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A hybrid likelihood model for sequence-based disease association studies.

  • Academic Journal
  • Chen YC; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
    Carter H
    Parla J
    Kramer M
    Goes FS
    Pirooznia M
    Zandi PP
    McCombie WR
    Potash JB
    Karchin R
  • PLoS genetics [PLoS Genet] 2013; Vol. 9 (1), pp. e1003224. Date of Electronic Publication: 2013 Jan 24.
  • English
  • In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values < 0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.
    Competing Interests: The authors have declared that no competing interests exist.
Additional Information
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-7404 (Electronic) Linking ISSN: 15537390 NLM ISO Abbreviation: PLoS Genet Subsets: MEDLINE
Original Publication: San Francisco, CA : Public Library of Science, c2005-
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R01MH087979 United States MH NIMH NIH HHS; UL1 TR001105 United States TR NCATS NIH HHS; K01 MH093809 United States MH NIMH NIH HHS; R01 MH087979 United States MH NIMH NIH HHS; UL1 TR000451 United States TR NCATS NIH HHS; R01 MH087992 United States MH NIMH NIH HHS; R01MH087992 United States MH NIMH NIH HHS; R00 MH086049 United States MH NIMH NIH HHS; K01MH093809 United States MH NIMH NIH HHS
EC (Mitogen-Activated Protein Kinase Kinases)
Date Created: 20130130 Date Completed: 20130530 Latest Revision: 20211021