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Dissecting polygenic signals from genome-wide association studies on human behaviour

Nature Human Behaviour volume 5, pages 686–694 (2021)Cite this article

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Abstract

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.

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Fig. 1: The complexity of the associations between DNA sequence variants and human behaviour.
Fig. 2: Genetic correlations, SNP-based heritabilities and twin/family-based heritabilities.

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References

  1. Boomsma, D., Busjahn, A. & Peltonen, L. Classical twin studies and beyond. Nat. Rev. Genet. 3, 872–882 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. 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).

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

    Article  Google Scholar 

  4. Gusella, J. F. et al. A polymorphic DNA marker genetically linked to Huntington’s disease. Nature 306, 234–238 (1983).

    Article  CAS  PubMed  Google Scholar 

  5. Tsui, L.-C. et al. Cystic fibrosis locus defined by a genetically linked polymorphic DNA marker. Science 230, 1054–1057 (1985).

    Article  CAS  PubMed  Google Scholar 

  6. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    Article  CAS  PubMed  Google Scholar 

  7. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  PubMed  Google Scholar 

  8. Daly, M. J., Rioux, J. D., Schaffner, S. F., Hudson, T. J. & Lander, E. S. High-resolution haplotype structure in the human genome. Nat. Genet. 29, 229–232 (2001).

    Article  CAS  PubMed  Google Scholar 

  9. Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

    Article  CAS  PubMed  Google Scholar 

  10. The International HapMap Consortium The international HapMap project. Nature 426, 789–796 (2003).

    Article  CAS  Google Scholar 

  11. DeWan, A. et al. HTRA1 promoter polymorphism in wet age-related macular degeneration. Science 314, 989–992 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. The Wellcome Trust Case Control Consortium Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

    Article  PubMed Central  CAS  Google Scholar 

  13. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Duncan, L. E., Ostacher, M. & Ballon, J. How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete. Neuropsychopharmacology 44, 1518–1523 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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  PubMed  PubMed Central  Google Scholar 

  17. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ehret, G. B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Lango, H. A. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    Article  CAS  Google Scholar 

  21. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  24. Ripke, S. et al. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–976 (2011).

    Article  CAS  Google Scholar 

  25. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sklar, P. et al. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).

    Article  CAS  PubMed Central  Google Scholar 

  27. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ripke, S. et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Lo, M.-T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet. 49, 152–156 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Ganna, A. et al. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science 365, eaat7693 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu, M., Jiang, Y. & Wedow, R. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pasman, J. A. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat. Neurosci. 21, 1161–1170 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lee, J. J. et al. Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fisher, R. A. XV.—The correlation between relatives on the supposition of Mendelian inheritance. Earth Environ. Sci. Trans. R. Soc. Edinb. 52, 399–433 (1919).

    Article  Google Scholar 

  38. Hivert, V. et al. Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals. Am. J. Hum. Genet. https://doi.org/10.1016/j.ajhg.2021.02.014 (2021).

  39. Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Crow, J. F. On epistasis: why it is unimportant in polygenic directional selection. Philos. Trans. R. Soc. B 365, 1241–1244 (2010).

    Article  Google Scholar 

  41. Wainschtein, P. et al. Recovery of trait heritability from whole genome sequence data. Preprint at bioRxiv https://doi.org/10.1101/588020 (2019).

  42. Kaiser, J. ‘Landmark’ study resolves a major mystery of how genes govern human height. Science (3 November 2020).

  43. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ward, L. D. & Kellis, M. Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095–1106 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Emes, R. D. et al. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nat. Neurosci. 11, 799–806 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ip, H. F. et al. Characterizing the relation between expression QTLs and complex traits: exploring the role of tissue specificity. Behav. Genet. 48, 374–385 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Ikeda, M., Saito, T., Kanazawa, T. & Iwata, N. Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint. J. Hum. Genet. 66, 53–60 (2020).

  55. Wray, N. R. et al. From basic science to clinical application of polygenic risk scores: a primer. JAMA Psychiatry 78, 101–109 (2021).

    Article  PubMed  Google Scholar 

  56. The International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

  57. The Schizophrenia Working Group of the Psychiatric Genomics Consortium, Ripke, S., Walters, J. T. R. & O’Donovan, M. C. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. Preprint at medRxiv https://doi.org/10.1101/2020.09.12.20192922 (2020).

