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A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank


Autoři: Beate Leppert aff001;  Louise A. C. Millard aff001;  Lucy Riglin aff004;  George Davey Smith aff001;  Anita Thapar aff004;  Kate Tilling aff001;  Esther Walton aff001;  Evie Stergiakouli aff001
Působiště autorů: MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom aff001;  Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom aff002;  Intelligent Systems Laboratory, University of Bristol, Bristol, United Kingdom aff003;  MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom aff004;  Division of Psychological Medicine and Clinical Neurosciences; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom aff004;  Department of Psychology, University of Bath, Bath, United Kingdom aff005
Vyšlo v časopise: A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008185
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008185

Souhrn

Psychiatric disorders are highly heritable and associated with a wide variety of social adversity and physical health problems. Using genetic liability (rather than phenotypic measures of disease) as a proxy for psychiatric disease risk can be a useful alternative for research questions that would traditionally require large cohort studies with long-term follow up. Here we conducted a hypothesis-free phenome-wide association study in about 330,000 participants from the UK Biobank to examine associations of polygenic risk scores (PRS) for five psychiatric disorders (major depression (MDD), bipolar disorder (BP), schizophrenia (SCZ), attention-deficit/ hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)) with 23,004 outcomes in UK Biobank, using the open-source PHESANT software package. There was evidence after multiple testing (p<2.55x10-06) for associations of PRSs with 294 outcomes, most of them attributed to associations of PRSMDD (n = 167) and PRSSCZ (n = 157) with mental health factors. Among others, we found strong evidence of association of higher PRSADHD with 1.1 months younger age at first sexual intercourse [95% confidence interval [CI]: -1.25,-0.92] and a history of physical maltreatment; PRSASD with 0.01% lower erythrocyte distribution width [95%CI: -0.013,-0.007]; PRSSCZ with 0.95 lower odds of playing computer games [95%CI:0.95,0.96]; PRSMDD with a 0.12 points higher neuroticism score [95%CI:0.111,0.135] and PRSBP with 1.03 higher odds of having a university degree [95%CI:1.02,1.03]. We were able to show that genetic liabilities for five major psychiatric disorders associate with long-term aspects of adult life, including socio-demographic factors, mental and physical health. This is evident even in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis.

Klíčová slova:

ADHD – Autism spectrum disorder – Bipolar disorder – Clinical genetics – Depression – Genome-wide association studies – Mental health and psychiatry – Schizophrenia


Zdroje

1. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, et al. (2015) Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet 47: 702. doi: 10.1038/ng.3285 25985137

2. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, et al. (2019) Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 51: 63–75. doi: 10.1038/s41588-018-0269-7 30478444

3. Ripke S, Consortium SWGotPG (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421–427. doi: 10.1038/nature13595 25056061

4. Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, et al. (2019) Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 51: 431–444. doi: 10.1038/s41588-019-0344-8 30804558

5. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, et al. (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50: 668–681. doi: 10.1038/s41588-018-0090-3 29700475

6. Thapar A, Cooper M, Rutter M (2017) Neurodevelopmental disorders. Lancet Psychiatry 4: 339–346. doi: 10.1016/S2215-0366(16)30376-5 27979720

7. Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. (2018) Analysis of shared heritability in common disorders of the brain. Science 360.

8. Wray NR, Lee SH, Mehta D, Vinkhuyzen AAE, Dudbridge F, et al. (2014) Research Review: Polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry 55: 1068–1087. doi: 10.1111/jcpp.12295 25132410

9. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, et al. (2017) Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol 186: 1026–1034. doi: 10.1093/aje/kwx246 28641372

10. Martin AR, Daly MJ, Robinson EB, Hyman SE, Neale BM (2019) Predicting Polygenic Risk of Psychiatric Disorders. Biol Psychiatry 86: 97–109. doi: 10.1016/j.biopsych.2018.12.015 30737014

11. Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9: e1003348. doi: 10.1371/journal.pgen.1003348 23555274

12. Riglin L, Collishaw S, Thapar AK, Dalsgaard S, Langley K, et al. (2016) Association of Genetic Risk Variants With Attention-Deficit/Hyperactivity Disorder Trajectories in the General Population. JAMA Psychiatry 73: 1285–1292. doi: 10.1001/jamapsychiatry.2016.2817 27806167

13. So H-C, Sham PC (2016) Exploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits. Bioinformatics 33: 886–892.

14. Jansen AG, Dieleman GC, Jansen PR, Verhulst FC, Posthuma D, et al. (2019) Psychiatric Polygenic Risk Scores as Predictor for Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder in a Clinical Child and Adolescent Sample. Behav Genet.

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

16. Leppert B, Havdahl A, Riglin L, Jones HJ, Zheng J, et al. (2019) Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry.

