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Cognitive Genomics: Recent Advances and Current Challenges

  • Genetic Disorders (F Goes, Section Editor)
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Abstract

Purpose of Review

We review recent progress in uncovering the complex genetic architecture of cognition, arising primarily from genome-wide association studies (GWAS). We explore the genetic correlations between cognitive performance and neuropsychiatric disorders, the genetic and environmental factors associated with age-related cognitive decline, and speculate about the future role of genomics in the understanding of cognitive processes.

Recent Findings

Improvements in genomic methods, and the increasing availability of large datasets via consortia cooperation, have led to a greater understanding of the role played by common and rare variants in the genomics of cognition, the highly polygenic basis of cognitive function and dysfunction, and the multiple biological processes involved.

Summary

Recent research has aided in our understanding of the complex biological nature of genomics of cognition. Further development of data banks and techniques to analyze this data hold significant promise for understanding cognitive ability, and for treating cognitively related disability.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Plomin R, Deary IJ. Genetics and intelligence differences: five special findings. Mol Psychiatry. 2014;20:98. https://doi.org/10.1038/mp.2014.105.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Plomin R, Spinath F. Intelligence: genetics, genes, and genomics. J Pers Soc Psychol. 2004;86:112–29. https://doi.org/10.1037/0022-3514.86.1.112.

    Article  PubMed  Google Scholar 

  3. Plomin R, DeFries JC, McClearn GE, McGuffin P. Behavioral genetics. 4th ed. New York: Worth Publishers; 2001.

    Google Scholar 

  4. Briley DA, Tucker-Drob EM. Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies. Psychol Sci. 2013;24(9):1704–13. https://doi.org/10.1177/0956797613478618.

    Article  PubMed  Google Scholar 

  5. Ramus F. Genes, brain, and cognition: a roadmap for the cognitive scientist. Cognition. 2006;101(2):247–69. https://doi.org/10.1016/j.cognition.2006.04.003.

    Article  PubMed  Google Scholar 

  6. • Plomin R, von Stumm S. The new genetics of intelligence. Nat Rev Genet. 2018;19:148. https://doi.org/10.1038/nrg.2017.104 This recent review article discusses the benefits of polygenic scores in intelligence research.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. •• Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50(8):1112–21. https://doi.org/10.1038/s41588-018-0147-3 This study is the largest GWAS study to date on educational attainment using both public and commerically available data and highlights the role of genes involved in the prenatal brain as well post natal development.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. • Lam M, Hill WD, Trampush JW, Yu J, Knowles E, Davies G, et al. Pleiotropic meta-analysis of cognition, education, and schizophrenia differentiates roles of early neurodevelopmental and adult synaptic pathways. bioRxiv. 2019;105:334. https://doi.org/10.1101/519967 This paper examines the pleiotropic nature of GWAS findings on intelligence and psychiatric disorders.

    Article  CAS  Google Scholar 

  9. Deary IJ, Penke L, Johnson W. The neuroscience of human intelligence differences. Nat Rev Neurosci. 2010;11:201–11. https://doi.org/10.1038/nrn2793.

    Article  CAS  PubMed  Google Scholar 

  10. Johnson W, Nijenhuis J, Bouchard TJ. Still just 1 g: consistent results from five test batteries. Intelligence. 2008;36(1):81–95. https://doi.org/10.1016/j.intell.2007.06.001.

    Article  Google Scholar 

  11. Warne RT, Burningham C. Spearman’s g found in 31 non-Western nations: strong evidence that g is a universal phenomenon. Psychol Bull. 2019;145(3):237–72. https://doi.org/10.1037/bul0000184.

    Article  PubMed  Google Scholar 

  12. Hedge C, Powell G, Sumner P. The reliability paradox: why robust cognitive tasks do not produce reliable individual differences. Behav Res Methods. 2018;50(3):1166–86. https://doi.org/10.3758/s13428-017-0935-1.

