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High-throughput shotgun sequence data makes it possible in principle toaccurately estimate population genetic parameters without confounding by SNPascertainment bias. One such statistic of interest is the proportion ofheterozygous sites within an individual's genome, which is informative aboutinbreeding and effective population size. However, in many cases, the availablesequence data of an individual is limited to low coverage, preventing theconfident calling of genotypes necessary to directly count the proportion ofheterozygous sites. Here, we present a method for estimating an individual'sgenome-wide rate of heterozygosity from low-coverage sequence data, without anintermediate step calling genotypes. Our method jointly learns the sharedallele distribution between the individual and a panel of other individuals,together with the sequencing error distributions and the reference bias. Weshow our method works well, first by its performance on simulated sequencedata, and secondly on real sequence data where we obtain estimates using lowcoverage data consistent with those from higher coverage. We apply our methodto obtain estimates of the rate of heterozygosity for 11 humans from diverseworld-wide populations, and through this analysis reveal the complex dependencyof local sequencing coverage on the true underlying heterozygosity, whichcomplicates the estimation of heterozygosity from sequence data. We showfilters can correct for the confounding by sequencing depth. We find inpractice that ratios of heterozygosity are more interpretable than absoluteestimates, and show that we obtain excellent conformity of ratios ofheterozygosity with previous estimates from higher coverage data.

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