  58. Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lambert, S. A., Abraham, G. & Inouye, M. Towards clinical utility of polygenic risk scores. Hum. Mol. Genet. 28, R133–R142 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Maas, P. et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2, 1295–1302 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Sharp, S. A. & Rich, S. S. Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis. Diabetes Care 42, 200–207 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sparano, J. A. et al. Clinical and genomic risk to guide the use of adjuvant therapy for breast cancer. N. Engl. J. Med. 380, 2395–2405 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Abdellaoui, A. et al. Population structure, migration, and diversifying selection in the Netherlands. Eur. J. Hum. Genet. 21, 1277–1285 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kerminen, S. et al. Fine-scale genetic structure in Finland. G3 7, 3459–3468 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Leslie, S. et al. The fine-scale genetic structure of the British population. Nature 519, 309–314 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Cardon, L. R. & Palmer, L. J. Population stratification and spurious allelic association. Lancet 361, 598–604 (2003).

    Article  PubMed  Google Scholar 

  74. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Galton, F. Typical laws of heredity. III. Nature 15, 512–514 (1877).

    Article  Google Scholar 

  76. Galton, F. I. Co-relations and their measurement, chiefly from anthropometric data. Proc. R. Soc. Lond. 45, 135–145 (1889).

    Article  Google Scholar 

  77. Plana-Ripoll, O. et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 76, 259–270 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Momen, N. C. et al. Association between mental disorders and subsequent medical conditions. N. Engl. J. Med. 382, 1721–1731 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Polderman, T. J. et al. A genetic study on attention problems and academic skills: results of a longitudinal study in twins. J. Canadian Acad. Child Adolesc. Psychiatry 20, 22–34 (2011).

    Google Scholar 

  80. Cardno, A. G., Rijsdijk, F. V., Sham, P. C., Murray, R. M. & McGuffin, P. A twin study of genetic relationships between psychotic symptoms. Am. J. Psychiatry 159, 539–545 (2002).

    Article  PubMed  Google Scholar 

  81. Polderman, T., Hoekstra, R., Posthuma, D. & Larsson, H. The co-occurrence of autistic and ADHD dimensions in adults: an etiological study in 17 770 twins. Transl. Psychiatry 4, e435–e435 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Bartels, M. et al. The five factor model of personality and intelligence: a twin study on the relationship between the two constructs. Pers. Individ. Dif. 53, 368–373 (2012).

    Article  Google Scholar 

  83. Plomin, R. & DeFries, J. Multivariate behavioral genetic analysis of twin data on scholastic abilities. Behav. Genet. 9, 505–517 (1979).

    Article  CAS  PubMed  Google Scholar 

  84. Verweij, K. J., Huizink, A. C., Agrawal, A., Martin, N. G. & Lynskey, M. T. Is the relationship between early-onset cannabis use and educational attainment causal or due to common liability? Drug Alcohol Depend. 133, 580–586 (2013).

    Article  PubMed  Google Scholar 

  85. Zietsch, B., Verweij, K., Bailey, J., Wright, M. & Martin, N. Genetic and environmental influences on risky sexual behaviour and its relationship with personality. Behav. Genet. 40, 12–21 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  88. van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    Article  PubMed  CAS  Google Scholar 

  89. Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Verbanck, M., Chen, C.-y, Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Keller, M. C. et al. The genetic correlation between height and IQ: shared genes or assortative mating? PLoS Genet. 9, e1003451 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Hugh-Jones, D., Verweij, K. J. H., Pourcain, B. S. & Abdellaoui, A. Assortative mating on educational attainment leads to genetic spousal resemblance for polygenic scores. Intelligence 59, 103–108 (2016).

    Article  Google Scholar 

  93. Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 0016 (2017).

    Article  Google Scholar 

  94. Kemper, K. E. et al. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals. Nat. Commun. 12, 1050 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Baselmans, B. M. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    Article  CAS  PubMed  Google Scholar 

  97. Lee, P. H. et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

    Article  CAS  Google Scholar 

  98. Hill, W. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24, 169–181 (2019).

    Article  CAS  PubMed  Google Scholar 

  99. Peyrot, W. J. & Price, A. L. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat. Genet. 53, 445–454 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  PubMed  CAS  Google Scholar 

  101. Lee, P. H. et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

    Article  CAS  Google Scholar 

  102. Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Demange, P. A. et al. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat. Genet. 53, 35–44 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Marees, A. T. et al. Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01053-4 (2021).

