Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 01 2023
09 01 2023
Historique:
received:
31
08
2022
accepted:
21
12
2022
entrez:
9
1
2023
pubmed:
10
1
2023
medline:
12
1
2023
Statut:
epublish
Résumé
Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.
Identifiants
pubmed: 36624241
doi: 10.1038/s41598-022-26845-0
pii: 10.1038/s41598-022-26845-0
pmc: PMC9829693
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
429Subventions
Organisme : NIA NIH HHS
ID : R01 AG050986
Pays : United States
Organisme : Medical Research Council
ID : MR/M021475/1
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : R01 MH110921
Pays : United States
Organisme : Medical Research Council
ID : G0901245
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : R01 MH109677
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH109514
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH116442
Pays : United States
Organisme : Medical Research Council
ID : G19/2
Pays : United Kingdom
Investigateurs
Rich Belliveau
(R)
Caitlin E Carey
(CE)
Felecia Cerrato
(F)
Kimberly Chambert
(K)
Claire Churchhouse
(C)
Mark J Daly
(MJ)
Ashley Dumont
(A)
Jacqueline Goldstein
(J)
Christine S Hansen
(CS)
Daniel P Howrigan
(DP)
Hailiang Huang
(H)
Julian Maller
(J)
Alicia R Martin
(AR)
Joanna Martin
(J)
Manuel Mattheisen
(M)
Jennifer Moran
(J)
Benjamin M Neale
(BM)
Jonatan Pallesen
(J)
Duncan S Palmer
(DS)
Carsten Bcker Pedersen
(CB)
Marianne Giørtz Pedersen
(MG)
Timothy Poterba
(T)
Stephan Ripke
(S)
F Kyle Satterstrom
(FK)
Wesley K Thompson
(WK)
Patrick Turley
(P)
Raymond K Walters
(RK)
Informations de copyright
© 2023. The Author(s).
Références
Krystal, J. H. & State, M. W. Psychiatric disorders: Diagnosis to therapy. Cell 157, 201–214 (2014).
doi: 10.1016/j.cell.2014.02.042
Pedersen, C. B. et al. A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. JAMA Psychiat. 71, 573–581 (2014).
doi: 10.1001/jamapsychiatry.2014.16
Sandstrom, A., Sahiti, Q., Pavlova, B. & Uher, R. Offspring of parents with schizophrenia, bipolar disorder, and depression: A review of familial high-risk and molecular genetics studies. Psychiatr. Genet. 29, 160–169 (2019).
doi: 10.1097/YPG.0000000000000240
Vreeker, A. et al. High educational performance is a distinctive feature of bipolar disorder: A study on cognition in bipolar disorder, schizophrenia patients, relatives and controls. Psychol. Med. 46, 807–818 (2016).
doi: 10.1017/S0033291715002299
Ranning, A. et al. School performance from primary education in the adolescent offspring of parents with schizophrenia and bipolar disorder- a national, register-based study. Psychol. Med. 48, 1993–2000 (2018).
doi: 10.1017/S0033291717003518
Chien, Y.-L., Tu, E.-N. & Gau, S.S.-F. School functions in unaffected siblings of youths with autism spectrum disorders. J. Autism Dev. Disord. 47, 3059–3071 (2017).
doi: 10.1007/s10803-017-3223-0
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
doi: 10.1038/s41588-018-0269-7
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
doi: 10.1038/s41588-019-0344-8
Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).
doi: 10.1038/s41586-022-04434-5
Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet. 53, 817–829 (2021).
doi: 10.1038/s41588-021-00857-4
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).
doi: 10.1038/s41588-018-0090-3
Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).
doi: 10.1038/s41588-019-0439-2
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).
doi: 10.1038/s41588-022-01016-z
Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).
doi: 10.1038/s41588-018-0152-6
Fröjd, S. A. et al. Depression and school performance in middle adolescent boys and girls. J. Adolesc. 31, 485–498 (2008).
doi: 10.1016/j.adolescence.2007.08.006
Keen, D., Webster, A. & Ridley, G. How well are children with autism spectrum disorder doing academically at school? An overview of the literature. Autism Int. J. Res. Pract. 20, 276–294 (2016).