    Article  PubMed  Google Scholar 

  13. De Schryver M, Hughes S, Rosseel Y, De Houwer J. Unreliable yet still replicable: a comment on LeBel and Paunonen (2011). Front Psychol. 2016;6(2039). https://doi.org/10.3389/fpsyg.2015.02039.

  14. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533(7604):539–42. https://doi.org/10.1038/nature17671.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fawns-Ritchie C, Deary IJ. Reliability and validity of the UK Biobank cognitive tests. medRxiv. 2019;19002204. https://doi.org/10.1101/19002204.

  16. Deary IJ, Strand S, Smith P, Fernandes C. Intelligence and educational achievement. Intelligence. 2007;35(1):13–21. https://doi.org/10.1016/j.intell.2006.02.001.

    Article  Google Scholar 

  17. Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49(7):1107–12. https://doi.org/10.1038/ng.3869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Trampush JW, Yang ML, Yu J, Knowles E, Davies G, Liewald DC, et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol Psychiatry. 2017;22(3):336–45. https://doi.org/10.1038/mp.2016.244.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davies G, Armstrong N, Bis JC, Bressler J, Chouraki V, Giddaluru S, et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949). Mol Psychiatry. 2015;20(2):183–92. https://doi.org/10.1038/mp.2014.188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hill WD, Davies G, Harris SE, Hagenaars SP, Liewald DC, Penke L, et al. Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions. Transl Psychiatry. 2016;6(12):e980. https://doi.org/10.1038/tp.2016.246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Davies G, Marioni RE, Liewald DC, Hill WD, Hagenaars SP, Harris SE, et al. Genome-wide association study of cognitive functions and educational attainment in UK biobank (N=112 151). Mol Psychiatry. 2016;21(6):758–67. https://doi.org/10.1038/mp.2016.45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. •• Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun. 2018;9(1):2098. https://doi.org/10.1038/s41467-018-04362-x This study demonstrates the power of sample size to GWAS findings by the addition of the UK Biobank data. It confirms previous findings and identifies new loci associated with neuronial communication.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. •• Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50(7):912–9. https://doi.org/10.1038/s41588-018-0152-6 Again using the UK Biobank and other data, this study had identified the largest number of association snps for IQ to date, and reports a bio-informatic analysis of these findings.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Eriksson N, Macpherson JM, Tung JY, Hon LS, Naughton B, Saxonov S, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 2010;6(6):1–20. https://doi.org/10.1371/journal.pgen.1000993.

    Article  CAS  Google Scholar 

  26. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50(2):229–37. https://doi.org/10.1038/s41588-017-0009-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. • Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, AM MI, et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 2018. https://doi.org/10.1038/s41380-017-0001-5 Hill et al. demonstrate the additive effects of multi-trait anlaysis and its utility to generate further findings.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lee C, Scherer SW. The clinical context of copy number variation in the human genome. Expert Rev Mol Med. 2010;12:e8. https://doi.org/10.1017/S1462399410001390.

    Article  CAS  PubMed  Google Scholar 

  29. Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet. 2006;7(2):85–97. https://doi.org/10.1038/nrg1767.

    Article  CAS  PubMed  Google Scholar 

  30. Kendall KM, Rees E, Escott-Price V, Einon M, Thomas R, Hewitt J, et al. Cognitive performance among carriers of pathogenic copy number variants: analysis of 152,000 UK Biobank subjects. Biol Psychiatry. 2017;82(2):103–10. https://doi.org/10.1016/j.biopsych.2016.08.014.

    Article  PubMed  Google Scholar 

  31. Nowakowska B. Clinical interpretation of copy number variants in the human genome. J Appl Genet. 2017;58(4):449–57. https://doi.org/10.1007/s13353-017-0407-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Huddleston J, Chaisson MJP, Steinberg KM, Warren W, Hoekzema K, Gordon D, et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 2017;27(5):677–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chiang C, Scott AJ, Davis JR, Tsang EK, Li X, Kim Y, et al. The impact of structural variation on human gene expression. Nat Genet. 2017;49:692–9. https://doi.org/10.1038/ng.3834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. • Huguet G, Schramm C, Douard E, Jiang L, Labbe A, Tihy F, et al. Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry. 2018;75(5):447–57. https://doi.org/10.1001/jamapsychiatry.2018.0039 This study presents a framework for examining the effects of copy number variants on general cognition.

