Polygenic risk for autism spectrum disorder associates with anger recognition in a neurodevelopment-focused phenome-wide scan of unaffected youths from a population-based cohort

Autoři: Frank R. Wendt aff001;  Carolina Muniz Carvalho aff001;  Gita A. Pathak aff001;  Joel Gelernter aff001;  Renato Polimanti aff001
Působiště autorů: Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, United States of America aff001;  Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil aff002;  Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, United States of America aff003
Vyšlo v časopise: Polygenic risk for autism spectrum disorder associates with anger recognition in a neurodevelopment-focused phenome-wide scan of unaffected youths from a population-based cohort. PLoS Genet 16(9): e32767. doi:10.1371/journal.pgen.1009036
Kategorie: Research Article
doi: 10.1371/journal.pgen.1009036


The polygenic nature and the contribution of common genetic variation to autism spectrum disorder (ASD) allude to a high degree of pleiotropy between ASD and other psychiatric and behavioral traits. In a pleiotropic system, a single genetic variant contributes small effects to several phenotypes or disorders. While analyzed broadly, there is a paucity of research studies investigating the shared genetic information between specific neurodevelopmental domains and ASD. We performed a phenome-wide association study of ASD polygenetic risk score (PRS) against 491 neurodevelopmental subdomains ascertained in 4,309 probands from the Philadelphia Neurodevelopmental Cohort (PNC) who lack an ASD diagnosis. Our main analysis calculated ASD PRS in 4,309 PNC probands using the per-SNP effects reported in a recent genome-wide association study of ASD in a case-control design. In a high-resolution manner, our main analysis regressed ASD PRS against 491 neurodevelopmental phenotypes with age, sex, and ten principal components of ancestry as covariates. Follow-up analyses included in the regression model PRS derived from brain-related traits genetically correlated with ASD. Our main finding demonstrated that 11-17-year old probands with the highest ASD genetic risk were able to identify angry faces (R2 = 1.06%, p = 1.38 × 10−7, pBonferroni-corrected = 1.9 × 10−3). This ability replicated in older probands (>18 years; R2 = 0.55%, p = 0.036) and persisted after covarying with other psychiatric disorders, brain imaging traits, and educational attainment (R2 = 0.2%, p = 0.019). We also detected several suggestive associations between ASD PRS and emotionality and connectedness with others. These data (i) indicate how genetic liability to ASD may influence neurodevelopment in the general population, (ii) reinforce epidemiological findings of heightened ability of ASD cases to predict certain social psychological events based on increased systemizing skills, and (iii) recapitulate theories of imbalance between empathizing and systemizing in ASD etiology.

Klíčová slova:

Autism spectrum disorder – Clinical genetics – Emotions – Face recognition – Genetics – Medical risk factors – Neuroimaging – Phenotypes


1. 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. doi: 10.1038/s41588-019-0344-8 30804558

2. Kim YS, Leventhal BL, Koh YJ, Fombonne E, Laska E, Lim EC, et al. Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry. 2011;168(9):904–12. doi: 10.1176/appi.ajp.2011.10101532 21558103

3. Polimanti R, Gelernter J. Widespread signatures of positive selection in common risk alleles associated to autism spectrum disorder. PLoS Genet. 2017;13(2):e1006618. doi: 10.1371/journal.pgen.1006618 28187187

4. Easey K, Haan E, Schellas L, Sallis H, Wootton R, Munafò M, et al. P11 The association of alcohol PRS on mental health phenotypes: a PheWAS in the avon longitudinal study of parents and children (ALSPAC). Journal of Epidemiology and Community Health. 2019;73(Suppl 1):A76–A7.

5. Fritsche LG, Beesley LJ, VandeHaar P, Peng RB, Salvatore M, Zawistowski M, et al. Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb. PLoS Genet. 2019;15(6):e1008202. doi: 10.1371/journal.pgen.1008202 31194742

6. Leppert B, Millard LA, Riglin L, Smith GD, Thapar A, Tilling K, et al. A cross-disorder MR-pheWAS of 5 major psychiatric disorders in UK Biobank. bioRxiv. 2019:634774.

