The new genetics of intelligence

Key Points

  • Until 2017, genome-wide polygenic scores derived from genome-wide association studies (GWAS) of intelligence were able to predict only 1% of the variance in intelligence in independent samples.

  • Polygenic scores derived from GWAS of intelligence can now predict 4% of the variance in intelligence.

  • More than 10% of the variance in intelligence can be predicted by multipolygenic scores derived from GWAS of both intelligence and years of education. This accounts for more than 20% of the 50% heritability of intelligence.

  • Polygenic scores are unique predictors in two ways. First, they predict psychological and behavioural outcomes just as well from birth as later in life. Second, polygenic scores are causal predictors in the sense that nothing in our brains, behaviour or environment can change the differences in DNA sequence that we inherited from our parents.

  • Polygenic scores for intelligence can bring the powerful construct of intelligence to any research in the life sciences without having to assess intelligence through the use of tests.

Abstract

Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.

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Figure 1: Variance explained by IQ GPSs and by EA GPSs in their target traits as a function of GWAS sample size.

References

  1. 1

    Gottfredson, L. S. Why g matters: The complexity of everyday life. Intelligence 24, 79–132 (1997).

    Google Scholar 

  2. 2

    Deary, I. J. et al. Genetic contributions to stability and change in intelligence from childhood to old age. Nature 482, 212–214 (2012).

    CAS  PubMed  Google Scholar 

  3. 3

    Deary, I. J., Strand, S., Smith, P. & Fernandes, C. Intelligence and educational achievement. Intelligence 35, 13–21 (2007).

    Google Scholar 

  4. 4

    Schmidt, F. L. & Hunter, J. General mental ability in the world of work: occupational attainment and job performance. J. Pers. Soc. Psychol. 86, 162–173 (2004).

    PubMed  Google Scholar 

  5. 5

    Strenze, T. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence 35, 401–426 (2007).

    Google Scholar 

  6. 6

    Calvin, C. M. et al. Childhood intelligence in relation to major causes of death in 68 year follow-up: prospective population study. Brit. Med. J. 357, 2708 (2017).

    Google Scholar 

  7. 7

    Deary, I. J., Pattie, A. & Starr, J. M. The stability of intelligence from age 11 to age 90 years: the Lothian birth cohort of 1921. Psychol. Sci. 24, 2361–2368 (2013).

    PubMed  Google Scholar 

  8. 8

    [No authors listed] Intelligence research should not be held back by its past. Nature 545, 385–386 (2017). This editorial is a landmark in the acceptance of genetic influence on intelligence, concluding, “it's well established and uncontroversial among geneticists that together, differences in genetics underwrite significant variation in intelligence between people.”

  9. 9

    Pinker, S. The Blank Slate: The Modern Denial of Human Nature (Penguin, 2003).

    Google Scholar 

  10. 10

    Block, N. J. & Dworkin, G. E. The IQ Controversy: Critical Readings (Pantheon, 1976).

    Google Scholar 

  11. 11

    Gould, S. J. The Mismeasure of Man (W.W. Norton, 1982).

    Google Scholar 

  12. 12

    Kamin, L. J. The Science and Politics of IQ (Routledge, 1974).

    Google Scholar 

  13. 13

    Bouchard, T. J. & McGue, M. Familial studies of intelligence: a review. Science 212, 1055–1059 (1981).

    PubMed  Google Scholar 

  14. 14

    Knopik, V. S., Neiderheiser, J., DeFries, J. C. & Plomin, R. Behavioral Genetics. 7th edn (Worth, 2017).

    Google Scholar 

  15. 15

    Haier, R. J. The Neuroscience of Intelligence (Cambridge Univ. Press, 2016).

    Google Scholar 

  16. 16

    Hare, B. Survival of the friendliest: Homo sapiens evolved via selection for prosociality. Annu. Rev. Psychol. 68, 155–186 (2017).

    PubMed  Google Scholar 

  17. 17

    Sternberg, R. J. & Kaufman, J. C. The Evolution of Intelligence (Psychology Press, 2013).

    Google Scholar 

  18. 18

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

    PubMed  PubMed Central  Google Scholar 

  19. 19

    Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014).

