Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Primer
  • Published:

Behavioural genetics methods

Nature Reviews Methods Primers volume 3, Article number: 10 (2023) Cite this article

Subjects

Abstract

The question of why people show individual differences in their behaviours and capacities has intrigued researchers for centuries. Behaviour genetics offers us various methods to address this question. The answers are interesting for a range of research fields, varying from medicine to psychology, economics and neuroscience. Starting with twin and family studies in the late 1970s, the field of behaviour genetics has rapidly developed by applying molecular genetic techniques next to, and sometimes combined with, family data. The overarching conclusion at this point in time is that all measured human traits are to some extent heritable, and that many genetic variants, with each exerting a small effect, explain this heritability. Against this backdrop, we offer readers who might be less familiar with behaviour genetics a brief Primer on the topic. Sitting atop our list of goals is to be a resource for scholars interested in applying the widely useful techniques of the field to their particular specialty, regardless of what that might be.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Three common twin family models.
Fig. 2: Correlations in cognitive ability between twin pairs.
Fig. 3: Example raw and standardized variance path diagrams.
Fig. 4: A cross-lagged longitudinal MZ discordant twin design.
Fig. 5: The Wilson effect for cognitive ability.
Fig. 6: Estimates of within-family shrinkage for GWAS predictors across a range of phenotypes from a within-sibship study52.

Similar content being viewed by others

References

  1. Turkheimer, E. Three laws of behavior genetics and what they mean. Curr. Dir. Psychol. Sci. 9, 160–164 (2000).

    Article  Google Scholar 

  2. Harden, K. P. “Reports of my death were greatly exaggerated”: behavior genetics in the postgenomic era. Annu. Rev. Psychol. 72, 37–60 (2021).

    Article  Google Scholar 

  3. Barnes, J. C. et al. Demonstrating the validity of twin research in criminology. Criminology 52, 588–626 (2014).

    Article  Google Scholar 

  4. Friedman, N. P., Banich, M. T. & Keller, M. C. Twin studies to GWAS: there and back again. Trends Cognit. Sci. 25, 855–869 (2021).

    Article  Google Scholar 

  5. Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    Article  Google Scholar 

  6. Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J. & Laibson, D. I. The fourth law of behavior genetics. Curr. Dir. Psychol. Sci. 24, 304–312 (2015).

    Article  Google Scholar 

  7. Visscher, PeterM. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

    Article  Google Scholar 

  8. Plomin, R., DeFries, J. C. & Loehlin, J. C. Genotype–environment interaction and correlation in the analysis of human behavior. Psychol. Bull. 84, 309–322 (1997).

    Article  Google Scholar 

  9. Scarr, S. & McCartney, K. How people make their own environments: a theory of genotype–environment effects. Child. Dev. 54, 424 (1983).

    Google Scholar 

  10. Fowler-Finn, K. D. & Boutwell, B. B. Using variation in heritability estimates as a test of G × E in behavioral research: a brief research note. Behav. Genet. 49, 340–346 (2019).

    Article  Google Scholar 

  11. Tucker-Drob, E. M. & Bates, T. C. Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychol. Sci. 27, 138–149 (2016).

    Article  Google Scholar 

  12. Loehlin, J., Corley, R., Reynolds, C. & Wadsworth, S. Heritability × SES interaction for IQ: is it present in US adoption studies? Behav. Genet. 52, 1–8 (2022).

    Article  Google Scholar 

  13. Heath, A. C., Kendler, K. S., Eaves, L. J. & Markell, D. The resolution of cultural and biological inheritance: informativeness of different relationships. Behav. Genet. 15, 439–465 (1985).

    Article  Google Scholar 

  14. Truett, K. R. et al. A model system for analysis of family resemblance in extended kinships of twins. Behav. Genet. 24, 35–49 (1994).

    Article  Google Scholar 

  15. Keller, M. C. et al. Modeling extended twin family data I: description of the Cascade model. Twin Res. Hum. Genet. 12, 8–18 (2009).

    Article  Google Scholar 

  16. Pettersson, E. et al. Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls. Psychol. Med. 49, 1166–1173 (2019).

    Article  Google Scholar 

  17. Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).

