Journal
GENETICS
Volume 203, Issue 1, Pages 493-+Publisher
GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.116.187278
Keywords
Bayesian inference; diffusion theory; natural selection; path augmentation
Categories
Funding
- National Science Foundation (NSF) [DBI-1402120]
- National Institutes of Health (NIH) [R01-GM40282]
- NSF [DMS-0907630, DMS-1512933]
- NIH [1R01GM109454-01]
- Direct For Mathematical & Physical Scien [0907630] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [0907630] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1512933] Funding Source: National Science Foundation
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The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in nonequilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.
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