    CAS  PubMed  Google Scholar 

  20. 20

    Butcher, L. M., Davis, O. S., Craig, I. W. & Plomin, R. Genome-wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays. Genes Brain Behav. 7, 435–446 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Davies, G. et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N = 53 949). Mol. Psychiatry 20, 183–192 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112 151). Mol. Psychiatry 21, 758–767 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Plomin, R. et al. A genome-wide scan of 1842 DNA markers for allelic associations with general cognitive ability: a five-stage design using DNA pooling and extreme selected groups. Behav. Genet. 31, 497–509 (2001).

    CAS  PubMed  Google Scholar 

  25. 25

    Trampush, J. et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol. Psychiatry 22, 336 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Cesarini, D. & Visscher, P. M. Genetics and educational attainment. Sci. Learn. 2, 1–7 (2017).

    Google Scholar 

  27. 27

    Rietveld, C. A. et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc. Natl Acad. Sci. USA 111, 13790–13794 (2014). This study uses EA1 SNPs to predict intelligence, although less than 1% of the variance is predicted.

    CAS  PubMed  Google Scholar 

  28. 28

    Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013). This is the GWAS origin of EA1, which yields a GPS that predicts 1% of the variance in years of education.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Rietveld, C. A. et al. Replicability and robustness of genome-wide-association studies for behavioral traits. Psychol. Sci. 25, 1975–1986 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016). This is the GWAS origin of EA2 GPS, which increases the prediction of educational attainment from 1% to 3% of the variance.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Behavior Genetics Association 47th Annual Meeting Abstracts. Okbay, A. et al. GWAS of educational attainment – phase 3: main results [abstract]. Behav. Genet. 47, 699 (2017). This study refers to the largest GWAS of educational attainment ( n = 1,100,000), which increases the power of its GPS, EA3, to predict more than 10% of the variance in the targeted trait.

    Google Scholar 

  32. 32

    von Stumm, S. & Plomin, R. Socioeconomic status and the growth of intelligence from infancy through adolescence. Intelligence 48, 30–36 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. 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). This is the GWAS origin of IQ2 GPS, which increases the prediction of intelligence from 1% to 3%.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Savage, J. E. et al. GWAS meta-analysis (N = 279,930) identifies new genes and functional links to intelligence. Preprint at https://doi.org/10.1101/184853 (2017). This paper describes the largest GWAS of intelligence to date, which yields a GPS (IQ3) that predicts 4% of the variance in intelligence.

  35. 35

    Davies, G. et al. Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360). Preprint at https://doi.org/10.1101/176511 (2017).

  36. 36

    Krapohl, E. et al. Multi-polygenic score approach to trait prediction. Mol. Psychiatry https://doi.org/10.1038/mp.2017.163 (2017). This study employs a multiple-GPS approach and finds that 81 GPSs derived from well-powered GWAS predict 5% of the variance in intelligence.

    PubMed  PubMed Central  Google Scholar 

  37. 37

    Hill, W. D., Davies, G., McIntosh, A. M., Gale, C. R. & Deary, I. J. A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence. Preprint at https://doi.org/10.1101/160291 (2017). This study employs multiple-trait analysis of GWAS for intelligence and finds that educational attainment and income predict 7% of the variance in intelligence in an independent sample.

  38. 38

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Plomin, R. et al. Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychol. Sci. 24, 562–568 (2013).

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Plomin, R. Blueprint: How DNA Makes Us Who We Are (Allen Lane/Penguin, in the press). This book describes genetic research on behaviour from twin studies to the DNA revolution and its implications for science and society.

  42. 42

    Honzik, M. P., Macfarlane, J. W. & Allen, L. The stability of mental test performance between two and eighteen years. J. Exp. Educ. 17, 309–324 (1948).

    Google Scholar 

  43. 43

    Haworth, C. M. et al. A twin study of the genetics of high cognitive ability selected from 11,000 twin pairs in six studies from four countries. Behav. Genet. 39, 359–370 (2009).

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Plomin, R. & Deary, I. J. Genetics and intelligence differences: five special findings. Mol. Psychiatry 20, 98–108 (2015). This article highlights five genetic findings that are special to intelligence differences, including one not mentioned in this Review — assortative mating is much greater for intelligence than for other traits.

