Coming to grips with complex disorders: Genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics

S. D. Patel, Helen Le-Niculescu, D. L. Koller, S. D. Green, Debomoy Lahiri, F. J. McMahon, John Nurnberger, Alexander Niculescu

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52 Citations (Scopus)

Abstract

We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animalmodel studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence fromother lines of work. In essence our analysis is themost comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European-American and African-American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond.

Original languageEnglish
Pages (from-to)850-877
Number of pages28
JournalAmerican Journal of Medical Genetics, Part B: Neuropsychiatric Genetics
Volume153
Issue number4
DOIs
StatePublished - Jun 2010

Fingerprint

Inborn Genetic Diseases
Genomics
Bipolar Disorder
Genome-Wide Association Study
Genes
Precision Medicine
Medical Genetics
African Americans
Single Nucleotide Polymorphism
Psychiatry
Gene Expression
Population
Datasets

Keywords

  • Bipolar disorder
  • Convergent functional genomics
  • Epistasis
  • Genetic risk
  • Pathways
  • Prediction

ASJC Scopus subject areas

  • Genetics(clinical)
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

Cite this

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title = "Coming to grips with complex disorders: Genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics",
abstract = "We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animalmodel studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence fromother lines of work. In essence our analysis is themost comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European-American and African-American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond.",
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AU - Patel, S. D.

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AU - Koller, D. L.

AU - Green, S. D.

AU - Lahiri, Debomoy

AU - McMahon, F. J.

AU - Nurnberger, John

AU - Niculescu, Alexander

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