sample sizes and in consequence less power to detect them. In consideration of this issue, we decided to present the full model and not to exclude non-significant influences. However, compared to the classical twin approach, our sample was not limited just to twin data, which strengthens the assumption that the results are representative of the population. Third, there are some limitations that are inherent to most behavior genetic studies concerning different assumptions of the classical twin design (for an overview, see [ 21 ]). For example, the effect of gene-environment correlation and interaction could also be relevant in explaining individual differences in primary emotional systems, and hence should be considered in future research which requires information about specific environmental characteristics or specific genes.

In sum, the present study demonstrates that the ANPS is a new substantive empirical tool for biological oriented personality psychologists, which can advance the understanding of other major dimensions of human life. We anticipate the relevance of such understanding to eventually contribute to the study of imbalances of various primary emotional systems not only in various human addictions, but also a wide range of psychopathologies [ 40 , 41 ] especially various affective disorders (e.g. [ 2 , 3 , 8 ]).

Acknowledgments

The present study was funded by a grant awarded to CM by the German Research Foundation (DFG MO 2363/2-1). Moreover, the position of CM is funded by a Heisenberg grant awarded to him by the German Research Foundation (DFG MO 2363/3-1).

Funding Statement

CM is funded by a Heisenberg grant awarded to him by the German Research Foundation (DFG, MO 2363/3-1). Moreover, the present study is funded by the German Research Foundation (DFG, MO 2363/2-1), www.dfg.de .

Data Availability

Nature vs. nurture: How twins can help us understand the microbiome

Apr 13, 2020 | Immunology , Microbiome

by Helen Robertson

In 1933, identical twin baby boys Oskar Stohr and Jack Yufe were separated as a consequence of their parents’ divorce. Their subsequent upbringings could not have been more different: Oskar was brought up as a Catholic in Germany and became an enthusiastic member of the Hitler Youth. Jack remained in the Caribbean where they were born, was Jewish, and even lived for a time in Israel. Yet when they were reunited some fifty years after they last saw each other they had an uncanny number of similarities. They shared thought patterns, walking gait, a taste for spicy food, and perhaps most unusually, a habit of flushing the toilet before using it.

The unfortunate separation of identical twins provides scientists with an exceptional laboratory  for exploring the “nature vs nurture” conundrum. How much of our identity is conferred by our genes, and how much is a product of the environment in which we are raised? This is also true for our microbiome: twin studies have shown that the microbiomes of identical siblings are far more similar than those of fraternal twins, indicating that genes are at play.

UChicago researchers Alexander Chervonsky, PhD, and Tatyana Golovkina, PhD , are particularly interested in exploring how genetics—especially the genes that control our immune system— influence the composition of our microbiome. They chose to explore this question with mice.

Both mice and humans have two types of immunity: innate, “inborn” immunity, the first line of defense against pathogens, and adaptive immunity, in which immune cells are trained by the specific pathogens they encounter to fight off the same bad guys in the future.

To make sure all the mice used in the study started with the same microbes, Chervonsky and Golovkina needed to isolate them from the regular, bacteria-filled world. A normal mouse—much like a normal human—is born into an environment with trillions of bacteria, spread to them from their mothers and cagemates, their handlers, bedding, and food. Fortunately, UChicago’s special germ-free “gnotobiotic” mouse facility allows scientists to experiment on mice born and raised in an environment that hosts precisely zero bacteria, which make the mice experimental blank slates.

The researchers transferred microbes from a source mouse, raised in a conventional environment, to several strains of germ-free mice: some genetically identical, others with slight differences in their immune-response genes. In collaboration with computational immunologist Aly A Khan, PhD , they compared the resulting microbiomes to see if changes to immune system genes resulted in different types of microbial communities.

They found that differences in the adaptive—targeted—immunity caused minimal differences in microbial composition, and that those differences affected only certain strains of bacteria. Other types of bacteria even took advantage of the genetic differences and multiplied.

