Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in nonmodel species

Abstract

Approximate Bayesian computation (ABC) is widely used to infer demographic history of populations and species using DNA markers. Genomic markers can now be developed for nonmodel species using reduced representation library (RRL) sequencing methods that select a fraction of the genome using targeted sequence capture or restriction enzymes (genotyping-by-sequencing, GBS). We explored the influence of marker number and length, knowledge of gametic phase, and tradeoffs between sample size and sequencing depth on the quality of demographic inferences performed with ABC. We focused on two-population models of recent spatial expansion with varying numbers of unknown parameters. Performing ABC on simulated data sets with known parameter values, we found that the timing of a recent spatial expansion event could be precisely estimated in a three-parameter model. Taking into account uncertainty in parameters such as initial population size and migration rate collectively decreased the precision of inferences dramatically. Phasing haplotypes did not improve results, regardless of sequence length. Numerous short sequences were as valuable as fewer, longer sequences, and performed best when a large sample size was sequenced at low individual depth, even when sequencing errors were added. ABC results were similar to results obtained with an alternative method based on the site frequency spectrum (SFS) when performed with unphased GBS-type markers. We conclude that unphased GBS-type data sets can be sufficient to precisely infer simple demographic models, and discuss possible improvements for the use of ABC with genomic data.

Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds

“…(2017), including 252 gray seals and 55 harbor seals of good to excellent DNA quality (Table 1). The strategy of larger sample sizes, at the expense of lower read depth per sample, was selected based on the recommendation of prior simulation studies that suggest better performance for demographic estimation (Elleouet & Aitken, 2018) and inferring diversity and population structure (Fumagalli, 2013). The difference in total number of samples between gray and harbor seals reflects the geographic and temporal expanse of sampling and is driven by sample availability from prior long‐term studies; however, the number of samples per cohort was approximately equivalent across sites and species.…”

“…From such simulations, we have learned how factors such as mating systems (Armburster & Pfenninger, 2003), epistasis (Turelli & Barton, 2006), baseline allele frequency distributions (Luikart, Allendorf, Cornuet, & Sherwin, 1998), intensity and length of bottleneck (England et al., 2003), and timing of recovery (Hoban, Gaggiotti, & Bertorelle, 2013) are likely to affect the loss of genetic diversity during bottlenecks. More recent studies that compare the power of traditional and genomic markers to detect bottlenecks also show us that the type and amount of data and the selected model parameters can significantly impact conclusions (Cabrera & Palsbøll, 2017; Elleouet & Aitken, 2018; Hoban et al., 2013; Peery et al., 2012; Shafer, Gattepaille, Stewart, & Wolf, 2015). While advances in computational power have enabled increasingly complex demographic models and the incorporation of Bayesian approaches provide more nuanced ways to draw predictions and interpret uncertainty, simulated datasets inherently lack natural variability that without a doubt influences these processes in the natural environment.…”

Double-digest RAD-sequencing: do wet and dry protocol parameters impact biological results?

“…To estimate the influence of the m and M values on RAD tag fragment recovery, we determined the number of reconstructed fragments, their mean coverage and the proportion of polymorphic fragments for each value of M and m tested. We also evaluated the impact of these parameters on population genetics results by performing, for all m and M values, some of the most commonly used analyses using ddRADseq data (Capblancq et al, 2015; Kjeldsen et al, 2016; Black et al, 2017; Nunziata et al, 2017; Settepani et al, 2017; Elleouet & Aitken, 2018; Sherpa, Rioux, Pougnet-Lagarde, et al, 2018), i.e. mean individual heterozygosity, F ST among populations (estimated with the adegenet R package (Jombart, 2008)), Principal Component Analysis (PCA, using the adegenet R package (Jombart, 2008)), genetic structure with sNMF (using the LEA R package (Frichot & François, 2015)) and evolutionary history reconstruction using Approximate Bayesian Computation (performed with the diyABC program (Cornuet et al, 2014)).…”

“…Among these, double-digest RADseq, or ddRADseq (Peterson et al, 2012), is highly customizable as regards the final number of loci, depending on the choice of enzymes and range of fragment size selected. The ddRADseq approach has been applied with success to many purposes including population genetic studies (Kjeldsen et al, 2016; Black, Seears, Hollenbeck, & Samollow, 2017; Sherpa, Rioux, Goindin, et al, 2018), phylogenetic reconstructions (DaCosta & Sorenson, 2016; Vargas, Ortiz, & Simpson, 2017; Boubli et al, 2018; Lee et al, 2018; Sherpa, Rioux, Pougnet-Lagarde, & Després, 2018), demographic inferences (Capblancq, Després, Rioux, & Mavárez, 2015; Nunziata, Lance, Scott, Lemmon, & Weisrock, 2017; Settepani et al, 2017; Elleouet & Aitken, 2018) and landscape genetic analyses (Saenz-Agudelo et al, 2015; Johnson, Gaddis, Cairns, Konganti, & Krutovsky, 2017). Despite the recognized advantages of the ddRADseq technique, several limitations and weaknesses arose in the literature (Davey et al, 2013; K. R. Andrews et al, 2016; Lowry et al, 2017).…”

Coalescent models characterize sources and demographic history of recent round goby colonization of Great Lakes and inland waters

“…Next-generation sequencing technologies, including massively parallel sequencing of reduced representation genomic libraries (Andrews & Luikart, 2014), have progressed to the point that it is now possible to genotype hundreds of individuals at thousands of loci. Encouragingly, simulation studies suggest that aspects of colonization history can be correctly inferred in as few as 10 generations when using genomic-scale datasets in an ABC framework (Elleouet & Aitken, 2018 (Jude et al, 1992), as a result of human-assisted movements in the ballast water of container ships originating from the Black and Caspian Seas (Brown & Stepien, 2009). Round Gobies were observed in the St. Clair (STC) River in 1990, but by 1993 they had established populations near shipping ports in southern Lake Michigan (LKM) near Chicago, Illinois and central Lake Erie (LKE), near Cleveland, Ohio (Fuller et al, 2018).…”