Abstract
Background
It has been well established that theoretical kernel for recently surging genome-wide association
study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic
marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several
confounding factors of which population stratification is the most prominent. Whilst many methods
have been proposed to correct for the influence either through predicting the structure parameters or
correcting inflation in the test statistic due to the stratification, these may not be feasible or may
impose further statistical problems in practical implementation.
Methodology
We propose here a novel statistical method to control spurious LD in GWAS from population
structure by incorporating a control marker into testing for significance of genetic association of a
polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of
structure prediction which may be infeasible or inadequate in practice and accounts properly for a
varying effect of population stratification on different regions of the genome under study. Utility
and statistical properties of the new method were tested through an intensive computer simulation
study and an association-based genome-wide mapping of expression quantitative trait loci in
genetically divergent human populations.
Results/Conclusions
The analyses show that the new method confers an improved statistical power for detecting genuine
genetic association in subpopulations and an effective control of spurious associations stemmed
from population structure when compared with other two popularly implemented methods in the
literature of GWAS.
Year
2011
Category
Refereed journal