Over the past few decades, the rapid democratization of high-throughput
sequencing and the growing emphasis on open science practices have
resulted in an explosion in the amount of publicly available sequencing
data. This opens new opportunities for combining datasets to achieve
unprecedented sample sizes, spatial coverage, or temporal replication in
population genomic studies. However, a common concern is that
non-biological differences between datasets may generate batch effects
that can confound real biological patterns. Despite general awareness
about the risk of batch effects, few studies have examined empirically
how they manifest in real datasets, and it remains unclear what factors
cause batch effects and how to best detect and mitigate their impact
bioinformatically. In this paper, we compare two batches of low-coverage
whole genome sequencing (lcWGS) data generated from the same populations
of Atlantic cod (Gadus morhua). First, we show that with a
“batch-effect-naive” bioinformatic pipeline, batch effects severely
biased our genetic diversity estimates, population structure inference,
and selection scan. We then demonstrate that these batch effects
resulted from multiple technical differences between our datasets,
including the sequencing instrument model/chemistry, read type, read
length, DNA degradation level, and sequencing depth, but their impact
can be detected and substantially mitigated with simple bioinformatic
approaches. We conclude that combining datasets remains a powerful
approach as long as batch effects are explicitly accounted for. We focus
on lcWGS data in this paper, which may be particularly vulnerable to
certain causes of batch effects, but many of our conclusions also apply
to other sequencing strategies.
Batch effects in population genomic studies with low‐coverage whole genome sequencing data: causes, detection, and mitigation
