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Genetic analysis of deep phenotyping projects in common disorders

  • Elliot S. Gershon(corresponding author)
    ,
  • Godfrey Pearlson
    ,
  • Matcheri S. Keshavan
    ,
  • Carol Tamminga
    ,
  • Brett Clementz
    ,
  • Peter F. Buckley
*Corresponding author for this work
  • The University of Chicago
    ,
  • Institute of Living
    ,
  • Yale University
    ,
  • Harvard University
    ,
  • University of Texas Southwestern Medical Center
    ,
  • University of Georgia
Scholary Output:
Contribution to journal
Review article
Peer-review

Open access

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

Abstract

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

Publication Information

Output type

Scholary Output:
Contribution to journal
Review article
Peer-review

Original language

English (US)

Pages from-to (Number of pages)

Pages 51-57 (7 pages)

Journal (Volume, Issue Number)

Schizophrenia Research (Volume 195)

Publication milestones

  • Accepted/In press - 01/01/2017
  • Published - 05/2018

Publication status

Published - 05/2018

ISSN

0920-9964

Publication IDs

  • Scopus: 85031759754

Publication metrics

Metrics

Scopus
citations
Fractional count
1
Fractional count
0.07
Fractional count
14
Fractional count
0.93
Fractional count
1
Fractional count
1
SciVal
FWCI
0.05
SciVal
Author count
15
SciVal
citations
1
SciVal
Paper percentile
40

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Citation count
9
Usage
90
Captures
58

Funding Details

Grant support: ESG: NIMH MH103368, GP: NIMH MH077945, CT: NIMH MH077851, MSK: NIMH MH078113, BC: NIMH MH103366, JAS: NIMH MH077862.
FunderFunding numbers
NIMH
MH077945, MH078113, MH077851, MH077862, R01MH103368, MH103366