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High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas “gold-standard” empirical datasets exist for this purpose in humans, synthetic (i.e., simulated) sequencing data can offer important insights into the capabilities and limitations of computational pipelines for any arbitrary species and/or study design—yet, the ability of read simulator software to emulate genomic characteristics of empirical datasets remains poorly understood. We here compare the performance of six popular short-read simulators—ART, DWGSIM, InSilicoSeq, Mason, NEAT, and wgsim—and discuss important considerations for selecting suitable models for benchmarking.

Recent advances in genomics have enabled researchers to shed light on the evolutionary processes driving human adaptation, by revealing the genetic architectures underlying traits ranging from lactase persistence, to skin pigmentation, to hypoxic response, to arsenic tolerance. Complicating the identification of targets of positive selection in modern human populations is their complex demographic history, characterized by population bottlenecks and expansions, population structure, migration, and admixture. In particular, founder effects and recent strong population size reductions, such as those experienced by the indigenous peoples of the Americas, have severe impacts on genetic variation that can lead to the accumulation of large allele frequency differences between populations due to genetic drift rather than natural selection. While distinguishing the effects of demographic history from selection remains challenging, neglecting neutral processes can lead to the incorrect identification of candidate loci. We here review the recent population genomic insights into the genetic basis of arsenic tolerance in Andean populations, and utilize this example to highlight both the difficulties pertaining to the identification of local adaptations in strongly bottlenecked populations, as well as the importance of controlling for demographic history in selection scans.

The rate of aquatic invasions by planktonic organisms has increased considerably in recent decades. In order to effectively direct funding and resources to control the spread of such invasions, a methodological framework for identifying high-risk transport vectors, as well as ruling out vectors of lesser concern will be necessary. A number of estuarine ecosystems on the North American Pacific Northwest coast have experienced a series of high impact planktonic invasions that have slowly unfolded across the region in recent decades, most notably, that of the planktonic copepod crustacean Pseudodiaptomus inopinus. Although introduction of P. inopinus to the United States almost certainly occurred through the discharge of ballast water from commercial vessels originating in Asia (the species’ native range), the mechanisms and patterns of subsequent spread remain unknown. In order to elucidate the migration events shaping this invasion, we sampled the genomes of copepods from seven invasive and two native populations using restriction-site associated DNA sequencing. This genetic data was evaluated against spatially-explicit genetic simulation models to evaluate competing scenarios of invasion spread. Our results indicate that invasive populations of P. inopinus exhibit a geographically unstructured genetic composition, likely arising from infrequent and large migration events. This pattern of genetic patchiness was unexpected given the linear geographic structure of the sampled populations, and strongly contrasts with the clear invasion corridors observed in many aquatic systems.

Genes mutated in congenital malformation syndromes are frequently implicated in oncogenesis, but the causative germline and somatic mutations occur in separate cells at different times of an organism's life. Here we unify these processes to a single cellular event for mutations arising in male germ cells that show a paternal age effect. Screening of 30 spermatocytic seminomas for oncogenic mutations in 17 genes identified 2 mutations in FGFR3 (both 1948A>G, encoding K650E, which causes thanatophoric dysplasia in the germline) and 5 mutations in HRAS. Massively parallel sequencing of sperm DNA showed that levels of the FGFR3 mutation increase with paternal age and that the mutation spectrum at the Lys650 codon is similar to that observed in bladder cancer. Most spermatocytic seminomas show increased immunoreactivity for FGFR3 and/or HRAS. We propose that paternal age-effect mutations activate a common 'selfish' pathway supporting proliferation in the testis, leading to diverse phenotypes in the next generation including fetal lethality, congenital syndromes and cancer predisposition.