The team was surprised to discover that it was the innate immune response—the one the mice were born with, that needs no training: it was more active in shaping the microbiome. But even then, the total influence over the microbes in the mice’s gut was fairly small, meaning there were likely other genetic and non-genetic factors at play in determining how bacteria colonized and proliferated in the animals guts.

The team intends to look deeper into understanding how genes and microbes influence each other in developing animals. But this study sets a valuable benchmark for future microbiome work: closely documenting how the immune system worked here in germ-free mice means comparisons against other studies are standardized. Now we have a better idea of the “nature” side of the equation.

The Gnotobiotic Research Animal Facility is a vital asset for researchers at UChicago, and just one example of the gold standard approaches being used by the Duchossois Family Institute to improve our understanding of the underlying components of health and wellness.

Helen Robertson is a postdoctoral scholar in Molecular Evolutionary Biology at the University of Chicago, with a keen interest in science communication and science in society.

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“The History of Twins, As a Criterion of the Relative Powers of Nature and Nurture” (1875), by Francis Galton

In the article “The History of Twins, As a Criterion of the Relative Powers of Nature and Nurture,” Francis Galton describes his study of twins. Published in 1875 in Fraser’s Magazine in London, England, the article lays out Galton’s use of twins to examine and distinguish between the characteristics people have at birth and the characteristics they receive from the circumstances of life and experience. Galton calls those factors nature and nurture. Based on his study, Galton concluded that nature has a larger effect than nurture on development. By studying twins, Galton introduced a way to examine the effects of nature and nurture in people who were born with similar traits, which allowed him to focus on the effects of experience on a person’s development.

Galton worked in England and studied many subjects, including geography, literature, heredity, and statistics. He also contributed many publications and inventions to science. In 1859, Galton’s half cousin Charles Darwin published On the Origin of Species , a book in which Darwin introduced the theory of evolution or the change in the heritable characteristics of biological populations over successive generations, natural selection or survival of better adapted individuals, and heredity or the passing of genetic information from parents to offspring. While genes were not completely understood at the time, researchers were trying to determine if certain traits such as intelligence or strength were present at birth, or what is also referred to as available through nature, or if those traits were obtained through experience and the environment that a person develops in after birth, which is also referred to as the effects of nurture. Galton’s studies about the effects of nature and nurture followed from theories presented in Darwin’s book. To study the effects of nature and nurture on an individual’s disposition and intellectual ability, Galton studied twins, who had similar or identical traits.

As many pieces of literature at that time, Galton’s article on twins is not separated into different sections. Galton starts his article by describing why he chose to conduct his examination of the effects of nature and nurture through the study of twins. Galton presents his own definition of twins. He then moves on to describe how he was able to collect data about twins through his studies. Galton then talks about the data he collected. Galton uses the descriptions in inquiries he collected to compare and contrast different physical and mental characteristics of twins, and he finally states the conclusion of his study.

Galton starts his article by stating the difficulty of determining the effects of nature and nurture on an individual’s intellectual ability and success in life when studying unrelated people. He notes that no matter how plausible it may seem that an individual inherited traits of intellectual aptitude from their parents, the success of an individual can always be attributed to the education and experience that individual received throughout her life. Galton continues to explain that twins should inherit similar traits from their parents, and twin studies would reveal whether nature or nurture has a bigger effect. To gain direct evidence about the effects of nature and nurture, Galton states that he examined twins who were similar during childhood and who were educated together and then determine if they became dissimilar as they grew. He states that he could ask the family their opinions on what caused the eventual dissimilarity. Galton also explains that he examined twins who were very dissimilar during their youth and determined if they become more alike as they grew.