    Article  PubMed  PubMed Central  Google Scholar 

  35. • Stefansson H, Meyer-Lindenberg A, Steinberg S, Magnusdottir B, Morgen K, Arnarsdottir S, et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature. 2013;505:361. https://doi.org/10.1038/nature12818 The study of population isolates can highlight the role of CNVs in cognition.

    Article  CAS  PubMed  Google Scholar 

  36. Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Büchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39. https://doi.org/10.1038/mp.2010.4.

    Article  CAS  PubMed  Google Scholar 

  37. Pausova Z, Paus T, Abrahamowicz M, Bernard M, Gaudet D, Leonard G, et al. Cohort profile: the Saguenay Youth Study (SYS). Int J Epidemiol. 2017;46(2):e19. https://doi.org/10.1093/ije/dyw023.

    Article  PubMed  Google Scholar 

  38. Kendall KM, Bracher-Smith M, Fitzpatrick H, Lynham A, Rees E, Escott-Price V, et al. Cognitive performance and functional outcomes of carriers of pathogenic copy number variants: analysis of the UK Biobank. Br J Psychiatry. 2019;214(5):297–304. https://doi.org/10.1192/bjp.2018.301.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Warland A, Kendall KM, Rees E, Kirov G, Caseras X. Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank. Mol Psychiatry. 2019:1–9. https://doi.org/10.1038/s41380-019-0355-y.

  40. •• Ganna A, Genovese G, Howrigan DP, Byrnes A, Kurki MI, Zekavat SM et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat Neurosci. 2016;19:1563. doi:https://doi.org/10.1038/nn.4404 Important paper highlighting the role of rare mutations in intelligence using EA as a proxy measure.

  41. • Hill WD, Arslan RC, Xia C, Luciano M, Amador C, Navarro P, et al. Genomic analysis of family data reveals additional genetic effects on intelligence and personality. Mol Psychiatry. 2018;23(12):2347–62. https://doi.org/10.1038/s41380-017-0005-1 This study shows how using family based data can resolve some of the missing hertiability in cognition.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Smith BH, Campbell H, Blackwood D, Connell J, Connor M, Deary IJ, et al. Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med Genet. 2006;7(1):74. https://doi.org/10.1186/1471-2350-7-74.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Zaitlen N, Kraft P, Patterson N, Pasaniuc B, Bhatia G, Pollack S, et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 2013;9(5):e1003520. https://doi.org/10.1371/journal.pgen.1003520.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Xia C, Amador C, Huffman J, Trochet H, Campbell A, Porteous D, et al. Pedigree- and SNP-associated genetics and recent environment are the major contributors to anthropometric and cardiometabolic trait variation. PLoS Genet. 2016;12(2):e1005804. https://doi.org/10.1371/journal.pgen.1005804.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Shafee R, Nanda P, Padmanabhan JL, Tandon N, Alliey-Rodriguez N, Kalapurakkel S, et al. Polygenic risk for schizophrenia and measured domains of cognition in individuals with psychosis and controls. Transl Psychiatry. 2018;8(1):78. https://doi.org/10.1038/s41398-018-0124-8.

    Article  PubMed  PubMed Central  Google Scholar 

  46. • Hasan A, Afzal M. Gene and environment interplay in cognition: Evidence from twin and molecular studies, future directions and suggestions for effective candidate gene x environment (cGxE) research. Mult Scler Relat Disord. 2019;33:121–30. https://doi.org/10.1016/j.msard.2019.05.005 This article reviews the interplay between the enviroment and cognition and explores the need to examine these effects in future studies.

