Candidate gene scoring to predict broad adolescent psychopathology

Schafer, Kathryn Elizabeth

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https://hdl.handle.net/2142/101365 Copy

Description

Title

Candidate gene scoring to predict broad adolescent psychopathology

Author(s)

Schafer, Kathryn Elizabeth

Issue Date

2018-04-23

Director of Research (if dissertation) or Advisor (if thesis)

Derringer, Jaime L.

Committee Member(s)

Hankin, Benjamin L.

Department of Study

Psychology

Discipline

Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• behavior genetics
• psychopathology
• mental illness
• p-factor
• genetic risk score
• candidate gene

Abstract

Candidate gene effects consistently fail to replicate. However, because it is now known that most genetic effects are incredibly minute, samples of the size typically employed in psychological research were undoubtedly too small to detect the effects of individual candidate genes. In addition, research showing strong genetic correlation among mental disorders suggests data on multiple disorders and their symptoms is the most appropriate for uncovering the etiology of mental illness. That is, single gene, single disorder studies are underpowered. We tested whether the combined effect of 121 candidate genes was sufficient to predict psychopathology in a sample of 343 adolescents. A genetic risk score was created with highly precise effect estimates from a genome-wide association study (GWAS) on 337,199 people. To maximize the strength of this score, we used transdiagnostic p-factor model scores as our measure of psychopathology. The genetic risk scores failed to predict in our sample and were dwarfed by age and gender effects, mirroring the genes’ weak and mostly non-significant results in the GWAS. Our results are most consistent with the view that the candidate gene approach is obsolete. However, modern molecular genetics studies like GWAS currently lack detailed, thorough phenotype measurement. Future work should focus on developing high quality, deeply phenotyped data currently lacking in large consortia efforts.

Graduation Semester

2018-05

Type of Resource

text

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http://hdl.handle.net/2142/101365

Copyright and License Information

Copyright 2018 Kathryn Schafer

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