To clarify the terms in his article, Galton provides two distinct definitions for the occurrence of twins. Galton first defines twins as being more than one offspring who are born at the same time. He gives examples of animals that normally give birth to multiple offspring. He defines a second subset of twins as more than one offspring born from double-yolked eggs that occur due to two germinal spots in the same ovum. In the article, Galton classifies twins into three groups, either strongly alike, moderately alike, or extremely dissimilar. He finally explains that from his observations, if the twins are of different gender, then they are never closely alike and did not originate from a single double-yolked egg. Galton does not specify the criteria he used to classify the three groups.

After defining his terms, Galton describes his study. In his paper, Galton states that he sent out surveys to twins or people who were related to twins. The surveys contained questions organized in thirteen groups. The last group of questions asked for the addresses of other twins who might be likely to respond to the surveys, which allowed Galton to obtain a large number of reported cases. Galton states he received responses from eighty sets of twins, and he focuses on thirty-five of those cases in his article, because those responses were the most detailed.

Galton continues to explain the data he received. Galton mentions that from the responses he received, only a few mentioned twins that were completely similar and indistinguishable. According to Galton, most of the responses he received detailed twins that had nearly identical hair and eye color, and were similar in weight, height, strength, and vocal intonation. He states that respondents reported many differences in handwriting. Galton describes interactions between the twins and their families and friends, and how the identities of the twins could be easily confused by other people. He also mentions that with all the similarities, there always seemed to be a difference of expression that helped identify the twins from each other. He also notes that mothers are more likely to be able to distinguish between twins and that the similarities are more common during youth but that twins become easier to distinguish as they mature into adulthood.

Galton provides anecdotes about several cases he received in which twins succeeded in intentionally misleading people about their identity. Galton also describes cases in which parents were not able to distinguish between their descendants, or children who could not distinguish between their twin mother and aunt, emphasizing the fact that twins remained similar even after maturity. Other descriptions include cases in which twins suffer from similar ailments at similar times even when they live in different areas. Galton also states that there is often similarity in twins’ ideas, remarks, and decisions.

Galton states that of the thirty-five cases about which he received detailed responses, sixteen cases were described as closely similar and nineteen were described as much alike but subject to certain differences. He states that the differences provided often related to personality traits such as energetic, gentle, timid, fearless, calm, independent, among others.

Finally, Galton mentions that twins that are similar during childhood rarely become dissimilar during maturity, even when they are raised in different environments. Moreover, Galton mentions that from twenty cases he looked at, twins that were born with different genders or were dissimilar at birth never became similar even when they were raised in similar environments. Galton uses those two observations to reach the conclusion of his study and concludes that nature has a larger effect than nurture on development.

After Galton’s study, scientists around the world began using twins in the study of heredity. In 1905, Edward Thorndike, a psychologist in the US, conducted physiological tests on fifty sets of twins and concluded that the similarity of twins were innate rather than acquired. In the mid-1900s, Hermann Werner Siemens, a dermatologist in Europe, introduced the widely used twin method in scientific studies. He used twin studies to determine the role of genes in skin diseases and distinguished between identical and non-identical twins. Those studies became the basis of understanding the development of twins and provided insight about heredity.

Galton’s article is highly cited, and his studies inspired multiple twin studies that followed from his work. According to the writers, Horatio Newman, Frank Freeman, and Karl Holzinger in a book that discusses various twin studies, Galton was the first investigator to relate the likeness of twins to evidence of inheritance.

Galton’s study of twins was the first to incorporate twins to determine the relationship between nature and nurture. Because heredity was a popular topic in the mid to late 1900s, many researchers tried to understand what caused people to have different characteristics, such as height or inelegance. Galton’s article established a way for scientists to study how environmental effects can influence heredity.

