Abstract
Comparing inferences among datasets generated using short read sequencing may provide insight into the concerted impacts of divergence, gene flow and selection across organisms, but comparisons are complicated by biases introduced during dataset assembly. Sequence similarity thresholds allow the de novo assembly of short reads into clusters of alleles representing different loci, but the resulting datasets are sensitive to both the similarity threshold used and to the variation naturally present in the organism under study. Thresholds that require high sequence similarity among reads for assembly (stringent thresholds) as well as highly variable species may result in datasets in which divergent alleles are lost or divided into separate loci (‘over-splitting’), whereas liberal thresholds increase the risk of paralogous loci being combined into a single locus (‘under-splitting’). Comparisons among datasets or species are therefore potentially biased if different similarity thresholds are applied or if the species differ in levels of within-lineage genetic variation. We examine the impact of a range of similarity thresholds on assembly of empirical short read datasets from populations of four different non-model bird lineages (species or species pairs) with different levels of genetic divergence. We find that, in all species, stringent similarity thresholds result in fewer alleles per locus than more liberal thresholds, which appears to be the result of high levels of over-splitting. The frequency of putative under-splitting, conversely, is low at all thresholds. Inferred genetic distances between individuals, gene tree depths, and estimates of the ancestral mutation-scaled effective population size (θ) differ depending upon the similarity threshold applied. Relative differences in inferences across species differ even when the same threshold is applied, but may be dramatically different when datasets assembled under different thresholds are compared. These differences not only complicate comparisons across species, but also preclude the application of standard mutation rates for parameter calibration. We suggest some best practices for assembling short read data to maximize comparability, such as using more liberal thresholds and examining the impact of different thresholds on each dataset.
Highlights
With the proliferation of population-level datasets obtained using massively parallel sequencing technologies, there is increasing interest in studies that compare inferences from genomic datasets obtained from different species (e.g., Leache et al, 2013; Smith et al, 2013) or from different genomic regions (e.g., Evans et al, 2014; Harvey et al, 2013; Leache et al, 2015)
That inferences differ among lineages with different population histories, and according to the similarity threshold applied during dataset assembly
Differences in the impact of similarity thresholds across datasets reduce the utility of those datasets for comparative studies, and preclude the application of standardized mutation rate estimates that would allow demographic parameters in non-model species to be converted to absolute values (DaCosta & Sorenson, 2014)
Summary
With the proliferation of population-level datasets obtained using massively parallel sequencing technologies, there is increasing interest in studies that compare inferences from genomic datasets obtained from different species (e.g., Leache et al, 2013; Smith et al, 2013) or from different genomic regions (e.g., Evans et al, 2014; Harvey et al, 2013; Leache et al, 2015). Of short sequence reads into orthologous loci is a key component of post-sequence processing, and commonly used methods can lead to biases in population genetic parameter estimation (Ilut, Nydam & Hare, 2014). Selecting the most appropriate similarity threshold is challenging, primarily because the amount of genetic (allelic) variation can vary greatly among orthologous loci within a species (Ilut, Nydam & Hare, 2014). Because the amount of genetic variation varies among species and genomic regions, a particular similarity threshold may impact each dataset differently, potentially influencing inferences in comparative studies
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