    Google Scholar 

  18. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  Google Scholar 

  19. Young, A. I. Solving the missing heritability problem. PLoS Genet. 15, e1008222 (2019).

    Article  Google Scholar 

  20. Young, A. I. Discovering missing heritability in whole-genome sequencing data. Nat. Genet. 54, 224–226 (2022).

    Article  Google Scholar 

  21. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits’. Nat. Genet. 47, 1236–1241 (2015).

    Article  Google Scholar 

  22. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    Article  Google Scholar 

  23. Falconer, D. S. Introduction to quantitative genetics (Longman Group, 1960).

  24. Falconer, D. S. & Mackay, T. F. C. Quantitative Genetics (Longman Group, 1996).

  25. Martin, N. & Eaves, L. The genetical analysis of covariance structure. Heredity 38, 79–95 (1977).

    Article  Google Scholar 

  26. Neale, M. C. et al. OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81, 535–549 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  27. Burt, S. A., McGue, M. & Iacono, W. G. Nonshared environmental mediation of the association between deviant peer affiliation and adolescent externalizing behaviors over time: results from a cross-lagged monozygotic twin differences design. Dev. Psychol. 45, 1752–1760 (2009).

    Article  Google Scholar 

  28. Bartels, M., Boomsma, D. I., Hudziak, J. J., van Beijsterveldt, T. C. E. M. & van den Oord, E. J. C. G. Twins and the study of rater (dis)agreement. Psychol. Meth. 12, 451–466 (2007); erratum 13, 170 (2008).

    Article  Google Scholar 

  29. Nivard, M. G., Middeldorp, C. M., Dolan, C. V. & Boomsma, D. I. Genetic and environmental stability of neuroticism from adolescence to adulthood. Twin Res. Hum. Genet. 18, 746–754 (2015).

    Article  Google Scholar 

  30. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    Article  Google Scholar 

  31. Willoughby, E. & Lee, J. in The Cambridge Handbook of Intelligence and Cognitive Neuroscience (eds Barbey, A., Karama, S., & Haier, R.) 349–364 (Cambridge Univ. Press, 2021).

  32. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  Google Scholar 

  33. Plomin, R. & von Stumm, S. Polygenic scores: prediction versus explanation. Mol. Psychiatry 27, 49–52 (2022).

    Article  Google Scholar 

  34. Li, M. X. et al. A major gene model of adult height is suggested in Chinese. J. Hum. Genet. 49, 148–153 (2004).

    Article  Google Scholar 

  35. Roberts, D. F., Billewicz, W. Z. & McGregor, I. A. Heritability of stature in a West African population. Ann. Hum. Genet. 42, 15–24 (1978).

    Article  Google Scholar 

  36. Tarnoki, A. D., Tarnoki, D. L. & Molnar, A. A. Past, present and future of cardiovascular twin studies. Cor Vasa 56, e486–e493 (2014).

    Article  Google Scholar 

  37. Pechlivanis, S. et al. Risk prediction for coronary heart disease by a genetic risk score — results from the Heinz Nixdorf Recall study. BMC Med. Genet. 21, 178 (2020).

    Article  Google Scholar 

  38. Yang, R. et al. A healthy lifestyle mitigates the risk of heart disease related to type 2 diabetes: a prospective nested case–control study in a nationwide Swedish twin cohort. Diabetologia 64, 530–539 (2021).

    Article  Google Scholar 

  39. McGue, M., Osler, M. & Christensen, K. Causal inference and observational research: the utility of twins. Perspect. Psychol. Sci. 5, 546–556 (2010).

    Article  Google Scholar 

  40. Lemvigh, C. et al. The relative and interactive impact of multiple risk factors in schizophrenia spectrum disorders: a combined register-based and clinical twin study. Psychol. Med. https://doi.org/10.1017/S0033291721002749 (2021).

    Article  Google Scholar 

  41. Squarcina, L., Fagnani, C., Bellani, M., Altamura, C. A. & Brambilla, P. Twin studies for the investigation of the relationships between genetic factors and brain abnormalities in bipolar disorder. Epidemiol. Psychiatr. Sci. 25, 515–520 (2016).