    Article  PubMed  Google Scholar 

  47. Cheesman R, Hunjan A, Coleman JRI, Ahmadzadeh Y, Plomin R, McAdams TA, et al. Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank. bioRxiv. 2019:707695. https://doi.org/10.1101/707695.

  48. Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE, et al. The nature of nurture: effects of parental genotypes. Science. 2018;359(6374):424–8. https://doi.org/10.1126/science.aan6877.

    Article  CAS  PubMed  Google Scholar 

  49. Selzam S, Ritchie SJ, Pingault J-B, Reynolds CA, O’Reilly PF, Plomin R. Comparing within- and between-family polygenic score prediction. bioRxiv. 2019:605006. https://doi.org/10.1101/605006.

  50. Toulopoulou T, Goldberg T, Rebollo Mesa I, Picchioni M, Rijsdijk F, Stahl D, et al. Impaired intellect and memory a missing link between genetic risk and schizophrenia? Arch Gen Psychiatry. 2010;67:905–13. https://doi.org/10.1001/archgenpsychiatry.2010.99.

    Article  PubMed  Google Scholar 

  51. Fowler D, Hodgekins J, Garety P, Freeman D, Kuipers E, Dunn G, et al. Negative cognition, depressed mood, and paranoia: a longitudinal pathway analysis using structural equation modeling. Schizophr Bull. 2012;38(5):1063–73. https://doi.org/10.1093/schbul/sbr019.

    Article  PubMed  Google Scholar 

  52. Blokland GAM, Del Re EC, Mesholam-Gately RI, Jovicich J, Trampush JW, Keshavan MS, et al. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: a collaborative cognitive and neuroimaging genetics project. Schizophr Res. 2018;195:306–17. https://doi.org/10.1016/j.schres.2017.09.024.

    Article  PubMed  Google Scholar 

  53. Lencz T, Knowles E, Davies G, Guha S, Liewald DC, Starr JM, et al. Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT). Mol Psychiatry. 2014;19(2):168–74. https://doi.org/10.1038/mp.2013.166.

    Article  CAS  PubMed  Google Scholar 

  54. Hubbard L, Tansey KE, Rai D, Jones P, Ripke S, Chambert KD, et al. Evidence of common genetic overlap between schizophrenia and cognition. Schizophr Bull. 2015;42(3):832–42. https://doi.org/10.1093/schbul/sbv168.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Hagenaars SP, Harris SE, Davies G, Hill WD, Liewald DC, Ritchie SJ, et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol Psychiatry. 2016;21(11):1624–32. https://doi.org/10.1038/mp.2015.225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Fanous AH, Zhou B, Aggen SH, Bergen SE, Amdur RL, Duan J, et al. Genome-wide association study of clinical dimensions of schizophrenia: polygenic effect on disorganized symptoms. Am J Psychiatry. 2012;169(12):1309–17. https://doi.org/10.1176/appi.ajp.2012.12020218.

    Article  PubMed  PubMed Central  Google Scholar 

  57. •• Richards AL, Pardiñas AF, Frizzati A, Tansey KE, Lynham AJ, Holmans P, et al. The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia. Schizophr Bull. 2019. https://doi.org/10.1093/schbul/sbz061 This study provides a current estimate of the genetic correlation between cognitive function and schizophrenia susceptibility.

  58. Green MF. Impact of cognitive and social cognitive impairment on functional outcomes in patients with schizophrenia. J Clin Psychiatry. 2016;77(Suppl 2):8–11. https://doi.org/10.4088/jcp.14074su1c.02.

    Article  PubMed  Google Scholar 

  59. World Health O. World report on ageing and health. Geneva: World Health Organization; 2015.

    Google Scholar 

  60. Andrews SJ, Das D, Cherbuin N, Anstey KJ, Easteal S. Association of genetic risk factors with cognitive decline: the PATH through life project. Neurobiol Aging. 2016;41:150–8. https://doi.org/10.1016/j.neurobiolaging.2016.02.016.