7. Richardson TG, Harrison S, Hemani G, Davey Smith G. An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. Elife. 2019;8.

8. Zheutlin AB, Dennis J, Karlsson Linner R, Moscati A, Restrepo N, Straub P, et al. Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. Am J Psychiatry. 2019;176(10):846–55. doi: 10.1176/appi.ajp.2019.18091085 31416338

9. Calkins ME, Merikangas KR, Moore TM, Burstein M, Behr MA, Satterthwaite TD, et al. The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. J Child Psychol Psychiatry. 2015;56(12):1356–69. doi: 10.1111/jcpp.12416 25858255

10. Calkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB, et al. The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry. 2014;13(3):296–305. doi: 10.1002/wps.20152 25273303

11. Glessner JT, Reilly MP, Kim CE, Takahashi N, Albano A, Hou C, et al. Strong synaptic transmission impact by copy number variations in schizophrenia. Proc Natl Acad Sci U S A. 2010;107(23):10584–9. doi: 10.1073/pnas.1000274107 20489179

12. Gur RC, Calkins ME, Satterthwaite TD, Ruparel K, Bilker WB, Moore TM, et al. Neurocognitive growth charting in psychosis spectrum youths. JAMA Psychiatry. 2014;71(4):366–74. doi: 10.1001/jamapsychiatry.2013.4190 24499990

13. Merikangas AK, Calkins ME, Bilker WB, Moore TM, Gur RC, Gur RE. Parental Age and Offspring Psychopathology in the Philadelphia Neurodevelopmental Cohort. J Am Acad Child Adolesc Psychiatry. 2017;56(5):391–400. doi: 10.1016/j.jaac.2017.02.004 28433088

14. Kohler CG, Turner T, Stolar NM, Bilker WB, Brensinger CM, Gur RE, et al. Differences in facial expressions of four universal emotions. Psychiatry Res. 2004;128(3):235–44. doi: 10.1016/j.psychres.2004.07.003 15541780

15. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562(7726):210–6. doi: 10.1038/s41586-018-0571-7 30305740

16. 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. doi: 10.1038/s41588-018-0269-7 30478444

17. Duncan L, Yilmaz Z, Gaspar H, Walters R, Goldstein J, Anttila V, et al. Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa. Am J Psychiatry. 2017;174(9):850–8. doi: 10.1176/appi.ajp.2017.16121402 28494655

18. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium (2018) Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell.173(7):1705–15 e16. doi: 10.1016/j.cell.2018.05.046 29906448

19. 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. doi: 10.1038/s41588-018-0090-3 29700475

20. Yu D, Sul JH, Tsetsos F, Nawaz MS, Huang AY, Zelaya I, et al. Interrogating the Genetic Determinants of Tourette's Syndrome and Other Tic Disorders Through Genome-Wide Association Studies. Am J Psychiatry. 2019;176(3):217–27. doi: 10.1176/appi.ajp.2018.18070857 30818990

21. International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS. Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol Psychiatry. 2018;23(5):1181–8. doi: 10.1038/mp.2017.154 28761083

22. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature.511(7510):421–7. doi: 10.1038/nature13595 25056061

23. 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. doi: 10.1038/s41588-018-0147-3 30038396

24. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41. doi: 10.1038/ng.3406 26414676

25. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523–36. doi: 10.1038/nn.4393 27643430

26. Baker LA, Jacobson KC, Raine A, Lozano DI, Bezdjian S. Genetic and environmental bases of childhood antisocial behavior: a multi-informant twin study. J Abnorm Psychol. 2007;116(2):219–35. doi: 10.1037/0021-843X.116.2.219 17516756

27. Lai MC, Lombardo MV, Baron-Cohen S. Autism. Lancet. 2014;383(9920):896–910. doi: 10.1016/S0140-6736(13)61539-1 24074734

28. Kou J, Le J, Fu M, Lan C, Chen Z, Li Q, et al. Comparison of three different eye-tracking tasks for distinguishing autistic from typically developing children and autistic symptom severity. bioRxiv. 2019:547505.

