Anand Bhaskar | bioRxiv Science

bioRxiv2016

Effects of variable mutation rates and epistasis on the distribution of allele frequencies in humans

The site frequency spectrum (SFS) has long been used to study demographic history and natural selection. Here, we extend this summary by examining the SFS conditional on the alleles found at the same site in other species. We refer to this extension as the \"phylogenetically-conditioned SFS\" or cSFS. Using recent large-sample data from the Exome Aggregation Consortium (ExAC), combined with primate genome sequences, we find that human variants that occurred independently in closely related primate lineages are at higher frequencies in humans than variants with parallel substitutions in more distant primates. We show that this effect is largely due to sites with elevated mutation rates causing significant departures from the widely-used infinite sites mutation model. Our analysis also suggests substantial variation in mutation rates even among mutations involving the same nucleotide changes. We additionally find evidence for epistatic effects on the cSFS: namely, that parallel primate substitutions at nonsynonymous sites are more informative about constraint in humans when the parallel substitution occurs in a closely related species. In summary, we show that variable mutation rates and local sequence context are important determinants of the SFS in humans.

Genomics↗

bioRxiv2014

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.\n\nSoftwareWe have implemented the algorithms described in this paper in an open-source software package called fastNeutrino, which stands for fast Ne (effective population size) and mutation rate inference using analytic optimization. It will be publicly available at https://sourceforge.net/projects/fastneutrino.

Evolutionary Biology↗