title: A semiparametric model for vQTL mapping.
abstract: Quantitative trait locus analysis has been used as an important tool to identify markers where the phenotype or quantitative trait is linked with the genotype. Most existing tests for single locus association with quantitative traits aim at the detection of the mean differences across genotypic groups. However, recent research has revealed functional genetic loci that affect the variance of traits, known as variability-controlling quantitative trait locus. In addition, it has been suggested that many genotypes have both mean and variance effects, while the mean effects or variance effects alone may not be strong enough to be detected. The existing methods accounting for unequal variances include the Levene's test, the Lepage test, and the D-test, but suffer from their limitations of lack of robustness or lack of power. We propose a semiparametric model and a novel pairwise conditional likelihood ratio test. Specifically, the semiparametric model is designed to identify the combined differences in higher moments among genotypic groups. The pairwise likelihood is constructed based on conditioning procedure, where the unknown reference distribution is eliminated. We show that the proposed pairwise likelihood ratio test has a simple asymptotic chi-square distribution, which does not require permutation or bootstrap procedures. Simulation studies show that the proposed test performs well in controlling Type I errors and having competitive power in identifying the differences across genotypic groups. In addition, the proposed test has certain robustness to model mis-specifications. The proposed test is illustrated by an example of identifying both mean and variances effects in body mass index using the Framingham Heart Study data.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
authors.forename: Chuan
authors.initials: C
authors.lastname: Hong
authors.affiliation: Department of Statistical Science, Cornell University, Ithaca, New York 14853, U.S.A.
authors.forename: Yang
authors.initials: Y
authors.lastname: Ning
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
authors.forename: Peng
authors.initials: P
authors.lastname: Wei
authors.affiliation: Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas 77030, U.S.A.
authors.forename: Ying
authors.initials: Y
authors.lastname: Cao
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Philadelphia 19104, U.S.A.
authors.forename: Yong
authors.initials: Y
authors.lastname: Chen
mesh_terms: D016009:Chi-Square Distribution; D002874:Chromosome Mapping; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D040641:Quantitative Trait Loci
keywords: Composite likelihood; Conditional likelihood; Exponential tilt model; Pseudolikelihood; Semiparametric model; VQTL