Limits and Convergence properties of the Sequentially Markovian Coalescent

Abstract

Many methods based on the Sequentially Markovian Coalescent (SMC) have been and are being developed. These methods make use of genome sequence data to uncover population demographic history. More recently, new methods even allow the simultaneous estimation of the demographic history and other biological variables, extending the original theoretical frameworks. Those methods can be applied to many different species, under different model assumptions, in hopes of unlocking the population/species evolutionary history. Although convergence proofs in particular cases have been given using simulated data, a clear outline of the performance limits of these methods is lacking. We here explore the limits of this methodology, as well as present a tool that can be used to help users quantify what information can be confidently retrieved from given datasets. In addition, we study the consequences for inference accuracy of the violation of hypotheses and assumptions of SMC approaches, such as the presence of transposable elements, variable recombination and mutation rates along the sequence and SNP call errors. We also provide a new interpretation of the SMC through the use of the estimated transition matrix and offer recommendations for the most efficient use of these methods under budget constraints, notably through the building of data sets that would be better adapted for the biological question at hand.

Heterogeneity in effective size across the genome: effects on the Inverse Instantaneous Coalescence Rate (IICR) and implications for demographic inference under linked selection

“…For instance, genomewide variations of the mutation rate may have similar effects on the data than genomewide variations of N e , because high mutation rates and large population sizes both lead to increase the number of polymorphic sites in a region. Consistent with our results, Sellinger et al (2021) showed that applying SMC methods to genomic sequences that were simulated with local variations of the mutation rate leads to infer spurious population size declines. Actually, a direct consequence of N e heterogeneity is to increase the variance of coalescence times along the genome (see the Supplementary Materials for a proof of this statement under panmixia).…”

Whole-genome re-sequencing data to infer historical demography and speciation processes in land snails: the study of twoCandidulasister species

“…Nevertheless, the obtained divergence time estimate concurs with previous mitochondrial estimates [22]. The Holocene population expansion of C. unifasciata inferred previously by phylogeographical methods [20] could not be resolved by PMSC with its limited power to infer very recent events [62].…”

Navigating the temporal continuum of effective population size

“…(2016) who found that high quality data (coverage >18x) are essential for proper inference. It has also been shown that high numbers of spurious SNPs (>10%) and unmasked repetitive sequences bias inference in several SMC methods (Sellinger et al., 2021). Many methods appear robust to the assembly quality (fragmentation) but their precision and accuracy differ across timescales (Patton et al., 2019)—often Ne for the most recent as well as ancient times is more difficult to infer.…”

“…An impressive number of new methods trying to improve resolution over recent timescales have been developed since the publication of the original PSMC study (Sellinger et al., 2021; Spence et al., 2018). In general, these methods improve the resolution by analysing genetic information from more individuals, from a few (Malaspinas et al., 2016; Schiffels & Durbin, 2014) to thousands (Speidel et al., 2019).…”