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.

journal_name

Genet Epidemiol

journal_title

Genetic epidemiology

authors

Li Q,Li Z,Zheng G,Gao G,Yu K

doi

10.1002/gepi.21723

subject

Has Abstract

pub_date

2013-05-01 00:00:00

pages

358-65

issue

4

eissn

0741-0395

issn

1098-2272

journal_volume

37

pub_type

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