29. Gollwitzer A, Bargh JA. Social psychological skill and its correlates. Social Psychology. 2018;49(2):88–102.

30. Gollwitzer A, Marte C, McPartland JC, Bargh JA. Autism spectrum traits predict higher social psychological skill. Proc Natl Acad Sci U S A. 2019.

31. Smith KW, Balkwill LL, Vartanian O, Goel V. Syllogisms delivered in an angry voice lead to improved performance and engagement of a different neural system compared to neutral voice. Front Hum Neurosci. 2015;9:273. doi: 10.3389/fnhum.2015.00273 26029089

32. Coleman JRI, Lester KJ, Keers R, Munafo MR, Breen G, Eley TC. Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders. Am J Med Genet B Neuropsychiatr Genet. 2017;174(7):701–11. doi: 10.1002/ajmg.b.32558 28608620

33. Warrier V, Baron-Cohen S. Genetic contribution to 'theory of mind' in adolescence. Sci Rep. 2018;8(1):3465. doi: 10.1038/s41598-018-21737-8 29472613

34. Warrier V, Grasby KL, Uzefovsky F, Toro R, Smith P, Chakrabarti B, et al. Genome-wide meta-analysis of cognitive empathy: heritability, and correlates with sex, neuropsychiatric conditions and cognition. Mol Psychiatry. 2018;23(6):1402–9. doi: 10.1038/mp.2017.122 28584286

35. Lausen A, Broering C, Penke L, Schacht A. Hormonal and modality specific effects on males’ emotion recognition ability. bioRxiv. 2019:791376.

36. Thompson AE, Voyer D. Sex differences in the ability to recognise non-verbal displays of emotion: a meta-analysis. Cogn Emot. 2014;28(7):1164–95. doi: 10.1080/02699931.2013.875889 24400860

37. Mancini G, Biolcati R, Agnoli S, Andrei F, Trombini E. Recognition of Facial Emotional Expressions Among Italian Pre-adolescents, and Their Affective Reactions. Front Psychol. 2018;9:1303. doi: 10.3389/fpsyg.2018.01303 30123150

38. St Pourcain B, Robinson EB, Anttila V, Sullivan BB, Maller J, Golding J, et al. ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties. Mol Psychiatry. 2018;23(2):263–70. doi: 10.1038/mp.2016.198 28044064

39. Thapar A, Riglin L. The importance of a developmental perspective in Psychiatry: what do recent genetic-epidemiological findings show? Mol Psychiatry. 2020.

40. Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat Methods. 2011;9(2):179–81. doi: 10.1038/nmeth.1785 22138821

41. Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3 (Bethesda). 2011;1(6):457–70.

42. Euesden J, Lewis CM, O'Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31(9):1466–8. doi: 10.1093/bioinformatics/btu848 25550326

43. Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74(4):765–9. doi: 10.1086/383251 14997420

Článek vyšel v časopise

PLOS Genetics

2020 Číslo 9

Nejčtenější v tomto čísle

Zvyšte si kvalifikaci online z pohodlí domova

Možnosti biologické léčby u CRSwNP
nový kurz
Autoři: MUDr. Zuzana Balatková, Ph.D.

Magnetická rezonance a diagnostika axiálních spondyloartritid - Virtuální trénink
Autoři: MUDr. Leona Procházková, Ph.D., MUDr. Monika Gregová, Ph.D., MUDr. Vladimír Červeňák, MUDr. Eva Korčáková, Ph.D.

Eozinofilní granulomatóza s polyangiitidou

Betablokátory a Ca antagonisté z jiného úhlu
Autoři: prof. MUDr. Michal Vrablík, Ph.D., MUDr. Petr Janský

Urtikarie – přehled o onemocnění a léčbě a významu antihistaminik

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.


Nemáte účet?  Registrujte se