• Darwin, Charles. On the Origin of Species . London: Ward, Lock, and Co, 1911. https://archive.org/details/in.ernet.dli.2015.93440 (Accessed September 1, 2017).
• Galton, Francis. “The History of Twins, As a Criterion of the Relative Powers of Nature and Nurture.” Fraser’s Magazine 12 (1875): 566–76. http://galton.org/essays/1870-1879/galton-1875-history-twins.pdf (Accessed September 1, 2017).
• Newman Horatio, Freeman Frank, and Holzinger Karl. Twins, a Study of Heredity and Environment . Chicago: The University of Chicago Press, 1937. https://archive.org/details/twinsastudyofher031983mbp (Accessed May, 3 2017).
• Siemens, Hermann Werner. “Die Zwillingspathologie: Ihre Bedeutung, ihre Methodik, ihre bishergien Ergebnisse (Twin Pathology: Its Importance, Its Methodology, Its Previous Results)” Berlin: Verlag von Julius Springer (1924).
• Thorndike, Edward. “Measurement of Twins.” The Journal of Philosophy, Psychology ad Scientific Methods . 2 (1905): 547–553. https://www.jstor.org/stable/2011451?seq=6#page_scan_tab_contents (Accessed September 1, 2017).

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Nature, nurture, and conservatism in the Australian twin study

• Published: May 1993
• Volume 23 , pages 287–290, ( 1993 )

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• John C. Loehlin 1  

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Church attendance, educational level, and six conservatism scales were the subject of a multivariate behavior-genetic analysis by Truett et al. (Behav. Genet. 22 , 43–62, 1992), based on responses from a large sample of adult Australian twins. These data are here analyzed in a different way to elicit general conservatism factors in the genetic, shared environmental, and unshared environmental covariation. The general genetic factor appears mainly to reflect intellectual sophistication; the general environmental factors, religious affiliation. These factors are similar, although not identical, for men and women.

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John C. Loehlin

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Loehlin, J.C. Nature, nurture, and conservatism in the Australian twin study. Behav Genet 23 , 287–290 (1993). https://doi.org/10.1007/BF01082468

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DOI : https://doi.org/10.1007/BF01082468

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Decoding Nature and Nurture: Insights From Twin Studies

Research finds genetics may help us distinguish between disgust and fear..

Posted March 15, 2024 | Reviewed by Tyler Woods

• Twin study using brain imaging reveals genetic influence on cognition, with less impact on emotion processing.
• One notable finding suggests genetics play a role in distinguishing between disgust and fear.
• Understanding genetic and environmental interactions can inform mental health interventions.

The nature versus nurture conundrum is an eternal debate. A recent study of 175 identical and 88 fraternal adult twins explores some of the questions of how genes and the environment determine the fundamental aspects of the emotional and rational life of humans.

The study, led by Haeme Park at Neuroscience Research Australia and recently published in the Human Brain Mapping journal, used advanced brain imaging techniques to investigate cognitive and emotional processes. By examining data from twins, the researchers sought to unravel the relative influence of genes and the environment on brain function.

What Are Twin Studies?

Using structural equation modeling, it is possible to break down the total variability observed in a specific trait, like conscious emotion recognition or sustained attention , into different components that contribute to this variability. These components include genetic factors, shared environmental effects, and individual environmental effects. By understanding how much of the variance in a trait is due to genetics (heritability) versus environmental factors, researchers can gain insights into the underlying mechanisms influencing human behavior.

Twin studies involve comparing data from identical and fraternal twins to understand the role of genetics and environment on various traits. Identical twins share close to all of their genetic material, while fraternal twins only share about 50 percent, allowing for a comparison that highlights genetic influences. Because twins often grow up in the same place, there is also less variance in environmental factors.

The recent study by Park used data from the large TWIN-E cohort study, which includes 1,669 healthy Australian twin adults split almost evenly between identical and fraternal and male and female twin pairs. The goal of the ongoing TWIN-E study is to identify biomarkers that influence emotional brain health over time.

The researchers collected extensive data, including online assessments, electroencephalograms, functional magnetic resonance imaging (fMRI), and cognitive tasks. The fMRI data was obtained from a subset of 263 participants which were then included in Park’s study.

