Title: Conditional asymptotic inference for the kernel association test.

Authors: Wang, Kai

Published In Bioinformatics, (2017 Dec 01)

Abstract: MOTIVATION: The kernel association test (KAT) is popular in biological studies for its ability to combine weak effects potentially of opposite direction. Its P-value is typically assessed via its (unconditional) asymptotic distribution. However, such an asymptotic distribution is known only for continuous traits and for dichotomous traits. Furthermore, the derived P-values are known to be conservative when sample size is small, especially for the important case of dichotomous traits. One alternative is the permutation test, a widely accepted approximation to the exact finite sample conditional inference. But it is time-consuming to use in practice due to stringent significance criteria commonly seen in these analyses. RESULTS: Based on a previous theoretical result a conditional asymptotic distribution for the KAT is introduced. This distribution provides an alternative approximation to the exact distribution of the KAT. An explicit expression of this distribution is provided from which P-values can be easily computed. This method applies to any type of traits. The usefulness of this approach is demonstrated via extensive simulation studies using real genotype data and an analysis of genetic data from the Ocular Hypertension Treatment Study. Numerical results showed that the new method can control the type I error rate and is a bit conservative when compared to the permutation method. Nevertheless the proposed method may be used as a fast screening method. A time-consuming permutation procedure may be conducted at locations that show signals of association. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method is provided in the R package iGasso. CONTACT: kai-wang@uiowa.edu.

PubMed ID: 28961861

MeSH Terms: Genotype; Humans; Machine Learning*; Ocular Hypertension/genetics