    Article  Google Scholar 

  42. Bouchard, T. J. & McGue, M. Genetic and environmental influences on human psychological differences. J. Neurobiol. 54, 4–45 (2003).

    Article  Google Scholar 

  43. Wilson, R. S. The Louisville twin study: developmental synchronies in behavior. Child. Dev. 54, 298–316 (1983).

    Article  Google Scholar 

  44. Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L. & Tellegen, A. Sources of human psychological differences: the minnesota study of twins reared apart. Science. 250, 223–228 (1990).

    Article  ADS  Google Scholar 

  45. Bouchard, T. The Wilson effect: the increase in heritability of IQ with age. Twin Res. Hum. Genet. 16, 923–930 (2013).

    Article  Google Scholar 

  46. Deary, I. J., Johnson, W. & Houlihan, L. M. Genetic foundations of human intelligence. Hum. Genetics. 126, 215–232 (2009).

    Article  Google Scholar 

  47. Jang, K. L., Livesley, W. J. & Vernon, P. A. Heritability of the big five personality dimensions and their facets: a twin study. J. Pers. 64, 577–591 (1996).

    Article  Google Scholar 

  48. Tellegen, A. & Niels, G. W. Exploring personality through test construction: development of the multidimensional personality questionnaire. SAGE Handb. Pers. Theory Assess. 2, 261–292 (2008).

    Google Scholar 

  49. Vukasović, T. & Bratko, D. Heritability of personality: a meta-analysis of behavior genetic studies. Psychol. Bull. 141, 769–785 (2015).

    Article  Google Scholar 

  50. Krueger, R. F., South, S., Johnson, W. & Iacono, W. The heritability of personality is not always 50%: gene–environment interactions and correlations between personality and parenting. J. Pers. 76, 1485–1522 (2008).

    Article  Google Scholar 

  51. Matteson, L. K., McGue, M. & Iacono, W. G. Shared environmental influences on personality: a combined twin and adoption approach. Behav. Genet. 43, 491–504 (2013).

    Article  Google Scholar 

  52. Howe, L. J. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. 54, 581–592 (2022).

    Article  Google Scholar 

  53. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  Google Scholar 

  54. Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).

    Article  ADS  Google Scholar 

  55. Belsky, D. W. et al. Genetic analysis of social-class mobility in five longitudinal studies. Proc. Natl Acad. Sci. USA 115, E7275–E7284 (2018).

    Article  Google Scholar 

  56. Bates, T. C. et al. Social competence in parents increases children’s educational attainment: replicable genetically-mediated effects of parenting revealed by non-transmitted DNA. Twin Res. Hum. Genet. 22, 1–3 (2019).

    Article  Google Scholar 

  57. Willoughby, E. A., McGue, M., Iacono, W. G., Rustichini, A. & Lee, J. J. The role of parental genotype in predicting offspring years of education: evidence for genetic nurture. Mol. Psychiatry 26, 3896–3904 (2021).

    Article  Google Scholar 

  58. Loehlin, J. C., Horn, J. M. & Willerman, L. in Intelligence, Heredity, and Environment (eds Sternberg, R. J., & Grigorenko, E. L.) 105–125 (Cambridge Univ. Press, 1997).

  59. Cadoret, R. J. Adoption Studies. Alcohol Health Res. World 19, 195–200 (1995).

    Google Scholar 

  60. Rhea, S. A., Bricker, J. B., Corley, R. P., DeFries, J. C. & Wadsworth, S. J. Design utility and history of the Colorado Adoption Project: examples involving adjustment interactions. Adopt. Q. 16, 17–39 (2013).

    Article  Google Scholar 

  61. Willoughby, E. A., McGue, M., Iacono, W. G. & Lee, J. J. Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. Intelligence 88, 101579 (2021).

    Article  Google Scholar 

  62. Baker, M. Reproducibility crisis? Nature 533, 26 (2016).

    Google Scholar 

  63. Fanelli, D. Opinion: is science really facing a reproducibility crisis, and do we need it to? Proc. Natl Acad. Sci. USA 115, 2628–2631 (2018).

    Article  ADS  Google Scholar 

  64. Plomin, R., DeFries, J. C., Knopik, V. S. & Neiderhiser, J. M. Top 10 replicated findings from behavioral genetics. Perspect. Psychol. Sci. 11, 3–23 (2016).

