Inference of mutation parameters and selective constraint in mammalian coding sequences by approximate Bayesian computation

We develop an inference method that uses approximate Bayesian computation (ABC) to simultaneously estimate mutational parameters and selective constraint on the basis of nucleotide divergence for protein-coding genes between pairs of species. Our simulations explicitly model CpG hypermutability and transition vs. transversion mutational biases along with negative and positive selection operating on synonymous and nonsynonymous sites. We evaluate the method by simulations in which true mean parameter values are known and show that it produces reasonably unbiased parameter estimates as long as sequences are not too short and sequence divergence is not too low. We show that the use of quadratic regression within ABC offers an improvement over linear regression, but that weighted regression has little impact on the efficiency of the procedure. We apply the method to estimate mutational and selective constraint parameters in data sets of protein-coding genes extracted from the genome sequences of primates, murids, and carnivores. Estimates of CpG hypermutability are substantially higher in primates than murids and carnivores. Nonsynonymous site selective constraint is substantially higher in murids and carnivores than primates, and autosomal nonsynonymous constraint is higher than X-chromsome constraint in all taxa. We detect significant selective constraint at synonymous sites in primates, carnivores, and murid rodents. Synonymous site selective constraint is weakest in murids, a surprising result, considering that murid effective population sizes are likely to be considerably higher than the other two taxa.