title: Rank-based robust tests for quantitative-trait genetic association studies.
abstract: Standard linear regression is commonly used for genetic association studies of quantitative traits. This approach may not be appropriate if the trait, on its original or transformed scales, does not follow a normal distribution. A rank-based nonparametric approach that does not rely on any distributional assumptions can be an attractive alternative. Although several nonparametric tests exist in the literature, their performance in the genetic association setting is not well studied. We evaluate various nonparametric tests for the analysis of quantitative traits and propose a new class of nonparametric tests that have robust performance for traits with various distributions and under different genetic models. We demonstrate the advantage of our proposed methods through simulation study and real data applications.
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. liqz@amss.ac.cn
authors.forename: Qizhai
authors.initials: Q
authors.lastname: Li
authors.forename: Zhengbang
authors.initials: Z
authors.lastname: Li
authors.forename: Gang
authors.initials: G
authors.lastname: Zheng
authors.forename: Guimin
authors.initials: G
authors.lastname: Gao
authors.forename: Kai
authors.initials: K
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000483:Alleles; D002874:Chromosome Mapping; D056726:Genetic Association Studies / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D018709:Statistics, Nonparametric
chemical_list: D005819:Genetic Markers