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Behavior Genetics

, Volume 49, Issue 2, pp 187–195 | Cite as

The Coherence Problem: Finding Meaning in GWAS Complexity

  • Mark A. ReimersEmail author
  • Carl Craver
  • Mikhail Dozmorov
  • Silviu-Alin Bacanu
  • Kenneth S. KendlerEmail author
Original Research

Abstract

Genome wide association studies (GWAS) for behavioral traits and psychiatric disorders have inspired both confident optimism and withering criticism. Although many recent findings from well powered GWAS have been replicated in independent data sets, the genes identified have pinned down few if any underlying causal mechanisms. Therefore, a key issue is whether or not the genes implicated by GWAS form a coherent story on their own and thus could in principle lead to insight into the biological mechanisms underlying the trait or disorder. We sketch here four scenarios for how genes may contribute to traits and disorders; genetic studies may help elucidate mechanisms under only two of our scenarios. We also describe here an approach to characterize, in an unbiased fashion, the molecular coherence of the gene sets implicated by GWAS of various behavioral and psychiatric phenotypes and we sketch how the four scenarios may be reflected in our molecular coherence measure.

Keywords

GWAS Psychiatric illness Molecular genetics Coherence Philosophy 

Notes

Funding

This project was supported by a Genetics and Human Agency Award to KSK. This work is solely the responsibility of the authors and does not necessarily represent the official view of the funders (John Templeton Foundation).

Compliance with ethical standards

Conflict of interest

Mark A. Reimers, Carl Craver, Mikhail Dozmorov, Silviu-Alin Bacanu and Kenneth S. Kendler declare no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

References

  1. Ball P (2010) Bursting the genomics bubble: column: muse. Nature 10(13-March-2010):1038–1145Google Scholar
  2. Collins FS, Patrinos A, Jordan E, Chakravarti A, Gesteland R, Walters L (1998) New goals for the U.S. Human Genome Project: 1998–2003. Science 282(5389):682–689CrossRefGoogle Scholar
  3. De Sousa E, Melo F, Vermeulen L, Fessler E, Medema JP (2013) Cancer heterogeneity—a multifaceted view. EMBO Rep 14(8):686–695CrossRefGoogle Scholar
  4. Eisenberg N, Duckworth AL, Spinrad TL, Valiente C (2014) Conscientiousness: origins in childhood? Dev Psychol 50(5):1331–1349CrossRefGoogle Scholar
  5. Emmert-Streib F, Dehmer M, Yli-Harja O (2017) Lessons from the human genome project: modesty, honesty, and realism. Front Genet 8:184CrossRefGoogle Scholar
  6. Fritsche LG et al (2014) Age-related macular degeneration: genetics and biology coming together. Annu Rev Genom Hum Genet 15:151–171CrossRefGoogle Scholar
  7. Herrera BM, Lindgren CM (2010) The genetics of obesity. Curr Diab Rep 10(6):498–505CrossRefGoogle Scholar
  8. Hromatka B et al (2015) Genetic variants associated with motion sickness point to roles for inner ear development, neurological processes, and glucose homeostasi. Hum Mol Genet 24(9):2700–2708CrossRefGoogle Scholar
  9. Hysi et al (2018) Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability. Nat Genet 50:652–656CrossRefGoogle Scholar
  10. Kendler KS, Eaves LJ (2005) Psychiatric genetics, review of psychiatry, vol 24. American Psychiatric, ArlingtonGoogle Scholar
  11. Kendler KS, Neale M, Kessler L, Heath A, Eaves L (1993) A twin study of recent life events and difficulties. ArchGenPsychiatry 50(10):789–796Google Scholar
  12. Locke AE (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538):197–206CrossRefGoogle Scholar
  13. Middeldorp CM, Danielle C, Cath JM, Vink, Dorret I (2005) Boomsma. “Twin and genetic effects on life events”. Twin Res Hum Genet 8(3):224–231CrossRefGoogle Scholar
  14. Mroz EA, Rocco JW (2017) The challenges of tumor genetic diversity. Cancer 123(6):917–927CrossRefGoogle Scholar
  15. Power RA, Wingenbach T, Cohen-Woods S, Uher R, Ng MY, Butler AW, Ising M, Craddock N, Owen MJ, Korszun A, Jones L (2013) Estimating the heritability of reporting stressful life events captured by common genetic variants. Psychol Med 43(9):1965–1971CrossRefGoogle Scholar
  16. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427CrossRefGoogle Scholar
  17. Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, Tooley K, Presumey J, Baum M, Van Doren V, Genovese G, Rose SA, Handsaker RE, Daly MJ, Carroll MC, Stevens B, McCarroll SA (2016) Schizophrenia risk from complex variation of complement component 4. Nature 530(7589):177–183CrossRefGoogle Scholar
  18. Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar HA, Giusti-Rodriguez P, Hodge RD, Miller JA, Muñoz-Manchado AB, O’Donovan MC, Owen MJ, Pardiñas AF, Ryge J, Walters JTR, Linnarsson S, Lein ES;, Sullivan PF, Hjerling-Leffler J, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2018) Genetic identification of brain cell types underlying schizophrenia. Nat Genet Jun 50(6):825–833CrossRefGoogle Scholar
  19. Stambolian D (2013) Genetic susceptibility and mechanisms for refractive error. Clin Genet 84(2):102–108CrossRefGoogle Scholar
  20. Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Borglum AD, Breen G, Cichon S, Edenberg HJ, Faraone SV, Gelernter J, Mathews CA, Nievergelt CM, Smoller JW, O’Donovan MC (2017) Psychiatric genomics: an update and an agenda. Am J Psychiatry 2017:appiajp201717030283Google Scholar
  21. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ von MC (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452CrossRefGoogle Scholar
  22. Turkheimer E (1998) Heritability and biological explanation. Psychol Rev 105(4):782–791CrossRefGoogle Scholar
  23. Turkheimer E (2001) Three laws of behavior genetics and what they mean. Curr Dir Psychol Sci 9(5):160–164CrossRefGoogle Scholar
  24. Turkheimer E (2011) Still missing. Res Hum Dev 8(3–4):227–241CrossRefGoogle Scholar
  25. Turkheimer E (2012) Genome wide association studies of behavior are social science. In: Plaisance KS, Reydon TAC (eds) Philosophy of behavioral biology: Boston studies in the philosophy of science, vol 282. Springer, Berlin, pp 43–64CrossRefGoogle Scholar
  26. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101(1):5–22CrossRefGoogle Scholar
  27. Wade N (2010) A decade later, genetic map yields few new cures. New York Times, New YorkGoogle Scholar
  28. Wingender E, Dietze P, Karas H, Knuppel R (1996) TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res 24(1):238–241CrossRefGoogle Scholar
  29. Zhang S, Jin H, Song Y, Yu W, Sun L (2016) Genetic study identifies CBLN4 as a novel susceptibility gene for accident proneness. Front Eng Manag 3(1):30–38CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Neuroscience Program and Department of Biomedical EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Philosophy-Neuroscience-Psychology ProgramWashington University in St. LouisSt. LouisUSA
  3. 3.Department of BiostatisticsVirginia Commonwealth UniversityRichmondUSA
  4. 4.Department of Psychiatry and the Virginia Institute for Psychiatric and Behavioral GeneticsVirginia Commonwealth UniversityRichmondUSA

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