    CAS  PubMed  Google Scholar 

  45. 45

    Briley, D. A. & Tucker-Drob, E. M. Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies. Psychol. Sci. 24, 1704–1713 (2013).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Selzam, S. et al. Predicting educational achievement from DNA. Mol. Psychiatry 22, 267–272 (2017). This study shows that EA2 predicts 9% of the variance in tested educational achievement at age 16, which was the strongest GPS prediction of a behavioural trait at that time.

    CAS  PubMed  Google Scholar 

  47. 47

    Plomin, R. & Kovas, Y. Generalist genes and learning disabilities. Psychol. Bull. 131, 592–617 (2005).

    PubMed  Google Scholar 

  48. 48

    Selzam, S. et al. Genome-wide polygenic scores predict reading performance throughout the school years. Sci. Stud. Read. 21, 334–349 (2017).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Carrion-Castillo, A. et al. Evaluation of results from genome-wide studies of language and reading in a novel independent dataset. Genes Brain Behav. 15, 531–541 (2016).

    CAS  PubMed  Google Scholar 

  50. 50

    Krapohl, E. et al. Phenome-wide analysis of genome-wide polygenic scores. Mol. Psychiatry 21, 1188–1193 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. 51

    Marioni, R. E. et al. Common genetic variants explain the majority of the correlation between height and intelligence: the generation Scotland study. Behav. Genet. 44, 91–96 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. 52

    Williams, K. M. et al. Phenotypic and genotypic correlation between myopia and intelligence. Sci. Rep. 7, 45977 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Hill, W. D. et al. Age-dependent pleiotropy between general cognitive function and major psychiatric disorders. Biol. Psychiatry 80, 266–273 (2016).

    PubMed  PubMed Central  Google Scholar 

  54. 54

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

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Plomin, R., Haworth, C. M. & Davis, O. S. Common disorders are quantitative traits. Nat. Rev. Genet. 10, 872–878 (2009).

    CAS  PubMed  Google Scholar 

  56. 56

    Spain, S. L. et al. A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence. Mol. Psychiatry 21, 1145–1151 (2016).

    CAS  PubMed  Google Scholar 

  57. 57

    Zabaneh, D. et al. A genome-wide association study for extremely high intelligence. Mol. Psychiatry https://doi.org/10.1038/mp.2017.121 (2017). This GWAS of intelligence uses a novel strategy to increase power — a case–control design in which the subjects were individuals with extremely high IQ from the top 0.0003 of the population (mean IQ of 170).

    PubMed  PubMed Central  Google Scholar 

  58. 58

    Reichenberg, A. et al. Discontinuity in the genetic and environmental causes of the intellectual disability spectrum. Proc. Natl Acad. Sci. USA 113, 1098–1103 (2016).

    CAS  PubMed  Google Scholar 

  59. 59

    Vissers, L. E., Gilissen, C. & Veltman, J. A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9–18 (2016).

    CAS  PubMed  Google Scholar 

  60. 60

    Plomin, R. & Daniels, D. Why are children in the same family so different from one another? Behav. Brain Sci. 10, 1–16 (1987).

    Google Scholar 

  61. 61

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

    PubMed  Google Scholar 

  62. 62

    Hanscombe, K. B. et al. Socioeconomic status (SES) and children's intelligence (IQ): in a UK-representative sample SES moderates the environmental, not genetic, effect on IQ. PLOS ONE 7, e30320 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Plomin, R. & Bergeman, C. S. The nature of nurture: genetic influence on “environmental” measures. Behav. Brain Sci. 14, 373–386 (1991).

    Google Scholar 

  64. 64

    Belsky, D. W. et al. The genetics of success. Psychol. Sci. 27, 957–972 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65

    Krapohl, E. et al. Widespread covariation of early environmental exposures and trait-associated polygenic variation. Proc. Natl Acad. Sci. USA 114, 11727–11732 (2017).

    CAS  PubMed  Google Scholar 

  66. 66

    Smith-Woolley, E. et al. Differences in exam performance between pupils attending different school types mirror the genetic differences between them. NPJ Sci. Learn. (in the press).

