Biology subjects

Simon Boitard

Publications and source records attributed to Simon Boitard.

2 recordsLinked to original sources

bioRxiv2016

Inferring population size history from large samples of genome wide molecular data - an approximate Bayesian computation approach

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.

Genetics↗

bioRxiv2015

On the importance of being structured: instantaneous coalescence rates and a re-evaluation of human evolution

Most species are structured and influenced by processes that either increased or reduced gene flow between populations. However, most population genetic inference methods ignore population structure and reconstruct a history characterized by population size changes under the assumption that species behave as panmictic units. This is potentially problematic since population structure can generate spurious signals of population size change. Moreover, when the model assumed for demographic inference is misspecified, genomic data will likely increase the precision of misleading if not meaningless parameters. In a context of model uncertainty (panmixia versus structure) genomic data may thus not necessarily lead to improved statistical inference.\n\nWe consider two haploid genomes and develop a theory which explains why any demographic model (with or without population size changes) will necessarily be interpreted as a series of changes in population size by inference methods ignoring structure. We introduce a new parameter, the IICR (inverse instantaneous coalescence rate), and show that it is equivalent to a population size only in panmictic models, and mostly misleading for structured models. We argue that this general issue affects all population genetics methods ignoring population structure. We take the PSMC method as an example and show that it infers population size changes that never took place. We apply our approach to human genomic data and find a reduction in gene flow at the start of the Pleistocene, a major increase throughout the Middle-Pleistocene, and an abrupt disconnection preceding the emergence of modern humans.

Evolutionary Biology↗