doi: 10.1177/1362361315580962
Sundquist, J., Ohlsson, H., Winkleby, M. A., Sundquist, K. & Crump, C. School achievement and risk of eating disorders in a Swedish National Cohort. J. Am. Acad. Child Adolesc. Psychiatry 55, 41-46.e1 (2016).
doi: 10.1016/j.jaac.2015.09.021
Bansal, V. et al. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat. Commun. 9, 3078 (2018).
doi: 10.1038/s41467-018-05510-z
MacCabe, J. H. et al. Scholastic achievement at age 16 and risk of schizophrenia and other psychoses: A national cohort study. Psychol. Med. 38, 1133–1140 (2008).
doi: 10.1017/S0033291707002048
Dickinson, D. Zeroing in on early cognitive development in schizophrenia. Am. J. Psychiatry 171, 9–12 (2014).
doi: 10.1176/appi.ajp.2013.13101303
Lima, I. M. M., Peckham, A. D. & Johnson, S. L. Cognitive deficits in bipolar disorders: Implications for emotion. Clin. Psychol. Rev. 59, 126–136 (2018).
doi: 10.1016/j.cpr.2017.11.006
Escott-Price, V. et al. Genetic liability to schizophrenia is negatively associated with educational attainment in UK Biobank. Mol. Psychiatry 25, 703–705 (2020).
doi: 10.1038/s41380-018-0328-6
Donati, G., Dumontheil, I., Pain, O., Asbury, K. & Meaburn, E. L. Evidence for specificity of polygenic contributions to attainment in English, Maths and Science during adolescence. Sci. Rep. 11, 3851 (2021).
doi: 10.1038/s41598-021-82877-y
Davis, O. S. P. et al. The correlation between reading and mathematics ability at age twelve has a substantial genetic component. Nat. Commun. 5, 4204 (2014).
doi: 10.1038/ncomms5204
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).
doi: 10.1038/mp.2017.196
Thornton, L. M. et al. The Anorexia Nervosa Genetics Initiative (ANGI): Overview and methods. Contemp. Clin. Trials 74, 61–69 (2018).
doi: 10.1016/j.cct.2018.09.015
Jensen, V. M. & Rasmussen, A. W. Danish education registers. Scand. J. Public Health 39, 91–94 (2011).
doi: 10.1177/1403494810394715
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).
doi: 10.1038/s41588-018-0147-3
Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11, 201–211 (2010).
doi: 10.1038/nrn2793
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
doi: 10.1038/ng.3211
Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
doi: 10.1038/nature13595
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
doi: 10.1038/s41588-019-0397-8
Rimfeld, K. et al. Twins early development study: A genetically sensitive investigation into behavioral and cognitive development from infancy to emerging adulthood. Twin Res. Hum. Genet. Off. J. Int. Soc. Twin Stud. 22, 508–513 (2019).
doi: 10.1017/thg.2019.56
Becker, G. The association of creativity and psychopathology: Its cultural-historical origins. Creat. Res. J. 13, 45–53 (2001).
doi: 10.1207/S15326934CRJ1301_6
Kyaga, S. et al. Creativity and mental disorder: Family study of 300,000 people with severe mental disorder. Br. J. Psychiatry J. Ment. Sci. 199, 373–379 (2011).
doi: 10.1192/bjp.bp.110.085316
Andreasen, N. C. Creativity and mental illness: Prevalence rates in writers and their first-degree relatives. Am. J. Psychiatry 144, 1288–1292 (1987).
doi: 10.1176/ajp.144.10.1288
Post, F. Creativity and psychopathology. A study of 291 world-famous men. Br. J. Psychiatry J. Ment. Sci. 165, 22–34 (1994).
doi: 10.1192/bjp.165.1.22
Power, R. A. et al. Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat. Neurosci. 18, 953–955 (2015).
doi: 10.1038/nn.4040
Li, H. et al. Genome-wide association study of creativity reveals genetic overlap with psychiatric disorders, risk tolerance, and risky behaviors. Schizophr. Bull. 46, 1317–1326 (2020).
doi: 10.1093/schbul/sbaa025
Gaziano, J. M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
doi: 10.1016/j.jclinepi.2015.09.016
Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. 53, 663–671 (2021).
