Combining genotype with allelic association as input for iterative pruning principal component analysis (ipPCA) to resolve population substructures

Single Nucleotide Polymorphisms (SNPs) are commonly used to capture variations between populations and often genome-wide SNP data are pruned based on linkage disequilibrium (LD) patterns. Notably, haplotype composition and the pattern of LD between markers may vary between larger populations but may also play a role within more confined geographic regions. Indeed, knowledge about haplotypes in unrelated individuals can reveal useful information about genetic ancestry. Here, we use iterative pruning principal component analysis (ipPCA) [Intarapanich 2009] to identify and characterize subpopulations in an unsupervised way using a rich set of genetic markers since using reduced sets of genetic markers for these purposes can become challenging, especially when similar geographic regions are involved or when spurious patterns are likely to exist. As input data, either pruned genome-wide SNP data are used or multilocus haplotype information derived from the genome-wide SNP panel. These approaches are applied to real-life data from 4028 Vietnamese individuals [Khor 2012]. Preliminary results indicate that ipPCA applied to pruned SNP data or ipPCA that explicitly uses multilocus information (haplotypes) give complementary information about population substructure for geographically confined populations. Both methods address different aspects of population structure. In conclusion, we propose to combine an LD-based haplotype encoding scheme with the ipPCA machinery to retrieve fine population substructures. Despite the complexities that are associated with haplotype inference, added value can be obtained when the LD structure between SNPs is exploited in the search for relevant population strata.