EigenGWAS is a simple linear regression method that has realized an unsupervised strategy for detection of loci under natural/artificial selection. EigenGWAS takes eigenvector generated from genomic data as response variable, regressing each marker against the eigenvector of study. After technical correction, the loci under selection are the genome-wide significant ones.
Applied populations: Outbred as well as inbred population.
Genotyping platforms: A wide range of genotyping platforms, chips, NGS, and GBS.
Subgroup information: Different from conventional Fst approach, subgroup information is not required in EigenGWAS. Actually, for many resequencing studies, precise subgroup information is unavailable.