Jan 8, 2015

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

Genome Research
Anand BhaskarYun S Song


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 that is difficult to pick up with small sample sizes. Lastly, we use our method to analyze data from recent sequencing studies, including a large-sample exome-sequenc...Continue Reading

  • References
  • Citations18


  • We're still populating references for this paper, please check back later.

Mentioned in this Paper

Human Genetics
Genetics, Population
Whole Exome Sequencing
Cell Differentiation Process

Related Feeds

BioHub - Researcher Network

The Chan-Zuckerberg Biohub aims to support the fundamental research and develop the technologies that will enable physicians to cure, prevent, or manage all diseases in our childrens' lifetimes. The CZ Biohub brings together researchers from UC Berkeley, Stanford, and UCSF. Find the latest research from the CZ Biohub researcher network here.