TITLE:
Genome-Wide Likelihood Ratio Tests under Heterogeneity
AUTHORS:
Xiaoxia Han, Yongzhao Shao
KEYWORDS:
Genetic Heterogeneity, Transmission Heterogeneity, Complex Disease, Genome-Wide Association Study, Genetic Linkage Analysis, R Software Package
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.3,
June
11,
2018
ABSTRACT: The commonly used statistical methods in medical
research generally assume patients arise from one homogeneous population.
However, the existence and importance of significant heterogeneity have been
widely documented. It is well known that common and complex human diseases
usually have heterogeneous disease etiology, which often involves interplay of
multiple genetic and environmental factors, leading to latent population
substructure. Genome-wide association studies (GWAS) is a useful tool to
uncover genetic association with disease of interest, while linkage analysis is
a commonly used method to identify statistical association between the
inheritance of a human disease and inheritance of marker loci that are in
linkage with disease causing loci. We propose a likelihood ratio test for
genome-wide linkage analysis under genetic heterogeneity using family data. We
derive a closed-form formula for the LRT test statistic and provide explicit
asymptotic null distribution. The closed form asymptotic distribution allows
easy determination of the asymptotic p-values. Our extensive simulation studies
indicate that the proposed test has proper type I error and good power under
genetic heterogeneity. In order to simplify application of the proposed method
for non-statisticians, we develop an R package gLRTH to implement the proposed LRT
for genome-wide linkage analysis as well as Qian and Shao’s LRT for GWAS under
heterogeneity. The newly developed open source R package gLRTH is available at
CRAN.