Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data

High-throughput sequencing methods that multiplex a large number of individuals have provided a cost-effective approach for discovering genome-wide genetic variation in large populations. These sequencing methods are increasingly being utilized in population genetic studies across a diverse range of species. Two side-effects of these methods, however, are (1) sequencing errors and (2) heterozygous genotypes called as homozygous due to only one allele at a particular locus being sequenced, which occurs when the sequencing depth is insufficient. Both of these errors have a profound effect on the estimation of linkage disequilibrium (LD) and, if not taken into account, lead to inaccurate estimates. We developed a new likelihood method, GUS-LD, to estimate pairwise linkage disequilibrium using low coverage sequencing data that accounts for undercalled heterozygous genotypes and sequencing errors. Our findings show that accurate estimates were obtained using GUS-LD, whereas underestimation of LD results if no adjustment is made for the errors.