Cognitive and Emotional Tasks

Previous studies have determined that genetics play a significant role in the structural development of different regions of the brain, but few have looked at genetics and brain function using brain imaging while participants actively complete a task.

For the Park study, the twins completed five tasks while having their brains scanned using functional magnetic resonance imaging. Two tasks measured their emotional responses: a nonconscious processing of emotional faces task and a conscious processing of emotional faces task. The participants were shown standardized faces depicting anger , fear , sadness, disgust, happiness , or neutral expressions. For the nonconscious version, the emotional faces were shown for only ten milliseconds before being masked by a neutral face so that there would not be a conscious processing of the emotion. For the conscious version, the emotional face was presented for 500 milliseconds. The participants were asked at the end how many different emotions they observed for each task.

The other three tasks measured cognition : a working memory and sustained attention task, a response inhibition task, and a selective attention and novelty processing task.

The N-back test measured working memory and sustained attention by showing a letter on a screen for 200 milliseconds and asking participants to remember which letters were yellow.

The Go-NoGo task measured response inhibition and involved participants pressing on a green "go" stimulus but ignoring the red "NoGo" stimulus.

The Oddball task measured selective attention and novelty processing by asking participants to respond to audible tones presented at 1000 Hertz and ignoring the tones presented at 50 Hertz.

While participants worked on the tasks, the functional magnetic resonance imaging would light up, revealing which parts of the brain were activated. The researchers then measured the brain activation and compared them across participants.

Study Results

In order to quantify the associations of heritability and brain activity, the researchers used two different methods: a multivariate independent component analysis (ICA) approach and a univariate brain region-of-interest (ROI) approach.

Independent component analysis is a statistical analysis that involves separating data into independent components that represent different sources of information. Researchers can detect local functional connectivity networks within the brain and identify distinct patterns and structures within the data. The univariate region of interest approach allows researchers to focus on specific brain regions known to be involved in cognitive and emotional functions. This method involves analyzing the activity of these predefined brain regions to assess their heritability.

For the working memory, sustained attention, nonconscious processing of positive and negative emotional faces, and selective attention tasks, the participants’ brain function all showed a small to moderate genetic influence, while conscious processing of emotion and response inhibition showed no evidence of heritability. Overall, the functional networks related to executive functions showed the most prominent evidence of genetic influence.

The independent component analysis results showed that the heritability of brain function depended on the particular task. For subconscious emotion recognition, the brain network involving the superior temporal gyrus and insula showed a significant genetic influence when individuals were exposed to nonconscious disgust compared to neutral stimuli (26 percent) and nonconscious fear compared to happy stimuli (23 percent). For the working memory networks, including the fronto-parietal region and the inferior parietal lobule, a significant heritability estimate was found (27 percent). The sustained attention networks, including the superior temporal and precentral gyri, insula, pre- and post-central gyri, and the inferior parietal lobule, showed significant heritability (33 percent). Novelty processing networks had significant heritability in the superior and middle temporal gyri (33 percent) and the frontoparietal-temporal network (32 percent).

The brain region of interest approach had varying results. The ventral striatum showed 20 percent heritability for conscious facial emotion stimuli. The bilateral amygdala revealed a significant heritability contribution (right: 33 percent, left: 34 percent) elicited by nonconscious facial emotion stimuli. The selective attention and novelty processing task showed a significant contribution of heritability in the medial superior prefrontal cortex (29 percent). The working memory, sustained attention, and response inhibition tasks showed no significant contribution of heritability in the brain regions of interest.

One notable finding is that the results suggest genetics play a role in distinguishing between disgust and fear more so than positive emotions. The researchers state that this may be due to an evolutionary adaptation, as identifying threats is key to survival. In general, however, they speculate that environmental factors have a greater influence on the perception of emotional expressions since “the intentional (conscious) and accurate perception of others’ emotional expressions within a particular environmental context is a paramount skill for successful social interactions.” Because social expectations vary so widely across cultures, it follows that the environment and external influences play a greater role in shaping social and emotional interactions compared to genetics.

