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Abstract The rate at which mutations arise is a fundamental parameter of biology. Despite progress in measuring germline mutation rates across diverse taxa, such estimates are missing for much of Earth's biodiversity. Here, we present the first estimate of a germline mutation rate from the phylum Mollusca. We sequenced three pedigreed families of the white abalone Haliotis sorenseni, a long-lived, large-bodied, and critically endangered mollusk, and estimated a de novo mutation rate of 8.60 × 10−9 single nucleotide mutations per site per generation. This mutation rate is similar to rates measured in vertebrates with comparable generation times and longevity to abalone, and higher than mutation rates measured in faster-reproducing invertebrates. The spectrum of de novo mutations is also similar to that seen in vertebrate species, although an excess of rare C > A polymorphisms in wild individuals suggests that a modifier allele or environmental exposure may have once increased C > A mutation rates. We use our rate to infer baseline effective population sizes (Ne) across multiple Pacific abalone and find that abalone persisted over most of their evolutionary history as large and stable populations, in contrast to extreme fluctuations over recent history and small census sizes in the present day. We then use our mutation rate to infer the timing and pattern of evolution of the abalone genus Haliotis, which was previously unknown due to few fossil calibrations. Our findings are an important step toward understanding mutation rate evolution and they establish a key parameter for conservation and evolutionary genomics research in mollusks. 

Abstract Species tree inference from multilocus data has emerged as a powerful paradigm in the postgenomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown not only to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets. In this article, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data. 

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a ‘Mutationathon,’ a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.