Estimating heterozygosity from a low-coverage genome sequence, leveraging data from other individuals sequenced at the same sites

TL;DR

This paper introduces a novel method to estimate an individual's genome-wide heterozygosity from low-coverage sequencing data by jointly modeling shared allele distributions, sequencing errors, and biases, validated on simulated and real human data.

Contribution

The authors present a new approach that accurately estimates heterozygosity without calling genotypes, overcoming challenges of low coverage sequencing.

Findings

Method performs well on simulated data.

Estimates from low coverage data are consistent with high coverage.

Heterozygosity ratios are more reliable than absolute estimates.

Abstract

High-throughput shotgun sequence data makes it possible in principle to accurately estimate population genetic parameters without confounding by SNP ascertainment bias. One such statistic of interest is the proportion of heterozygous sites within an individual's genome, which is informative about inbreeding and effective population size. However, in many cases, the available sequence data of an individual is limited to low coverage, preventing the confident calling of genotypes necessary to directly count the proportion of heterozygous sites. Here, we present a method for estimating an individual's genome-wide rate of heterozygosity from low-coverage sequence data, without an intermediate step calling genotypes. Our method jointly learns the shared allele distribution between the individual and a panel of other individuals, together with the sequencing error distributions and the reference bias. We show our method works well, first by its performance on simulated sequence data, and secondly on real sequence data where we obtain estimates using low coverage data consistent with those from higher coverage. We apply our method to obtain estimates of the rate of heterozygosity for 11 humans from diverse world-wide populations, and through this analysis reveal the complex dependency of local sequencing coverage on the true underlying heterozygosity, which complicates the estimation of heterozygosity from sequence data. We show filters can correct for the confounding by sequencing depth. We find in practice that ratios of heterozygosity are more interpretable than absolute estimates, and show that we obtain excellent conformity of ratios of heterozygosity with previous estimates from higher coverage data.