Benchmarking Imputed Low Coverage Genomes in a Human Population Genetics Context.

Abstract

Ongoing advances in population genomic methodologies have recently enabled the study of millions of loci across hundreds of genomes at a relatively low cost, by leveraging a combination of low-coverage shotgun sequencing and innovative genotype imputation methods. This approach has the potential to provide abundant genotype information at low costs comparable to another widely used cost-effective genotyping approach-that is, SNP panels-while avoiding potential issues related to loci being ascertained in distantly related populations. Nonetheless, the wide adoption of imputation methods in humans and other species is currently constrained by the lack of publicly available reference panels that capture diversity representative of the target genomes-though the recent development of 'joint' imputation approaches, which allow genetic information from the target population to be used in genotype calling, may potentially mitigate this shortcoming. Here, we assess the performance of multiple genotyping approaches on eight low coverage genomes (range ~3× to ~5×) sourced from different Indonesian populations-including a joint imputation approach that leverages 248 additional low coverage genomes (mean ~2.4×) from related populations. The inclusion of these related genomes in the joint imputation process resulted in more accurate genotype calls and produced population genetic inferences with similar accuracy but improved precision compared to pseudohaploid calls-even though the reference panel was only weakly representative of the target genomes. These results highlight the enormous potential of joint imputation to enable economical population genetic research for taxa that are currently poorly represented in publicly available reference panels.