  67. 67

    Ayorech, Z., Krapohl, E., Plomin, R. & von Stumm, S. Genetic influence on intergenerational educational attainment. Psychol. Sci. 28, 1302–1310 (2017). This paper describes both twin analyses and EA2 GPSs that show genetic influence on intergenerational EA.

    PubMed  PubMed Central  Google Scholar 

  68. 68

    Behavior Genetics Association 46th Annual Meeting Abstracts. Rimfeld, K., Trzaskowski, M., Esko, T., Metspalu, A. & Plomin, R. Genetic influence on educational attainment and occupational status during and after the Soviet era in Estonia [abstract]. Behav. Genet. 46, 803 (2016).

    Google Scholar 

  69. 69

    Plomin, R. & DeFries, J. C. Genetics and intelligence: recent data. Intelligence 4, 15–24 (1980).

    Google Scholar 

  70. 70

    McEwen, J. E. et al. The ethical, legal, and social implications program of the National Human Genome Research Institute: reflections on an ongoing experiment. Annu. Rev. Genom. Hum. Genet. 15, 481–504 (2014).

    CAS  Google Scholar 

  71. 71

    Bouregy, S., Grigorenko, E. L., Latham, S. R. & Tan, M. Genetics, Ethics and Education (Cambridge Univ. Press, 2017).

    Google Scholar 

  72. 72

    Conley, D. & Fletcher, J. The Genome Factor: What the Social Genomics Revolution Reveals about Ourselves, our History, and the Future (Princeton Univ. Press, 2017).

    Google Scholar 

  73. 73

    Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, 1977).

    Google Scholar 

  74. 74

    Gottfredson, L. S. Mainstream science on intelligence. Wall Street Journal (13 December 1994).

  75. 75

    Carroll, J. B. Human Cognitive Abilities: A Survey of Factor-Analytic Studies (Cambridge Univ. Press, 1993).

    Google Scholar 

  76. 76

    Spearman, C. 'General Intelligence' objectively determined and measured. Am. J. Psychol. 15, 201–292 (1904).

    Google Scholar 

  77. 77

    Jensen, A. R. The g Factor: The Science of Mental Ability (Praeger, 1998).

    Google Scholar 

  78. 78

    Deary, I. J. Intelligence. Annu. Rev. Psychol. 63, 453–482 (2012). This article is an authoritative overview of intelligence research.

    PubMed  Google Scholar 

  79. 79

    Gow, A. J. et al. Stability and change in intelligence from age 11 to ages 70, 79, and 87: the Lothian Birth Cohorts of 1921 and 1936. Psychol. Ageing 26, 232–240 (2011).

    Google Scholar 

  80. 80

    Schaie, K. W. Developmental Influences on Adult Intelligence: The Seattle Longitudinal Study (Oxford Univ. Press, 2005).

    Google Scholar 

  81. 81

    Brinch, C. N. & Galloway, T. A. Schooling in adolescence raises IQ scores. Proc. Natl Acad. Sci. USA 109, 425–430 (2012).

    CAS  PubMed  Google Scholar 

  82. 82

    Protzko, J. Does the raising IQ–raising g distinction explain the fadeout effect? Intelligence 56, 65–71 (2016).

    Google Scholar 

  83. 83

    Duyme, M., Dumaret, A.-C. & Tomkiewicz, S. How can we boost IQs of “dull children”?: a late adoption study. Proc. Natl Acad. Sci. USA 96, 8790–8794 (1999).

    CAS  PubMed  Google Scholar 

  84. 84

    Melby-Lervåg, M. & Hulme, C. Is working memory training effective? A meta-analytic review. Dev. Psychol. 49, 270–291 (2013).

    PubMed  Google Scholar 

  85. 85

    Puma, M. et al. Head Start Impact Study Final Report. Administration for Children and Families https://www.acf.hhs.gov/sites/default/files/opre/hs_impact_study_final.pdf (2010).

  86. 86

    Plomin, R. & Simpson, M. A. The future of genomics for developmentalists. Dev. Psychopathol. 25, 1263–1278 (2013).

    PubMed  PubMed Central  Google Scholar 

  87. 87

    Pasaniuc, B. & Price, A. L. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet. 18, 117–127 (2017).

