Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful
and cost-effective approach for population genomic studies in both model
and non-model species. However, with read depths too low to confidently
call individual genotypes, lcWGS requires specialized analysis tools
that explicitly account for genotype uncertainty. A growing number of
such tools have become available, but it can be difficult to get an
overview of what types of analyses can be performed reliably with lcWGS
data, and how the distribution of sequencing effort between the number
of samples analyzed and per-sample sequencing depths affects inference
accuracy. In this introductory guide to lcWGS, we first illustrate how
the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq
in many systems. We then provide an overview of software packages that
explicitly account for genotype uncertainty in different types of
population genomic inference. Next, we use both simulated and empirical
data to assess the accuracy of allele frequency and genetic diversity
estimation, detection of population structure, and selection scans under
different sequencing strategies. Our results show that spreading a given
amount of sequencing effort across more samples with lower depth per
sample consistently improves the accuracy of most types of inference,
with a few notable exceptions. Finally, we assess the potential for
using imputation to bolster inference from lcWGS data in non-model
species, and discuss current limitations and future perspectives for
lcWGS-based population genomics research. With this overview, we hope to
make lcWGS more approachable and stimulate its broader adoption.
Population Genomic- and Phylogenomic-Enabled Advances to Increase Insight Into Pathogen Biology and Epidemiology