Future Directions

The study is one of the first to analyze the shared genetic and environmental correlations across heritable brain networks/regions across multiple tasks. The researchers used advanced technology and research methods to investigate the extent to which brain function elicited by executive function and emotion processing may be heritable.

The results are interesting, yet they do not provide definitive answers to the complex nature versus nurture debate. Twin studies can provide interesting new information and allow researchers to unravel genetic mysteries, but there are limitations to using twin models, including assumptions of equal environments and random mating . While uncommon, it is also possible for twins to have different biological fathers and share only 25 percent of their DNA. These limitations in twin research methods keep us from arriving at definitive conclusions regarding the influence of genetics on behavior.

Researchers continue to seek answers that may provide groundbreaking revelations. A recent twin study published in JAMA Psychiatry demonstrated the significant impact of the environment on mental health outcomes, studying twins who had adverse childhood experiences . Longitudinal studies tracking brain development over time could provide more insights into how genetic and environmental factors interact to influence cognitive and emotional processes. Additionally, advances in imaging technology and computational methods offer exciting opportunities to explore the neural mechanisms underlying genetic influences on brain function.

Understanding the genetic basis of cognitive and emotional processes could also have various practical implications, informing mental health treatment and intervention. Insights into the genetic underpinnings of emotional processing could inform therapeutic strategies for conditions such as anxiety and depression . By recognizing the role of both genetics and the environment in shaping brain function, clinicians can tailor interventions to meet the specific needs of each patient.

The study represents a significant step forward in our understanding of the genetic and environmental influences on brain function. By uncovering the complex relationship between genes, brain networks, and cognitive processes, the research opens new avenues for personalized approaches to mental health care and intervention. As we continue to unravel the mysteries of the human brain, studies like this provide valuable insights into the influence of biology on human behavior.

William A. Haseltine, Ph.D., is known for his pioneering work on cancer, HIV/AIDS, and genomics. He is Chair and President of the global health think tank Access Health International. His recent books include My Lifelong Fight Against Disease.

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Rich, poor, educated, newly arrived, separated ... in 1990, researchers started following the lives of 167 babies — this is how it worked out

Is life what you make it? Or does the start you get make you? 

A study that followed 167 babies , from birth until the age of 34, has some answers.

Isabel has travelled the world, gained academic degrees and learned multiple languages. 

But she hasn't ticked off many of the life milestones she had expected: like owning a house, holding a secure job or starting a family.

In recent years Isabel has worked in cafes and relied on her family for housing.

"I always thought, especially in my 20s, I would have a kid by 30. And then at 30, I thought I would have a kid by 34. And now I'm like, 'Oh, we'll see.' And I always thought I would sort of be like this 'office person' wearing fancy suits to work," she said. 

"I didn't have an idea of what the job would be. But I definitely didn't think I'd be living in my Mum's spare room.

"So it hasn't turned out how I thought it would," Isabel said. 

"But I don't regret how it's turned out so far." 

The study began in 1990 and followed 167 babies born in the inner suburbs of Melbourne. 

The families were a diverse mix of incomes, education levels and ethnicities. Some lived in public housing, others were home owners. Some lived in multi-generational families. Some, like Isabel's parents — were separated.  

Dina Bowman, principal research fellow with anti-poverty organisation the Brotherhood of St. Laurence, said your start doesn't determine where you end up.

"We've learned that the family that you're born into, the circumstances you're born into isn't necessarily destiny. Social policies and programs can make a difference and do make a difference."

But Australia's social safety net, things like access to affordable public housing and welfare payments for people who are unemployed or carers, has degraded massively over the period of the study.

"There's been a disinvestment in some of the policies that are really important in evening up the odds," she said.

While public policy is a huge factor, the study keeps coming back to two key elements: family and support. 

And that doesn't always mean money.

Family strength

As costs linked to education have increased, the disparity between the ability of wealthy and poorer families in the study to help their children increased.

Having money also helped buffer people through periods of high unemployment.

Better-off children more likely to work in family businesses, be referred into jobs through friendship networks or invest in higher education.

"Family wealth or resources can act as a safety net, and a springboard."

It doesn't mean, Dr Bowman notes, that people with wealth don't experience problems. 

"Better off families still experienced tragedy and hard times," she said. 

"Disability, ill health, family separation, etc. But those resources act as a buffer and can provide that support when people fall on hard times."

And families with strong connections and connection to their culture can overcome some of the disadvantages of having less money.

"Money is important, resources are important," Dr Bowman said.

"But strong family bonds, and particularly cultural identity can really provide sources of support for children as they grow."

Alan wasn't born when his parents left Hong Kong and arrived in Australia in 1986.

Speaking little English and without formal qualifications, his dad started work as a waiter and restaurant manager. He's still at it. 

His mother worked as a cleaner, at a laundromat and raising children.

"So not highly skilled roles, but very hardworking people," he said. 

"My parents ingrained in me from a very young age that higher education was the road to success. Now it's deeply ingrained in my belief system."

Alan explains how "education equals success" was a consistent narrative in the life of his family.

"Every so often, during a car ride, home or at dinner, Dad was like, 'Do you want to be like me, working a really hard job, 8am to 8pm?' And so going to school, having an education, going to university was almost a default."

But it's not just education.

His family were also focused on owning property, and ingrained this in their children.

When Alan entered the workforce over a decade ago, his parents used their savings to fund a deposit on a small apartment. 

After Alan's marriage, he and his wife bought their own place, but they're currently living with his parents as they save for a larger house to accommodate an imminent second child.

"It's not new (to me) this concept of moving in and saving money. That was ideal for us," he said. 

"There are sacrifices, but we're having quality family time, my little boy is spending time with his grandparents on a daily basis."

Alan agrees that people with strong family support can negotiate the bumps of life more easily.

"Every milestone that me or my family have hit is not a result of no help from external things," he said. 

"Money's obviously a huge factor, but the support and social structures I've had have enabled me to be ... I wouldn't say 'successful', but like a relatively smooth and seamless transition from different life stages." 

Change is change

The report's long view takes in broad changes in society over decades. 

Because of that, its key findings aren't a shock, but they illustrate important shifts that have affected society in massive ways, for example:

• Education remains highly valued but does not guarantee a good job:

The rise of rolling short-term contracts and "gig" work in white-collar professions like teaching and academia mean even people with a high level of tertiary education, and the substantial debt that comes with it, can't get jobs that give them security in either employment or housing.

"So it's that really extended transition," Dr Bowman said.

"It's not just gaining a higher education — that qualification doesn't necessarily lead to a secure role that will enable you to save for a house deposit, have children. It's that delay and extension, extension."

• Economic and industrial-relations changes have affected employment and family relationships:

Those changes in the labour market, and an economic slowdown after the Global Financial Crisis (GFC) has also had the impact of keeping wage growth low. 

With rising living costs people who make their living from income (workers) haven't fared as well as those whose income comes from assets like real estate and shares. 

This has also extended the transition from schooling and higher education into work.

• Rising house prices have benefited home owners as investment in public housing has fallen:

When the study began in 1990 around one quarter of the families lived in public housing. 

But the amount state and federal governments spend on the sector had fallen spectacularly, until a recent turn-around. 

The rising cost of rent has made it difficult to get together the deposit needed for a loan. 

Many parents, even poorer ones, were making large financial gifts towards a house deposit for their children, or guaranteeing the loans with their own property as collateral.

• Pride in Australia and in their origins: 

Almost one-third of the families in the study didn't speak English at home when quizzed in 1990. 

Many flourished but others struggled and were faced with discrimination and racism.

