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

doi:10.1016/j.schres.2017.09.031

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 journal › Review article › peer-review

8 Scopus citations

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.

Original language	English (US)
Pages (from-to)	51-57
Number of pages	7
Journal	Schizophrenia Research
Volume	195
DOIs	https://doi.org/10.1016/j.schres.2017.09.031
State	Published - May 2018
Externally published	Yes

Keywords

Functional genomics
Genetic analysis
Imputation
Multiple testing
Phenotype

ASJC Scopus subject areas

Psychiatry and Mental health
Biological Psychiatry

Access to Document

10.1016/j.schres.2017.09.031

Cite this

Gershon, E. S., Pearlson, G., Keshavan, M. S., Tamminga, C., Clementz, B., Buckley, P. F., Alliey-Rodriguez, N., Liu, C., Sweeney, J. A., Keedy, S., Meda, S. A., Tandon, N., Shafee, R., Bishop, J. R., & Ivleva, E. I. (2018). Genetic analysis of deep phenotyping projects in common disorders. Schizophrenia Research, 195, 51-57. https://doi.org/10.1016/j.schres.2017.09.031

@article{467afa73be5f412cb24f9082ac9b19ca,

title = "Genetic analysis of deep phenotyping projects in common disorders",

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.",

keywords = "Functional genomics, Genetic analysis, Imputation, Multiple testing, Phenotype",

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

note = "Funding Information: Grant support: ESG: NIMH MH103368, GP: NIMH MH077945, CT: NIMH MH077851, MSK: NIMH MH078113, BC: NIMH MH103366, JAS: NIMH MH077862. Publisher Copyright: {\textcopyright} 2017 Elsevier B.V.",

year = "2018",

month = may,

doi = "10.1016/j.schres.2017.09.031",

language = "English (US)",

volume = "195",

pages = "51--57",

journal = "Schizophrenia Research",

issn = "0920-9964",

publisher = "Elsevier",

}

TY - JOUR

T1 - Genetic analysis of deep phenotyping projects in common disorders

AU - Gershon, Elliot S.

AU - Pearlson, Godfrey

AU - Keshavan, Matcheri S.

AU - Tamminga, Carol

AU - Clementz, Brett

AU - Buckley, Peter F.

AU - Alliey-Rodriguez, Ney

AU - Liu, Chunyu

AU - Sweeney, John A.

AU - Keedy, Sarah

AU - Meda, Shashwath A.

AU - Tandon, Neeraj

AU - Shafee, Rebecca

AU - Bishop, Jeffrey R.

AU - Ivleva, Elena I.

PY - 2018/5

Y1 - 2018/5

N2 - 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.

AB - 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.

KW - Functional genomics

KW - Genetic analysis

KW - Imputation

KW - Multiple testing

KW - Phenotype

UR - http://www.scopus.com/inward/record.url?scp=85031759754&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031759754&partnerID=8YFLogxK

U2 - 10.1016/j.schres.2017.09.031

DO - 10.1016/j.schres.2017.09.031

M3 - Review article

AN - SCOPUS:85031759754

SN - 0920-9964

VL - 195

SP - 51

EP - 57

JO - Schizophrenia Research

JF - Schizophrenia Research

ER -

Genetic analysis of deep phenotyping projects in common disorders

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this