    Article  CAS  PubMed  Google Scholar 

  61. Tucker-Drob EM, Brandmaier AM, Lindenberger U. Coupled cognitive changes in adulthood: a meta-analysis. Psychol Bull. 2019. https://doi.org/10.1037/bul0000179.

    Article  PubMed  PubMed Central  Google Scholar 

  62. •• Cabeza R, Albert M, Belleville S, FIM C, Duarte A, Grady CL, et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat Rev Neurosci. 2018;19(11):701–10. https://doi.org/10.1038/s41583-018-0068-2 This article presents a clear theortitical model to explain the factors involved in cognitive decline.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2018. https://doi.org/10.1016/j.jalz.2018.07.219.

  64. • Ritchie SJ, Hill WD, Marioni RE, Davies G, Hagenaars SP, Harris SE, et al. Polygenic predictors of age-related decline in cognitive ability. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-019-0372-x This study explores the use of PGS in understanding the genetics of cognitive decline and highlights the need for further work.

  65. Deary IJ, Yang J, Davies G, Harris SE, Tenesa A, Liewald D, et al. Genetic contributions to stability and change in intelligence from childhood to old age. Nature. 2012;482(7384):212–5. https://doi.org/10.1038/nature10781.

    Article  CAS  PubMed  Google Scholar 

  66. Plassman BL, Williams JW Jr, Burke JR, Holsinger T, Benjamin S. Systematic review: factors associated with risk for and possible prevention of cognitive decline in later life. Ann Intern Med. 2010;153(3):182–93. https://doi.org/10.7326/0003-4819-153-3-201008030-00258.

    Article  PubMed  Google Scholar 

  67. •• Boldrini M, Fulmore CA, Tartt AN, Simeon LR, Pavlova I, Poposka V, et al. Human hippocampal neurogenesis persists throughout aging. Cell Stem Cell. 2018;22(4):589–99.e5. https://doi.org/10.1016/j.stem.2018.03.015 This research explores new frontiers in the understanding of cognitive variance in aging and proposes novel concepts involving neurogenesis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Van Hout CV, Tachmazidou I, Backman JD, Hoffman JX, Ye B, Pandey AK, et al. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. bioRxiv. 2019;572347. https://doi.org/10.1101/572347.

  69. • Eichler EE. Genetic variation, comparative genomics, and the diagnosis of disease. N Engl J Med. 2019;381(1):64–74. https://doi.org/10.1056/NEJMra1809315 Very good review of the challenges facing genetic studies into the future.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48(10):1279–83. https://doi.org/10.1038/ng.3643.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. McClellan JM, Lehner T, King M-C. Gene discovery for complex traits: lessons from Africa. Cell. 2017;171(2):261–4. https://doi.org/10.1016/j.cell.2017.09.037.

    Article  CAS  PubMed  Google Scholar 

  72. Rietveld CA, Esko T, Davies G, Pers TH, Turley P, Benyamin B, et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc Natl Acad Sci. 2014;111(38):13790–4. https://doi.org/10.1073/pnas.1404623111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75. https://doi.org/10.1038/s41588-018-0269-7.

    Article  CAS  PubMed  Google Scholar 

  74. Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431–44. https://doi.org/10.1038/s41588-019-0344-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ruderfer DM, Ripke S, McQuillin A, Boocock J, Stahl EA, Pavlides JMW et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173(7):1705–15.e16. doi: https://doi.org/10.1016/j.cell.2018.05.046.

    Article  PubMed Central  Google Scholar 

  76. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50(5):668–81. https://doi.org/10.1038/s41588-018-0090-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50(3):381–9. https://doi.org/10.1038/s41588-018-0059-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339–48. https://doi.org/10.1038/s41588-019-0481-0.

    Article  CAS  PubMed  Google Scholar 

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Fitzgerald, J., Morris, D.W. & Donohoe, G. Cognitive Genomics: Recent Advances and Current Challenges. Curr Psychiatry Rep 22, 2 (2020). https://doi.org/10.1007/s11920-019-1125-x

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