A tailored variant filtering procedure for multi-breed and multi-species unbalanced animal SNP collections

Abstract

Technological advancements and decreasing costs of whole-genome sequencing have generated a huge amount of resequencing data. Large-sized datasets, spanning several species and/or populations can now be assembled easily. However, they vary widely in geographical provenance, and sample sizes, with taxonomic groups varying from single to hundreds of entries. Consequently, standard filtering approaches may bias the representation of groups or gene pools. Commonly adopted variant filters relying on minor allele frequency (MAF) and linkage disequilibrium (LD) are not adequate because LD patterns and allele frequencies differ substantially within datasets representing both local and global diversity of multiple populations and species. Thus, by using the VarGoats 1000 goat genome project, we devised a novel approach which avoids the biases of the standard filtering procedures by adopting within-population subsampling, minor allele count (MAC) and marker spacing (bp-space) as filters. Starting from a quality-filtered dataset of >28M SNPs from 1372 animals, we generated a dataset of <14M markers and 750 individuals, complying with the initial requirements and facilitating further computational steps.