17. Loe IM, Feldman HM (2007) Academic and Educational Outcomes of Children With ADHD. J Pediat Psychol 32: 643–654.

18. Stergiakouli E, Martin J, Hamshere ML, Heron J, St Pourcain B, et al. (2017) Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population. Int J Epidemiol 46: 421–428. doi: 10.1093/ije/dyw216 27694570

19. Rhodes JD, Pelham WE, Gnagy EM, Shiffman S, Derefinko KJ, et al. (2016) Cigarette smoking and ADHD: An examination of prognostically relevant smoking behaviors among adolescents and young adults. Psychol Addict Behav 30: 588–600. doi: 10.1037/adb0000188 27824233

20. Flory K, Molina BS, Pelham WE, Jr., Gnagy E, Smith B (2006) Childhood ADHD predicts risky sexual behavior in young adulthood. J Clin Child Adolesc Psychol 35: 571–577. doi: 10.1207/s15374424jccp3504_8 17007602

21. Hanc T, Cortese S (2018) Attention deficit/hyperactivity-disorder and obesity: A review and model of current hypotheses explaining their comorbidity. Neurosci Biobehav Rev 92: 16–28. doi: 10.1016/j.neubiorev.2018.05.017 29772309

22. Schachar R, Taylor E, Wieselberg M, Thorley G, Rutter M (1987) Changes in Family Function and Relationships in Children Who Respond to Methylphenidate. J Am Acad Child Adolesc Psychiatry 26: 728–732. doi: 10.1097/00004583-198709000-00019 3667503

23. Lifford KJ, Harold GT, Thapar A (2009) Parent-child hostility and child ADHD symptoms: a genetically sensitive and longitudinal analysis. J Child Psychol Psychiatry 50: 1468–1476. doi: 10.1111/j.1469-7610.2009.02107.x 19508494

24. Harold GT, Leve LD, Barrett D, Elam K, Neiderhiser JM, et al. (2013) Biological and rearing mother influences on child ADHD symptoms: revisiting the developmental interface between nature and nurture. J Child Psychol Psychiatry 54: 1038–1046. doi: 10.1111/jcpp.12100 24007415

25. Neumeyer AM, Cano Sokoloff N, McDonnell EI, Macklin EA, McDougle CJ, et al. (2018) Nutrition and Bone Density in Boys with Autism Spectrum Disorder. J Acad Nutr Diet 118: 865–877. doi: 10.1016/j.jand.2017.11.006 29409733

26. Ekhlaspour L, Baskaran C, Campoverde KJ, Sokoloff NC, Neumeyer AM, et al. (2016) Bone Density in Adolescents and Young Adults with Autism Spectrum Disorders. J Autism Dev Disord 46: 3387–3391. doi: 10.1007/s10803-016-2871-9 27491424

27. Neumeyer AM, O'Rourke JA, Massa A, Lee H, Lawson EA, et al. (2015) Brief report: bone fractures in children and adults with autism spectrum disorders. J Autism Dev Disord 45: 881–887. doi: 10.1007/s10803-014-2228-1 25193141

28. Mostafa GA, Al-Ayadhi LY (2012) Reduced serum concentrations of 25-hydroxy vitamin D in children with autism: relation to autoimmunity. J Neuroinflammation 9: 201. doi: 10.1186/1742-2094-9-201 22898564

29. Macova L, Bicikova M, Ostatnikova D, Hill M, Starka L (2017) Vitamin D, neurosteroids and autism. Physiol Res 66: S333–s340. doi: 10.33549/physiolres.933721 28948817

30. Reitan RM (1958) Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.

31. Aleman A, Hijman R, de Haan EH, Kahn RS (1999) Memory impairment in schizophrenia: a meta-analysis. Am J Psychiatry 156: 1358–1366. doi: 10.1176/ajp.156.9.1358 10484945

32. Wolwer W, Gaebel W (2002) Impaired Trail-Making Test-B performance in patients with acute schizophrenia is related to inefficient sequencing of planning and acting. J Psychiatr Res 36: 407–416. doi: 10.1016/s0022-3956(02)00050-x 12393310

33. Heinrichs RW, Zakzanis KK (1998) Neurocognitive deficit in schizophrenia: a quantitative review of the evidence. Neuropsychol 12: 426–445.