doi: 10.1038/s41588-021-00846-7
Sikela, J. M. & Searles Quick, V. B. Genomic trade-offs: Are autism and schizophrenia the steep price of the human brain?. Hum. Genet. 137, 1–13 (2018).
doi: 10.1007/s00439-017-1865-9
Crow, T. J. Schizophrenia as the price that homo sapiens pays for language: A resolution of the central paradox in the origin of the species. Brain Res. Brain Res. Rev. 31, 118–129 (2000).
doi: 10.1016/S0165-0173(99)00029-6
Morris, T. T., Davies, N. M., Hemani, G. & Smith, G. D. Population phenomena inflate genetic associations of complex social traits. Sci. Adv. 6, eaay0328 (2020).
doi: 10.1126/sciadv.aay0328
Belfi, B., Haelermans, C. & De Fraine, B. The long-term differential achievement effects of school socioeconomic composition in primary education: A propensity score matching approach. Br. J. Educ. Psychol. 86, 501–525 (2016).
doi: 10.1111/bjep.12120
Selzam, S. et al. Comparing within- and between-family polygenic score prediction. Am. J. Hum. Genet. 105, 351–363 (2019).
doi: 10.1016/j.ajhg.2019.06.006
Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).
doi: 10.1016/j.ajhg.2014.12.021
Mors, O., Perto, G. P. & Mortensen, P. B. The Danish Psychiatric Central Research Register. Scand. J. Public Health 39, 54–57 (2011).
doi: 10.1177/1403494810395825
Andersen, T. F., Madsen, M., Jørgensen, J., Mellemkjoer, L. & Olsen, J. H. The Danish National Hospital Register. A valuable source of data for modern health sciences. Dan. Med. Bull. 46, 263–268 (1999).
Selzam, S. et al. Predicting educational achievement from DNA. Mol. Psychiatry 22, 267–272 (2017).
doi: 10.1038/mp.2016.107
Nørgaard-Pedersen, B. & Hougaard, D. M. Storage policies and use of the Danish Newborn Screening Biobank. J. Inherit. Metab. Dis. 30, 530–536 (2007).
doi: 10.1007/s10545-007-0631-x
Hollegaard, M. V. et al. Archived neonatal dried blood spot samples can be used for accurate whole genome and exome-targeted next-generation sequencing. Mol. Genet. Metab. 110, 65–72 (2013).
doi: 10.1016/j.ymgme.2013.06.004
O’Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817–820 (2016).
doi: 10.1038/ng.3583
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 Bethesda Md 1, 457–470 (2011).
doi: 10.1534/g3.111.001198
Loh, P.-R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).
doi: 10.1038/ng.3571
Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).
doi: 10.1002/gepi.20533
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
doi: 10.1038/ng.3643
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
doi: 10.1038/ng.3656
Hunter-Zinck, H. et al. Genotyping array design and data quality control in the million veteran program. Am. J. Hum. Genet. 106, 535–548 (2020).
doi: 10.1016/j.ajhg.2020.03.004
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: Faster genotype imputation. Bioinformatics 31, 782–784 (2015).
doi: 10.1093/bioinformatics/btu704
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
doi: 10.1086/519795
Wu, C., DeWan, A., Hoh, J. & Wang, Z. A comparison of association methods correcting for population stratification in case-control studies. Ann. Hum. Genet. 75, 418–427 (2011).
doi: 10.1111/j.1469-1809.2010.00639.x
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinform. Oxf. Engl. 26, 2867–2873 (2010).
doi: 10.1093/bioinformatics/btq559
Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).
doi: 10.1016/j.ajhg.2019.08.012
Kiers, H. A. L. Simplimax: Oblique rotation to an optimal target with simple structure. Psychometrika 59, 567–579 (1994).
doi: 10.1007/BF02294392
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
doi: 10.1038/s41588-019-0481-0
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
doi: 10.1016/j.ajhg.2010.11.011
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C.A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
doi: 10.1038/s41467-019-09718-5
LDpred2: better, faster, stronger|Bioinformatics|Oxford Academic. https://academic.oup.com/bioinformatics/article/36/22-23/5424/6039173 .
Choi, S. W. & O’Reilly, P. F. PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience 8, giz082 (2019).
doi: 10.1093/gigascience/giz082
Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).
doi: 10.1186/s13742-015-0047-8