Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus

Approximate Bayesian Computation (ABC) is a powerful tool for model-based
inference of demographic population histories from large genetic data
sets. For most organisms its implementation has been hampered by the lack
of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap
genome-scale data to fill this gap, but its potential has not fully been
exploited. Here, we explored power, precision and biases of a
coalescent-based ABC approach where GBS data were modeled with either a
population mutation parameter (θ) or with a fixed sites (FS) approach,
allowing single or several segregating sites per locus. With simulated
data ranging from 500 to 50,000 loci a variety of demographic models could
be reliably inferred across a range of timescales and migration scenarios.
Posterior estimates were informative with 1,000 loci for migration and
split time in simple population divergence models. In more complex models
posterior distributions were wide and almost reverted to the uninformative
prior even with 50,000 loci. ABC parameter estimates, however, were
generally more accurate than an alternative composite-likelihood method.
Bottleneck scenarios proved particularly difficult and only recent
bottlenecks without recovery could be reliably detected and dated.
Notably, minor allele frequency filters – usual practice for GBS data –
negatively affected nearly all estimates. With this in mind, we used a
combination of FS and θ approaches on empirical GBS data generated from
the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing
support for a population split before the last glacial maximum followed by
asymmetrical migration and a range-wide bottleneck. Overall, this study
evaluates the potential and limitations of GBS data in an ABC-coalescence
framework and proposes a best-practice approach.