  105. Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  106. Munafò, M. R. et al. Association between genetic variants on chromosome 15q25 locus and objective measures of tobacco exposure. J. Natl Cancer Inst. 104, 740–748 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Voight, B. F. et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380, 572–580 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Rossouw, J. E. et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA 288, 321–333 (2002).

    Article  CAS  PubMed  Google Scholar 

  109. Davey Smith, G. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    Article  Google Scholar 

  110. Abdellaoui, A. et al. Genetic correlates of social stratification in Great Britain. Nat. Hum. Behav. 3, 1332–1342 (2019).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  112. Demange, P. A. et al. Parental influences on offspring education: indirect genetic effects of non-cognitive skills. Preprint at bioRxiv https://doi.org/10.1101/2020.09.15.296236 (2020).

  113. Selzam, S. et al. Comparing within- and between-family polygenic score prediction. Am. J. Hum. Genet. 105, 351–363 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Cheesman, R. et al. Comparison of adopted and nonadopted individuals reveals gene–environment interplay for education in the UK Biobank. Psychol. Sci. 31, 582–591 (2020).

    Article  PubMed  Google Scholar 

  115. Howe, L. J. et al. Within-sibship GWAS improve estimates of direct genetic effects. Preprint at bioRxiv https://doi.org/10.1101/2021.03.05.433935 (2021).

  116. Hur, Y.-M. & Craig, J. M. Twin registries worldwide: an important resource for scientific research. Twin Res. Hum. Genet. 16, 1–12 (2013).

    Article  PubMed  Google Scholar 

  117. Hur, Y.-M. et al. Twin family registries worldwide: an important resource for scientific research. Twin Res. Hum. Genet. 22, 427–437 (2019).

    Article  PubMed  Google Scholar 

  118. Abdellaoui, A., Verweij, K. J. H. & Nivard, M. G. Geographic confounding in genome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2021.03.18.435971 (2021).

  119. Martin, J. et al. Association of genetic risk for schizophrenia with nonparticipation over time in a population-based cohort study. Am. J. Epidemiol. 183, 1149–1158 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2018).

    Article  PubMed  Google Scholar 

  121. Batty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J. & Bell, S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 368, m131 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Adams, M. J. et al. Factors associated with sharing e-mail information and mental health survey participation in large population cohorts. Int. J. Epidemiol. 49, 410–421 (2020).

    Article  PubMed  Google Scholar 

  124. Tyrrell, J. et al. Genetic predictors of participation in optional components of UK Biobank. Nat. Commun. 12, 886 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. https://doi.org/10.1038/s41588-021-00846-7 (2021).

  126. Xue, A. et al. Genome-wide analyses of behavioural traits biased by misreports and longitudinal changes. Nat. Commun. 12, 20211 (2021).

    Article  CAS  PubMed  Google Scholar 

  127. Mills, M. C. & Rahal, C. A scientometric review of genome-wide association studies. Commun. Biol. 2, 9 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Pedersen, C. B. et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

    Article  CAS  PubMed  Google Scholar 

  129. Stefansson, K. Letters from Iceland. Nat. Genet. 47, 425 (2015).

    Article  CAS  Google Scholar 

  130. Kerminen, S. et al. Geographic variation and bias in the polygenic scores of complex diseases and traits in Finland. Am. J. Hum. Genet. 104, 1169–1181 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Bulik-Sullivan, B. K., Loh, P. R., Finucane, H. K., Ripke, S. & Yang, J. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

A.A. and K.J.H.V. are supported by the Foundation Volksbond Rotterdam. A.A. is also supported by ZonMw grant no. 849200011 from The Netherlands Organisation for Health Research and Development.

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  1. Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

    Abdel Abdellaoui & Karin J. H. Verweij

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  1. Abdel Abdellaoui
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Correspondence to Abdel Abdellaoui.

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Supplementary Table 1: the numerical values of heritability estimates from Fig. 2, as well as the references for the twin/family studies and GWASs that Fig. 2 is based on.

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Abdellaoui, A., Verweij, K.J.H. Dissecting polygenic signals from genome-wide association studies on human behaviour. Nat Hum Behav 5, 686–694 (2021). https://doi.org/10.1038/s41562-021-01110-y

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