34. Zalla T, Joyce C, Szoke A, Schurhoff F, Pillon B, et al. (2004) Executive dysfunctions as potential markers of familial vulnerability to bipolar disorder and schizophrenia. Psychiatry Res 121: 207–217. doi: 10.1016/s0165-1781(03)00252-x 14675740

35. Sitskoorn MM, Aleman A, Ebisch SJ, Appels MC, Kahn RS (2004) Cognitive deficits in relatives of patients with schizophrenia: a meta-analysis. Schizophr Res 71: 285–295. doi: 10.1016/j.schres.2004.03.007 15474899

36. Perianez JA, Rios-Lago M, Rodriguez-Sanchez JM, Adrover-Roig D, Sanchez-Cubillo I, et al. (2007) Trail Making Test in traumatic brain injury, schizophrenia, and normal ageing: sample comparisons and normative data. Arch Clin Neuropsychol 22: 433–447. doi: 10.1016/j.acn.2007.01.022 17336493

37. Zhou RY, Wang JJ, Sun JC, You Y, Ying JN, et al. (2017) Attention deficit hyperactivity disorder may be a highly inflammation and immune-associated disease (Review). Mol Med Rep 16: 5071–5077. doi: 10.3892/mmr.2017.7228 28849096

38. Galecki P, Talarowska M (2018) Inflammatory theory of depression. Psychiatr Pol 52: 437–447. doi: 10.12740/PP/76863 30218560

39. Savitz J, Harrison NA (2018) Interoception and Inflammation in Psychiatric Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 3: 514–524. doi: 10.1016/j.bpsc.2017.12.011 29884282

40. Tonacci A, Billeci L, Ruta L, Tartarisco G, Pioggia G, et al. (2017) A systematic review of the association between allergic asthma and autism. Minerva Pediatr 69: 538–550. doi: 10.23736/S0026-4946.16.04623-5 27706122

41. Billeci L, Tonacci A, Tartarisco G, Ruta L, Pioggia G, et al. (2015) Association Between Atopic Dermatitis and Autism Spectrum Disorders: A Systematic Review. Am J Clin Dermatol 16: 371–388. doi: 10.1007/s40257-015-0145-5 26254000

42. Schans JV, Cicek R, de Vries TW, Hak E, Hoekstra PJ (2017) Association of atopic diseases and attention-deficit/hyperactivity disorder: A systematic review and meta-analyses. Neurosci Biobehav Rev 74: 139–148. doi: 10.1016/j.neubiorev.2017.01.011 28111269

43. Hackinger S, Prins B (2018) Evidence for genetic contribution to the increased risk of type 2 diabetes in schizophrenia. 8: 252.

44. Taylor AE, Jones HJ, Sallis H, Euesden J, Stergiakouli E, et al. (2018) Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. Int J Epidemiol.

45. Munafò MR, Tilling K, Taylor AE, Evans DM, Davey Smith G (2018) Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol 47: 226–235. doi: 10.1093/ije/dyx206 29040562

46. Lawlor DA, Tilling K, Davey Smith G (2016) Triangulation in aetiological epidemiology. Int J Epidemiol 45: 1866–1886. doi: 10.1093/ije/dyw314 28108528

47. Davey Smith G, Ebrahim S (2001) Epidemiology—is it time to call it a day? Int J Epidemiol 30: 1–11. doi: 10.1093/ije/30.1.1 11171840

48. World Health Organisation WHO (2003) Investing in mental health. Geneva.

49. Mitchell R, Elsworth B, Mitchell R, Raistrick C, Paternoster L, et al. (2019) MRC IEU UK Biobank GWAS pipeline version 2. https://doi.org/10.5523/bris.pnoat5528cxo5520u5552p5526ynfaekeigi.

50. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, et al. (2010) Robust relationship inference in genome-wide association studies. Bioinformatics 26: 2867–2873. doi: 10.1093/bioinformatics/btq559 20926424

51. Martin J, Taylor MJ, Rydell M, Riglin L, Eyre O, et al. (2018) Sex-specific manifestation of genetic risk for attention deficit hyperactivity disorder in the general population. J Child Psychol Psychiatry.

52. Richardson TG, Harrison S, Hemani G, Davey Smith G (2019) An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. eLife 8: e43657. doi: 10.7554/eLife.43657 30835202

53. Ruderfer DM, Ripke S, McQuillin A, Boocock J, Stahl EA, et al. (2018) Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell 173: 1705–1715.e1716. doi: 10.1016/j.cell.2018.05.046 29906448

54. Millard LAC, Davies NM, Gaunt TR, Davey Smith G, Tilling K (2017) Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int J Epidemiol.

55. Millard LAC, Davies NM, Tilling K, Gaunt TR, Davey Smith G (2019) Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. PLoS Genet 15: e1007951. doi: 10.1371/journal.pgen.1007951 30707692

56. Zheng J, Richardson TG, Millard LAC, Hemani G, Elsworth BL, et al. (2018) PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics. Gigascience 7.

57. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, et al. (2019) Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 22: 343–352. doi: 10.1038/s41593-018-0326-7 30718901


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