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Using ancestry matching to combine family‐based and unrelated samples for genome‐wide association studies
Author(s) -
Crossett Andrew,
Kent Brian P.,
Klei Lambertus,
Ringquist Steven,
Trucco Massimo,
Roeder Kathryn,
Devlin Bernie
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4057
Subject(s) - genetic genealogy , proband , genome wide association study , biology , genetic association , genetics , genotype , matching (statistics) , snp , allele , association test , conditional logistic regression , type i and type ii errors , regression , logistic regression , association (psychology) , statistics , multiple comparisons problem , case control study , single nucleotide polymorphism , mathematics , mutation , demography , gene , psychology , population , psychotherapist , sociology
We propose a method to analyze family‐based samples together with unrelated cases and controls. The method builds on the idea of matched case–control analysis using conditional logistic regression (CLR). For each trio within the family, a case (the proband) and matched pseudo‐controls are constructed, based upon the transmitted and untransmitted alleles. Unrelated controls, matched by genetic ancestry, supplement the sample of pseudo‐controls; likewise unrelated cases are also paired with genetically matched controls. Within each matched stratum, the case genotype is contrasted with control/pseudo‐control genotypes via CLR, using a method we call matched ‐CLR (mCLR). Eigenanalysis of numerous SNP genotypes provides a tool for mapping genetic ancestry. The result of such an analysis can be thought of as a multidimensional map, or eigenmap, in which the relative genetic similarities and differences amongst individuals is encoded in the map. Once constructed, new individuals can be projected onto the ancestry map based on their genotypes. Successful differentiation of individuals of distinct ancestry depends on having a diverse, yet representative sample from which to construct the ancestry map. Once samples are well‐matched, mCLR yields comparable power to competing methods while ensuring excellent control over Type I error. Copyright © 2010 John Wiley & Sons, Ltd.