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Semiparametric Methods for Mapping Quantitative Trait Loci with Censored Data
Author(s) -
Diao Guoqing,
Lin D. Y.
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00346.x
Subject(s) - quantitative trait locus , censoring (clinical trials) , inference , statistical inference , computer science , trait , statistics , biology , computational biology , genetics , mathematics , gene , artificial intelligence , programming language
Summary Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure‐time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow‐up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure‐time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187–220) proportional hazards model and derive efficient likelihood‐based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.