    CAS  PubMed  Google Scholar 

  88. 88

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

    PubMed  PubMed Central  Google Scholar 

  89. 89

    Euseden, J. et al. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    Google Scholar 

  90. 90

    Hill, W. D. et al. Molecular genetic contributions to social deprivation and household income in UK Biobank. Curr. Biol. 26, 3083–3089 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91

    Turley, P. et al. MTAG: Multi-Trait Analysis of GWAS. Preprint at https://doi.org/10.1101/118810 (2017).

  92. 92

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    CAS  PubMed  Google Scholar 

  93. 93

    Yang, J. et al. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

    CAS  PubMed  Google Scholar 

  94. 94

    Sullivan, P. F. et al. Psychiatric genomics: an update and an agenda. Am. J. Psychol. https://doi.org/10.1176/appi.ajp.2017.17030283 (2017).

    PubMed  Google Scholar 

  95. 95

    Bacanu, S. A. Sharing extended summary data from contemporary genetic studies is unlikely to threaten subject privacy. PLOS ONE 12, e0179504 (2017).

    PubMed  PubMed Central  Google Scholar 

  96. 96

    Calvin, C. M. et al. Multivariate genetic analyses of cognition and academic achievement from two population samples of 174,000 and 166,000 school children. Behav. Genet. 42, 699–710 (2012).

    PubMed  Google Scholar 

  97. 97

    Marioni, R. E. et al. Molecular genetic contributions to socioeconomic status and intelligence. Intelligence 44, 26–32 (2014).

    PubMed  PubMed Central  Google Scholar 

  98. 98

    Branigan, A. R., McCallum, K. J. & Freese, J. Variation in the heritability of educational attainment: An international meta-analysis. Soc. Forces 92, 109–140 (2013).

    Google Scholar 

  99. 99

    Krapohl, E. et al. The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proc. Natl Acad. Sci. USA 111, 15273–15278 (2014).

    CAS  PubMed  Google Scholar 

  100. 100

    Haworth, C. M., Davis, O. S. & Plomin, R. Twins Early Development Study (TEDS): a genetically sensitive investigation of cognitive and behavioral development from childhood to young adulthood. Twin Res. Hum. Genet. 16, 117–125 (2013).

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families. TEDS is supported by a programme grant to R.P. from the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The research reported here has also received funding from the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement 602768 and ERC grant agreement 295366. R.P. is also supported by a Medical Research Council Professorship award (G19/2). S.v.S. is supported by a Jacobs Foundation Research Fellowship award (2017–2019).

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The authors contributed equally to all aspects of the manuscript.

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Correspondence to Robert Plomin or Sophie von Stumm.

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Glossary

Twin studies

Studies comparing the resemblance of identical and fraternal twins to estimate genetic and environmental components of variance.

Variance

An index of how spread out scores are in a study population, which is calculated as the average of the squared deviations from the mean.

Genome-wide association studies

(GWAS). Studies that aim to identify loci throughout the genome associated with an observed trait or disorder.

Heritability

The proportion of observed differences among individuals that can be attributed to inherited differences in genome sequence.

Genome-wide polygenic scores

(GPSs). Genetic indices of a trait for each individual that are the sum across the genome of thousands of single-nucleotide polymorphisms (SNPs) of the individual's increasing alleles associated with the trait, usually weighted by the effect size of each SNP's association with the trait in genome-wide association studies.

Candidate gene studies

Studies that focus on genes for which the function suggests that they are associated with a trait, in contrast to genome-wide association studies.

Effect sizes

Proportions of variance of traits in the study population accounted for by a particular factor such as a genome-wide polygenic score.

Single-nucleotide polymorphisms

(SNPs). Single base pair differences in inherited DNA sequence between individuals.

Linkage disequilibrium (LD) score regression analysis

Analysis that, for each single-nucleotide polymorphism in a genome-wide association study (GWAS), regresses χ2 statistics from GWAS summary statistics against LD scores.

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Plomin, R., von Stumm, S. The new genetics of intelligence. Nat Rev Genet 19, 148–159 (2018). https://doi.org/10.1038/nrg.2017.104

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