Genetic analysis of deep phenotyping projects in common disorders

Elliot S. Gershon, Godfrey Pearlson, Matcheri S. Keshavan, Carol Tamminga, Brett Clementz, Peter F. Buckley, Ney Alliey-Rodriguez, Chunyu Liu, John A. Sweeney, Sarah Keedy, Shashwath A. Meda, Neeraj Tandon, Rebecca Shafee, Jeffrey R. Bishop, Elena I. Ivleva

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)51-57
Number of pages7
JournalSchizophrenia Research
StatePublished - May 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.


  • Functional genomics
  • Genetic analysis
  • Imputation
  • Multiple testing
  • Phenotype


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