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Computational Approaches for Identification of Pleiotropic Biomarker Profiles in Psychiatry

  • Han Cao
  • Emanuel SchwarzEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1134)

Abstract

The discovery of biomarkers is considered a critical step towards an improved clinical management of psychiatric disorders. Despite the availability of advanced computational approaches, the lack of strong individual predictors of clinically relevant outcomes, combined with the usually high dimensionality, significantly hamper the identification of such markers. Consistent with the often observed lack of diagnostic specificity of biological alterations, research suggests an underlying genetic pleiotropy between psychiatric illnesses and frequently comorbid conditions, such as type 2 diabetes or cardiovascular illnesses. As research is transitioning away from conventional diagnostic delineations towards a dimensional understanding of psychiatric illness, gaining insight into such pleiotropy and its downstream biological effects bears promise for identification of clinically useful biomarkers. In this review, we summarize the computational methods for identifying biological markers indexing pleiotropic effects and discuss recent research findings in this context.

Keywords

Computational approaches Genetics Biomarkers Psychiatric disorders Metabolic disorders 

Notes

Acknowledgments

This study was supported by the Deutsche Forschungsgemeinschaft (DFG), SCHW 1768/1-1.

References

  1. 1.
    Vigo D, Thornicroft G, Atun R (2016) Estimating the true global burden of mental illness. Lancet Psychiatry 3(2):171–178PubMedGoogle Scholar
  2. 2.
    Saha S, Chant D, McGrath J (2007) A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? Arch Gen Psychiatry 64(10):1123–1131PubMedGoogle Scholar
  3. 3.
    Laursen TM, Agerbo E, Pedersen CB (2009) Bipolar disorder, schizoaffective disorder, and schizophrenia overlap: a new comorbidity index. J Clin Psychiatry 70(10):1432–1438PubMedGoogle Scholar
  4. 4.
    Mukherjee S, Schnur DB, Reddy R (1989) Family history of type 2 diabetes in schizophrenic patients. Lancet 1(8636):495PubMedGoogle Scholar
  5. 5.
    Argo T, Carnahan R, Barnett M, Holman TL, Perry PJ (2011) Diabetes prevalence estimates in schizophrenia and risk factor assessment. Ann Clin Psychiatry 23(2):117–124PubMedGoogle Scholar
  6. 6.
    De Hert M, Schreurs V, Sweers K, Van Eyck D, Hanssens L, Sinko S et al (2008) Typical and atypical antipsychotics differentially affect long-term incidence rates of the metabolic syndrome in first-episode patients with schizophrenia: a retrospective chart review. Schizophr Res 101(1–3):295–303PubMedGoogle Scholar
  7. 7.
    Guest PC, Wang L, Harris LW, Burling K, Levin Y, Ernst A et al (2010) Increased levels of circulating insulin-related peptides in first-onset, antipsychotic naive schizophrenia patients. Mol Psychiatry 15(2):118–119PubMedGoogle Scholar
  8. 8.
    Venkatasubramanian G, Chittiprol S, Neelakantachar N, Naveen MN, Thirthall J, Gangadhar BN et al (2007) Insulin and insulin-like growth factor-1 abnormalities in antipsychotic-naive schizophrenia. Am J Psychiatry 164(10):1557–1560PubMedGoogle Scholar
  9. 9.
    Perry BI, McIntosh G, Weich S, Singh S, Rees K (2016) The association between first-episode psychosis and abnormal glycaemic control: systematic review and meta-analysis. Lancet Psychiatry 3(11):1049–1058PubMedGoogle Scholar
  10. 10.
    Miller BJ, Goldsmith DR, Paletta N, Wong J, Kandhal P, Black C et al (2016) Parental type 2 diabetes in patients with non-affective psychosis. Schizophr Res 175(1–3):223–225PubMedPubMedCentralGoogle Scholar
  11. 11.
    Lin PI, Shuldiner AR (2010) Rethinking the genetic basis for comorbidity of schizophrenia and type 2 diabetes. Schizophr Res 123(2–3):234–243PubMedGoogle Scholar
  12. 12.
    Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, Griffin JL et al (2004) Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry 9(7):684–697. 643PubMedGoogle Scholar
  13. 13.
    Lowell BB, Shulman GI (2005) Mitochondrial dysfunction and type 2 diabetes. Science 307(5708):384–387PubMedGoogle Scholar
  14. 14.
    Cao H, Chen J, Meyer-Lindenberg A, Schwarz E (2017) A polygenic score for schizophrenia predicts glycemic control. Transl Psychiatry 7(12):1295.  https://doi.org/10.1038/s41398-017-0044-zPubMedPubMedCentralGoogle Scholar
  15. 15.
    Hennekens CH (2007) Increasing global burden of cardiovascular disease in general populations and patients with schizophrenia. J Clin Psychiatry 68(Suppl 4):4–7PubMedGoogle Scholar
  16. 16.
    Zhuo C, Triplett PT (2018) Association of schizophrenia with the risk of breast cancer incidence: a meta-analysis. JAMA Psychiat 75(4):363–369Google Scholar
  17. 17.
    Andreassen OA, Djurovic S, Thompson WK, Schork AJ, Kendler KS, O'Donovan MC et al (2013) Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am J Hum Genet 92(2):197–209PubMedPubMedCentralGoogle Scholar
