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Bivariate combined linkage and association mapping of quantitative trait loci
Author(s) -
Jung Jeesun,
Zhong Ming,
Liu Lian,
Fan Ruzong
Publication year - 2008
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20313
Subject(s) - bivariate analysis , quantitative trait locus , linkage (software) , trait , biology , family based qtl mapping , genetics , association (psychology) , association mapping , genetic linkage , statistics , genetic association , mathematics , gene mapping , single nucleotide polymorphism , psychology , computer science , genotype , gene , chromosome , programming language , psychotherapist
In this paper, bivariate/multivariate variance component models are proposed for high‐resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, “genotype effect model” and “additive effect model”, are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the “genotype effect model” can be used to model the additive and dominant effects simultaneously; the “additive effect model” only takes care of additive effect. Based on the two models, F ‐test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate‐sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models. Genet. Epidemiol . 2008. © 2008 Wiley‐Liss, Inc.