A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts.
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ABSTRACT: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case-control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc.We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson's disease (PD) case-control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size?
SUBMITTER: Wang M
PROVIDER: S-EPMC3626840 | biostudies-literature | 2013 Feb
REPOSITORIES: biostudies-literature
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