    Article  Google Scholar 

  65. Chabris, C. F. et al. Most reported genetic associations with general intelligence are probably false positives. Psychol. Sci. 23, 1314–1323 (2012).

    Article  Google Scholar 

  66. Johnson, E. C. et al. No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol. Psychiatry 82, 702–708 (2017).

    Article  Google Scholar 

  67. Border, R. et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am. J. Psychiatry 176, 376–387 (2019).

    Article  Google Scholar 

  68. Lin, X. Learning lessons on reproducibility and replicability in large scale genome-wide association studies. Harvard Data Sci. Rev. https://doi.org/10.1162/99608f92.33703976 (2020).

    Article  Google Scholar 

  69. Røysamb, E., & Tambs, K. The beauty, logic and limitations of twin studies. Nor. Epidemiol. 26, 35–46 (2016).

    Google Scholar 

  70. van Dongen, J., Slagboom, P. E., Draisma, H. H., Martin, N. G. & Boomsma, D. I. The continuing value of twin studies in the omics era. Nat. Rev. Genet. 13, 640–653 (2012).

    Article  Google Scholar 

  71. Wilson, S. et al. Minnesota Center for Twin and Family Research. Twin Res. Hum. Genet. 22, 746–752 (2019).

    Article  Google Scholar 

  72. Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2017).

    Article  Google Scholar 

  73. Mills, M. C. & Rahal, C. A scientometric review of genome-wide association studies. Commun. Biol. 2, 1–11 (2019).

    Article  Google Scholar 

  74. George, S., Duran, N. & Norris, K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am. J. Public Health https://doi.org/10.2105/AJPH.2013.301706 (2014).

    Article  Google Scholar 

  75. Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    Article  Google Scholar 

  76. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    Article  Google Scholar 

  77. Giannakopoulou, O. et al. The genetic architecture of depression in individuals of East Asian ancestry: a genome-wide association study. JAMA Psychiatry 78, 1258–1269 (2021).

    Article  Google Scholar 

  78. Meng, X. et al. Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference. Preprint at bioRxiv https://doi.org/10.1101/2022.07.20.500802 (2022).

    Article  Google Scholar 

  79. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  Google Scholar 

  80. Bigdeli, T. B. et al. Genetic effects influencing risk for major depressive disorder in China and Europe. Transl. Psychiatry 7, e1074 (2017).

    Article  Google Scholar 

  81. Bigdeli, T. B. et al. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol. Psychiatry 25, 2455–2467 (2020).

    Article  Google Scholar 

  82. Hyttinen, V., Kaprio, J., Kinnunen, L., Koskenvuo, M. & Tuomilehto, J. Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes 52, 1052–1055 (2003).

    Article  Google Scholar 

  83. Snieder, H. et al. HbA1c levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins. Diabetes 50, 2858–2863 (2001).

    Article  Google Scholar 

  84. McAdams, T. A., Rijsdijk, F. V., Zavos, H. M. & Pingault, J. B. Twins and causal inference: leveraging nature’s experiment. Cold Spring Harb. Perspect. Med. 11, a039552 (2021).

    Article  Google Scholar 

  85. Alberg, A. J., Brock, M. V. & Samet, J. M. in Murray & Nadel’s Textbook of Respiratory Medicine 6th edn, Ch. 52, 927–939 (Saunders Elsevier, 2016).

  86. Poulton, R., Moffitt, T. E. & Silva, P. A. The Dunedin multidisciplinary health and development study: overview of the first 40 years, with an eye to the future. Soc. Psychiatry Psychiatr. Epidemiol., 50, 679–693 (2015).

    Article  Google Scholar 

  87. Krüger, O., Korsten, P. & Hoffman, J. I. in J. Call, G. M. Burghardt, I. M. Pepperberg, C. T. Snowdon, & T. Zentall (Eds.), APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior (eds Call, J., Burghardt, G. M., Pepperberg, I. M., Snowdon, C. T. & Zentall, T.) 365–379 (American Psychological Association, 2017).

