Dealing with genetic (sub)structure in GWAS
|Submitted:||Friday 6th of July 2012 11:03:46 AM|
|Content type:||Learning resource|
|Educational levels:||qc2, qc3|
AbstractGenome-Wide Association Studies (GWAS) is a powerful tool for identifying loci involved in the control of complex traits. In most GWAS, study participants are assumed to be unrelated and coming from a single population. However, even for carefully designed studies, some degree of relatedness and population stratification is expected. This problem is even more pronounced in studies embed in genetically isolated populations. The genetic origin of study participants acts as a confounder, which, if not accounted for properly, induces false-positives and reduces power of genetic association analysis. In this talk, I will review statistical methods used to account for sub-structure in genetic association analyses, with an emphasize on one of the most flexible and powerful methods -- mixed-models (MM) based variance components approach. I will describe several statistical and computational approaches allowing substantial speed-up in MM-based computations.
This resource is part of the following learning package:
Y. Aulchenko. Dealing with genetic (sub)structure in GWAS. EUROGENE portal. July 2012. online: http://eurogene.open.ac.uk/content/dealing-genetic-substructure-gwas
Keywordsadditive, allele, association, base, confounder, correlation, drift, frequency, genetic isolates, genome, genome wide association, genotype, heritability, ibs, identity by descent, linkage disequilibrium, locus, marker, merlin, mixed model, pedigree, phenotype, polygenic, population, population isolate, power, quantitative trait, regression, segregation, selection, single nucleotide polymorphism, test statistics, threshold, trait, variant, variation, vector
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