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

Abstract

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 ofNC 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/036178 doi: bioRxiv preprint first posted online Jan. 7, 2016; 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.

An evaluation of the methods to estimate effective population size from measures of linkage disequilibrium

“…In agreement with previous studies (Hudson, 1985;Ober et al, 2013), our study also shows that LD alone is likely to result in biased estimates for freely recombining loci. In such cases, it could be combined with allelic frequencies or other statistics, as in recent methods which use whole-genome information (Boitard, Rodríguez, Jay, Mona, & Austerlitz, 2016;Terhorst, Kamm, & Song, 2017) to improve estimates. However, for a rapid estimate of N e only based on LD, Sved and Feldman (1973) and our TPM approach provide the most accurate estimate.…”

Genomics advances the study of inbreeding depression in the wild

“…Recently, other approaches have been developed to infer recent historical fluctuations in N e (i.e. over the past few hundred generations) using large-scale SNP or whole-genome resequencing data (Boitard, Rodriguez, Jay, Mona, & Austeritz, 2016;Browning & Browning, 2015;Palamara, Lencz, Darvasi, & Pe'er, 2012;Thompson, 2013). For example, Browning & Browning (2015) presented a method that uses the distribution of lengths of chromosome segments shared IBD between pairs of individuals (Browning & Browning, 2013) to estimate a time series of N e from a generation or two before sampling to a few hundred generations back in time.…”

Demographic model selection using random forests and the site frequency spectrum

“…While methods have been suggested to guide researchers in their choice of summary statistics (e.g., partial least-squares transformation ;Wegmann, Leuenberger, & Excoffier, 2009), they still result in a large decrease in the information content of the data. Some recent studies have used the bins of the site frequency spectrum (SFS) as a summary statistic for ABC inference (e.g., Boitard, Rodriguez, Jay, Mona, & Austerlitz, 2016;Prates, Rivera, Rodrigues, & Carnaval, 2016;Stocks, Siol, Lascoux, & De Mita, 2014;Xue & Hickerson, 2015), but these approaches have not taken advantages of joint or multidimensional SFS (mSFS). Consideration of the mSFS is necessary to make inferences about multiple populations, but the dimensionality of the mSFS increases as the number of individuals and populations sampled increases such that the number of bins in the joint or multidimensional SFS becomes very large, and the "curse of dimensionality" becomes a limiting factor.…”