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A Population‐based Latent Variable Approach for Association Mapping of Quantitative Trait Loci
Author(s) -
Wang Tao,
Weir Bruce,
Zeng ZhaoBang
Publication year - 2006
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2006.00264.x
Subject(s) - quantitative trait locus , family based qtl mapping , haplotype , inclusive composite interval mapping , association mapping , biology , genetics , population , genetic association , expectation–maximization algorithm , penetrance , latent variable model , latent variable , allele , gene mapping , statistics , genotype , single nucleotide polymorphism , maximum likelihood , mathematics , gene , phenotype , demography , sociology , chromosome
Summary A population‐based latent variable approach is proposed for association mapping of quantitative trait loci (QTL), using multiple closely linked genetic markers within a small candidate region in the genome. By incorporating QTL as latent variables into a penetrance model, the QTL are flexible to characterize either alleles at putative trait loci or potential risk haplotypes/sub‐haplotypes of the markers. Under a general likelihood framework, we develop an EM‐based algorithm to estimate genetic effects of the QTL and haplotype frequencies of the QTL and markers jointly. Closed form solutions derived in the maximization step of the EM procedure for updating the joint haplotype frequencies of QTL and markers can effectively reduce the computational intensity. Various association measures between QTL and markers can then be derived from the haplotype frequencies of markers and used to infer QTL positions. The likelihood ratio statistic also provides a joint test for association between a quantitative trait and marker genotypes without requiring adjustment for the multiple testing. Extensive simulation studies are performed to evaluate the approach.