  88. Turkheimer, E. & Harden, K. P. in Handbook of Research Methods in Social and Personality Psychology (eds Reis, H. T. & Judd, C. M.) 159–187 (Cambridge Univ. Press, 2014).

  89. Okbay, A. et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat. Genet. 54, 437–449 (2022).

    Article  Google Scholar 

  90. Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186190 (2017).

    Article  Google Scholar 

  91. Wainschtein, P. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).

    Article  Google Scholar 

  92. Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  Google Scholar 

  93. Gupta, S. et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 5, 5748 (2014).

    Article  ADS  Google Scholar 

  94. Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  Google Scholar 

  95. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).

    Article  Google Scholar 

  96. Neale, M. C. & Cardon, L. R. Methodology for Genetic Studies of Twins and Families. (Kluwer Academic/Plenum, 1992).

  97. Vitaro, F., Brendgen, M. & Arseneault, L. Methods and measures: the discordant MZ-twin method: one step closer to the holy grail of causality. Int. J. Behav. Dev. 33, 376–382 (2009).

    Article  Google Scholar 

  98. Keller, M. C., Medland, S. E. & Duncan, L. E. Are extended twin family designs worth the trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four twin family models. Behav. Genet. 40, 377–393 (2010).

    Article  Google Scholar 

  99. McAdams, T. A. et al. Revisiting the children-of-twins design: improving existing models for the exploration of intergenerational associations. Behav. Genet. 48, 397–412 (2018).

    Article  Google Scholar 

  100. Scarr, S. & Weinberg, R. A. The Minnesota Adoption Studies: genetic differences and malleability. Child. Dev. 54, 260–267 (1983).

    Article  Google Scholar 

  101. Murphy, K. et al. Twins Research Australia: a new paradigm for driving twin research. Twin Res. Hum. Genet. 22, 438–445 (2019).

    Article  Google Scholar 

  102. Otta, E. et al. The University of São Paulo Twin Panel: current status and prospects for Brazilian twin studies in behavioral research. Twin Res. Hum. Genet. 22, 467–474 (2019).

    Article  Google Scholar 

  103. Huang et al. The Chinese National Twin Registry: a unique data source for systems epidemiology of complex disease. Twin Res. Hum. Genet. 22, 482–485 (2019).

    Article  Google Scholar 

  104. Pedersen et al. The Danish Twin Registry: an updated overview. Twin Res. Hum. Genet. 22, 499–507 (2020).

    Article  Google Scholar 

  105. Bjerregaard-Andersen et al. The Guinea-Bissau Twin Registry update: a platform for studying twin mortality and metabolic disease. Twin Res. Hum. Genet. 22, 554–560 (2019).

    Article  Google Scholar 

  106. Gharipour et al. Isfahan Twins Registry (ITR): an invaluable platform for epidemiological and epigenetic studies: design and methodology of ITR. Twin Res. Hum. Genet. 22, 579–582 (2019).

    Article  Google Scholar 

  107. Ligthart et al. The Netherlands Twin Register: longitudinal research based on twin and twin-family designs. Twin Res. Hum. Genet. 22, 623–636 (2019).

    Article  Google Scholar 

  108. Rimfeld et al. Twins Early Development Study: a genetically sensitive investigation into behavioral and cognitive development from infancy to emerging adulthood. Twin Res. Hum. Genet. 22, 508–513 (2019).

    Article  Google Scholar 

  109. Pingault, J. B. et al. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19, 566–580 (2018).

    Article  Google Scholar 

  110. Lee, J. J. Correlation and causation in the study of personality. Eur. J. Personal. 26, 372–412 (2012).

    Article  Google Scholar 

  111. Vilhjámsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  Google Scholar 

  112. de Vlaming, R. & Groenen, P. J. F. The current and future use of ridge regression for prediction in quantitative genetics. BioMed. Res. Int. 2015, 1–18 (2015).

    Article  Google Scholar 

  113. Vattikuti, S., Lee, J. J., Chang, C. C., Hsu, S. D. H. & Chow, C. C. Applying compressed sensing to genome-wide association studies. GigaScience 3, 10 (2014).