By the time they were 21, over half the young people identified themselves simply as 'Australian' with a further quarter identified as Australian followed by another ethnicity (for example “Australian Chinese” or “Australian with a little bit of Italian dropped in as well”). 

Later studies have shown a growing sense of the benefits of cultural identity, Dr Bowman said.

"There were young people who were trying to deny their cultural identity when they were a bit younger. But as they've grown older and become parents themselves, they're embracing their cultural identities — and acknowledging a different form of wealth that it brings."

• Links between gender and inequality remain stubborn:

Significant shifts in policies and what's considered 'normal' about gender roles have occurred since 1990, with increased access to education meaning women and girls are in a wider and better-paying range of occupations. 

But "gendered cultural expectations" still hit aspirations. 

Then and now, it was usually mothers who withdrew from the work force when they had children. With that, patterns of work and care persisted along gender lines, even though there was a strong desire for more equal sharing of the load. 

In addition, housing costs influenced decisions and reinforced the gendered division of labour, because men tend to and continue to earn more it makes financial sense for them to work more, with women working part-time while also carrying the bulk of the care load.

Debt direction

Many of those broad factors have had a direct impact on Isabel's life and direction.

She currently owes the government more than $100,000 for her degrees.

"I think if education had been free, I probably would have also studied law," she said. 

"My brother went to a fancy private school. He's now a lawyer, and his partner is a lawyer, and he's bought a house. It's just a different way of looking at life and running through life, but those are choices that your parents make, too."

Being able to rely on the 'backstop' of family has enabled Isabel to travel and chase her interests.

"I am very lucky in that sense. And I haven't sort of thought about it until recently," she said. 

"Having family, supportive family that lets you do what you want to do without judging you too much is really, really special."

But the broken link between getting degrees and building a secure future is one that Isabel feels keenly.

She's been working casually in a cafe in the country.

"So no one's ever going to give me a credit card, let alone a bank loan at the moment." 

Isabel doesn't think she'll ever own a house — "a pipe dream" — but has had recent good fortune. 

She's soon starting a three-month contract in the curatorial field she's qualified in, conserving a historic homestead. 

"You can gain more experience and then potentially get longer work." 

"So it's hard. But it's nice to think that, yeah, you can eventually get working. Its great. It's beautiful."

Run the same race?

But the stories we tell ourselves aren't always how things work more broadly.

"There's a narrative," academic Elise Klein said.

"That if you work hard enough you'll get where you want to go: you get an education, you do all the right things you will be okay.

"But this research and other research also circulating shows that that's not the always the case." 

The associate professor of public policy at the Australian National University has written and consumed reams of studies about how the social safety net impacts society: those it catches and those it fails.

"A cohort of babies are all born at the same time. But policies and government decisions, get behind some of those babies and put obstacles in the way of others."

As a participant in the study put it, they all ran the same race "but some of them got mountains put in their way, and others got a helping hand". 

As Associate Professor Klein sees it, the report is a reminder to focus on decisions of state and federal governments.

"People talk about 'The Canberra Bubble', (but policy decisions) have real implications for people's lives," she said.

"You can just see it in this cohort of babies. Now they're adults, policies have actively picked winners and picked babies that have had to struggle."

With the study ending, its participants have reflected on the annual nature of assessing their own lives. 

Early in the study the researchers would talk to their parents but as they grew, it changed to be annual interviews or an online survey.

"The interviewers will come out to the house and then we would do a tape recorder session and my mum would be interviewed," Alan recalls. 

"This study has been part of every year. You slow down and have time to reflect on what has happened in the past year. 

"That self reflection enabled me to say, 'Yep, I've achieved something' or 'I need to, pull up my socks in this area of my life'."

"But I always say to people it's almost a jackpot. Coming and being able to grow up in Australia. It's a huge privilege"

That resonates with Isabel, despite the different path she's taken to Alan.

"People have different goals and different things they want to achieve in life. And mine happen to not be the ones that a lot of people have."

She smiles. 

"I've got other ones as well."

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