  18. 18.
    Birkenaes AB, Opjordsmoen S, Brunborg C, Engh JA, Jonsdottir H, Ringen PA et al (2007) The level of cardiovascular risk factors in bipolar disorder equals that of schizophrenia: a comparative study. J Clin Psychiatry 68(6):917–923PubMedGoogle Scholar
  19. 19.
    De Hert M, Detraux J, van Winkel R, Yu W, Correll CU (2011) Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. Nat Rev Endocrinol 8(2):114–126PubMedGoogle Scholar
  20. 20.
    Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T et al (2011) Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet 89(5):607–618PubMedPubMedCentralGoogle Scholar
  21. 21.
    Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C et al (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 7(8):e1002254.  https://doi.org/10.1371/journal.pgen.1002254PubMedPubMedCentralGoogle Scholar
  22. 22.
    Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM et al (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45(9):984–994PubMedCentralGoogle Scholar
  23. 23.
    Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47(11):1236–1241PubMedPubMedCentralGoogle Scholar
  24. 24.
    McLaughlin RL, Schijven D, van Rheenen W, van Eijk KR, O'Brien M, Kahn RS et al (2017) Genetic correlation between amyotrophic lateral sclerosis and schizophrenia. Nat Commun 8:14774.  https://doi.org/10.1038/ncomms14774PubMedPubMedCentralGoogle Scholar
  25. 25.
    Zuber V, Jönsson EG, Frei O, Witoelar A, Thompson WK, Schork AJ et al (2018) Identification of shared genetic variants between schizophrenia and lung cancer. Sci Rep 8(1):674.  https://doi.org/10.1038/s41598-017-16481-4PubMedPubMedCentralGoogle Scholar
  26. 26.
    Vandenberg SG (Ed.) (1965) Methods and goals in human behavior genetics. Academic Press Inc. (Nov. 1965). ISBN-10: 0127106502Google Scholar
  27. 27.
    Horvath S, Mirnics K (2014) Immune system disturbances in schizophrenia. Biol Psychiatry 75(4):316–323PubMedGoogle Scholar
  28. 28.
    Smith GD, Ebrahim S (2003) Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32(1):1–22PubMedGoogle Scholar
  29. 29.
    Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23(R1):R89–R98PubMedPubMedCentralGoogle Scholar
  30. 30.
    Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK et al (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380(9841):572–580PubMedPubMedCentralGoogle Scholar
  31. 31.
    Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC et al (2017) 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(2):272–279PubMedGoogle Scholar
  32. 32.
    International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460(7256):748–752PubMedCentralGoogle Scholar
  33. 33.
    Power RA, Steinberg S, Bjornsdottir G, Rietveld CA, Abdellaoui A, Nivard MM et al (2015) Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat Neurosci 18(7):953–955PubMedGoogle Scholar
  34. 34.
    Hubbard L, Tansey KE, Rai D, Jones P, Ripke S, Chambert KD et al (2016) Evidence of common genetic overlap between schizophrenia and cognition. Schizophr Bull 42(3):832–842PubMedGoogle Scholar
  35. 35.
    Gurriarán X, Rodríguez-López J, Flórez G, Pereiro C, Fernández JM, Fariñas E et al (2018) Polygenic risk scores and substance abuse comorbidity in patients with schizophrenia and bipolar disorders. Genes Brain Behav 4:e12504.  https://doi.org/10.1111/gbb.12504Google Scholar
  36. 36.
    Reginsson GW, Ingason A, Euesden J, Bjornsdottir G, Olafsson S, Sigurdsson E et al (2018) Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addict Biol 23(1):485–492PubMedGoogle Scholar
  37. 37.
    Margolese HC, Malchy L, Negrete JC, Tempier R, Gill K (2004) Drug and alcohol use among patients with schizophrenia and related psychoses: levels and consequences. Schizophr Res 67(2–3):157–166PubMedGoogle Scholar
  38. 38.
    Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S et al (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet 97(4):576–592PubMedPubMedCentralGoogle Scholar
  39. 39.
    Krapohl E, Patel H, Newhouse S, Curtis CJ, von Stumm S, Dale PS et al (2018) Multi-polygenic score approach to trait prediction. Mol Psychiatry 23(5):1368–1374PubMedGoogle Scholar
  40. 40.
    Pare G, Mao S, Deng WQ (2017) A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci Rep 7(1):12665.  https://doi.org/10.1038/s41598-017-13056-1PubMedPubMedCentralGoogle Scholar
  41. 41.
    Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47(3):291–295PubMedPubMedCentralGoogle Scholar
  42. 42.
    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York Inc.; 2nd ed. 2009, Corr. 9th printing 2017 edition (1 Oct. 2009). ISBN-10: 0387848576Google Scholar
  43. 43.
    Márquez-Luna C, Loh PR, South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, Price AL (2017) Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol 41(8):811–823PubMedPubMedCentralGoogle Scholar
  44. 44.
    Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7):565–569PubMedPubMedCentralGoogle Scholar
  45. 45.
    Lee SH, DeCandia TR, Ripke S, Yang J, Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), International Schizophrenia Consortium (ISC) et al (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44(3):247–250PubMedPubMedCentralGoogle Scholar
  46. 46.
    Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR (2012) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28(19):2540–2542PubMedPubMedCentralGoogle Scholar
  47. 47.
    Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82PubMedPubMedCentralGoogle Scholar
  48. 48.
    Davis LK, Yu D, Keenan CL, Gamazon ER, Konkashbaev AI, Derks EM et al (2013) Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture. PLoS Genet 9(10):e1003864.  https://doi.org/10.1371/journal.pgen.1003864PubMedPubMedCentralGoogle Scholar
  49. 49.
    Brainstorm Consortium, 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(6395):eaap8757.  https://doi.org/10.1126/science.aap8757Google Scholar
  50. 50.
    Elks CE, Ong KK, Scott RA, van der Schouw YT, Brand JS, Wark PA et al (2013) Age at menarche and type 2 diabetes risk: the EPIC-InterAct study. Diabetes Care 36(11):3526–3534PubMedPubMedCentralGoogle Scholar
  51. 51.
    Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47(11):1228–1235PubMedPubMedCentralGoogle Scholar
  52. 52.
    Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C (2014) Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 13(8):788–794PubMedGoogle Scholar
  53. 53.
    Striegel-Moore RH, Garvin V, Dohm FA, Rosenheck RA (1999) Psychiatric comorbidity of eating disorders in men: a national study of hospitalized veterans. Int J Eat Disord 25(4):399–404PubMedGoogle Scholar
  54. 54.
    Sun L, Craiu RV, Paterson AD, Bull SB (2006) Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet Epidemiol 30(6):519–530PubMedGoogle Scholar
  55. 55.
    Efron B (2007) Size, power and false discovery rates. Ann Stat 35(4):1351–1377Google Scholar
  56. 56.
    Storey JD (2003) The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat 31(6):2013–2035Google Scholar
  57. 57.
    Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43(10):969–976Google Scholar
  58. 58.
    Psychiatric GWAS Consortium Bipolar Disorder Working Group (2011) Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43(10):977–983Google Scholar
  59. 59.
    Ferreira MA, O'Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L et al (2008) Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40(9):1056–1058PubMedPubMedCentralGoogle Scholar
  60. 60.
    Green EK, Grozeva D, Forty L, Gordon-Smith K, Russell E, Farmer A et al (2013) Association at SYNE1 in both bipolar disorder and recurrent major depression. Mol Psychiatry 18(5):614–617PubMedGoogle Scholar
  61. 61.
    Le Hellard S, Wang Y, Witoelar A, Zuber V, Bettella F, Hugdahl K et al (2017) Identification of gene loci that overlap between schizophrenia and educational attainment. Schizophr Bull 43(3):654–664PubMedGoogle Scholar
  62. 62.
    Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D et al (2008) A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature 452(7187):633–637PubMedGoogle Scholar
  63. 63.
    Zuber V, Marconett CN, Shi J, Hua X, Wheeler W, Yang C et al (2016) Pleiotropic analysis of lung cancer and blood triglycerides. J Natl Cancer Inst 108(12):djw167.  https://doi.org/10.1093/jnci/djw167PubMedPubMedCentralGoogle Scholar
  64. 64.
    Xia CH, Ma Z, Ciric R, Gu S, Betzel RF, Kaczkurkin AN et al (2018) Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9(1):3003.  https://doi.org/10.1038/s41467-018-05317-yPubMedPubMedCentralGoogle Scholar
  65. 65.
    Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C et al (2018) Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359(6376):693–697PubMedPubMedCentralGoogle Scholar
  66. 66.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559.  https://doi.org/10.1186/1471-2105-9-559PubMedPubMedCentralGoogle Scholar
  67. 67.
    Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16(6):321–332PubMedPubMedCentralGoogle Scholar
  68. 68.
    Fan J, Lv J (2010) A selective overview of variable selection in high dimensional feature space. Stat Sin 20(1):101–148PubMedPubMedCentralGoogle Scholar
  69. 69.
    Richfield O, Ashad A, Calhoun V, Wang Y-P (2018) Learning schizophrenia imaging genetics data via Multiple Kernel canonical correlation analysis. Comput Stat Data Anal 125:70–85Google Scholar
  70. 70.
    Hardoon DR, Shawe-Taylor J (2010) Sparse canonical correlation analysis. Mach Learn 83(3):331–353Google Scholar
  71. 71.
    Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L et al (2010) Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 49(10):980–989PubMedPubMedCentralGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Psychiatry and PsychotherapyCentral Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany

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