    Article  Google Scholar 

  114. Lello, L. et al. Accurate genomic prediction of human height. Genetics 210, 477–497 (2018).

    Article  Google Scholar 

  115. Allegrini, A. G. et al. Genomic prediction of cognitive traits in childhood and adolescence. Mol. Psychiatry 24, 819–827 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

T.J.C.P. is supported by ZonMw grant 60-63600-98-834. Additionally, all of the authors thank the individuals who together provided incredibly constructive and useful feedback. All of these individuals contributed to the improvement of this Primer, and have their sincere gratitude for this.

Author information

Authors and Affiliations

  1. Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA

    Emily A. Willoughby

  2. Department of Clinical Developmental Psychology, Vrije Universiteit, Amsterdam, Netherlands

    Tinca J. C. Polderman

  3. Department of Child and Adolescent Psychiatry & Social Care, Amsterdam UMC, Amsterdam, Netherlands

    Tinca J. C. Polderman

  4. School of Applied Sciences, University of Mississippi, University, MS, USA

    Brian B. Boutwell

  5. John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA

    Brian B. Boutwell

Authors
  1. Emily A. Willoughby
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tinca J. C. Polderman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian B. Boutwell
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Introduction (B.B.B. and T.J.C.P.); Experimentation (E.A.W.); Results (E.A.W.); Applications (E.A.W.); Reproducibility and data deposition (T.J.C.P.); Limitations and optimizations (B.B.B. and T.J.C.P.); Outlook (E.A.W. and B.B.B.); Overview of the Primer (T.J.C.P. and B.B.B.).

Corresponding author

Correspondence to Brian B. Boutwell.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Methods Primers thanks Thalia Eley, Elliott Rees, Lawrence Wilkinson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Akaike’s information criteria

A statistical method for evaluating how well a model fits the given data in which fit is penalized for the number of parameters estimated. In twin family studies, this term is often used to compare different possible models to determine which has the best fit.

Bayesian information criteria

A statistical method for evaluating model fit in which a penalty term is introduced for the number of parameters. This is a more explanatory tool that assesses the underlying data.

Cascade model

An extension of the extended twin family design that models covariances of twin pairs in addition to their siblings, spouses, parents and children. The Cascade model relaxes the assumptions of assortative mating and vertical transmission by allowing for the inclusion of latent phenotypes.

Classical twin design

The simplest twin design in which pairs of monozygotic and dizygotic twins are compared for similarity on some phenotype, and the observed covariances are used to calculate the relative magnitudes of genetic and environmental sources of variance.

Dizygotic

(DZ). Describes a pair of twins derived from two different sperm and two different ova who develop together in utero. DZ twins share approximately 50% of their DNA, similar to regular siblings.

Equal environment assumption

The assumption that monozygotic and dizygotic twin pairs experience the same environmental factors.

Extended twin family design

A type of twin family design that models observed covariances using the relatives of twins in addition to the focal twin covariances for some variable.

Gene–environment correlations

(rGE). A phenomenon in which genes may influence individual variations in exposure to certain types of environment.

Gene–environment interaction

An interplay between genes and environments in which different genomes can cause individuals to respond differently to the same environmental exposure.

Monozygotic

(MZ). Describes a pair of twins derived from a single sperm and ovum, who are therefore genetically identical.

Missing heritability

The observation that the sum total of genetic variants from a genome-wide association study cannot completely explain the heritabilities of complex traits derived from twin family studies.

Nuclear twin family design

A type of twin family design that models observed covariances of the parents of twins in addition to the twins themselves.

Placement effects

The non-random placement of children based on traits that are similar between biological and adoptive families.

Polygenic score

(PGS). A number generated from genome-wide association data that summarizes the estimated effect of a large number of summed genetic variants on a phenotype of interest.

Stealth model

An extension of the extended twin family design that models covariances of twin pairs in addition to their siblings, spouses, parents and children. The Stealth model relies on primary phenotypic assortment to model assortative mating, and on direct parent to offspring phenotypic transmission to model vertical transmission.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Willoughby, E.A., Polderman, T.J.C. & Boutwell, B.B. Behavioural genetics methods. Nat Rev Methods Primers 3, 10 (2023). https://doi.org/10.1038/s43586-022-00191-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s43586-022-00191-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing