No abstract

J.B.S. Haldane proposed in 1947 that the male germline may be more mutagenic than the female 1. Diverse studies have supported Haldane’s contention of a higher average mutation rate in the male germline in a variety of mammals, including humans (e.g. 2,3). Here we present the first direct comparative analysis of male and female germline mutation rates from complete genome sequences of two parent-offspring trios. Through extensive validation, we identified 49 and 35 germline de novo mutations (DNMs) in two trio offspring, as well as 1,586 non-germline DNMs arising either somatically or in the cell-lines from which DNA was derived. Most strikingly, in one family we observed that 92% of germline DNMs were from the paternal germline, while, in complete contrast, in the other family 64% of DNMs were from the maternal germline. These observations reveal considerable variation in mutation rates within and between families.

Understanding the molecular basis of repeatedly evolved phenotypes can yield key insights into the evolutionary process. Quantifying gene flow between populations is especially important in interpreting mechanisms of repeated phenotypic evolution, and genomic analyses have revealed that admixture occurs more frequently between diverging lineages than previously thought. In this study, we resequenced 47 whole genomes of the Mexican tetra from three cave populations, two surface populations, and outgroup samples. We confirmed that cave populations are polyphyletic and two Astyanax mexicanus lineages are present in our dataset. The two lineages likely diverged much more recently than previous mitochondrial estimates of 5–7mya. Divergence of cave populations from their phylogenetically closest surface population likely occurred between ~161k - 191k generations ago. The favored demographic model for most population pairs accounts for divergence with secondary contact and heterogeneous gene flow across the genome, and we rigorously identified gene flow among all lineages sampled. Therefore, the evolution of cave-related traits occurred more rapidly than previously thought, and trogolomorphic traits are maintained despite gene flow with surface populations. The recency of these estimated divergence events suggests that selection may drive the evolution of cave-derived traits, as opposed to disuse and drift. Finally, we show that a key trogolomorphic phenotype QTL is enriched for genomic regions with low divergence between caves, suggesting that regions important for cave phenotypes may be transferred between caves via gene flow. Our study shows that gene flow must be considered in studies of independent, repeated trait evolution.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

We present the DeNovoGear software for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis, and fragment information to identify the parental origin of germline mutations. We used our program to create a whole-genome de novo indel callset with a 95% validation rate, producing a direct estimate of the human germline indel mutation rate.