Although the utility of short tandem repeats on the Y-chromosome (Y-STRs) has long been recognized and leveraged in forensics, genealogy and paternity testing, the bulk of these applications have relied on only a few dozen loci identified as having remarkably high mutation rates. Recent efforts have expanded the set of Y-STRs with known mutation rates to two hundred markers, but the limited throughput of the capillary method for estimating mutation rates has left the mutability of most Y-STRs uncharacterized, particularly those with dinucleotide repeat units. To address this limitation, we developed a novel method capable of concurrently estimating the mutation rates of all Y-STRs by leveraging population-scale whole-genome sequencing data. Extensive simulations confirmed that our method robustly accounts for PCR stutter artifacts and obtains unbiased mutation rate estimates. Application of the method to orthogonal datasets from the 1000 Genomes Project and Simons Genome Diversity Project utilized evolutionary data from over 250,000 meioses to estimate the mutation rates of more than 700 Y-STRs with 2-6 base pair repeat units, yielding the largest such set to date. Comparison of these estimates with those from father-son studies indicated a high degree of concordance for loci that have been previously characterized. In addition, we identified nearly 100 previously uncharacterized Y-STRs with per-generation mutation rates greater than 1 in 3000. Altogether, our study provides a broadly applicable method for estimating Y-STR mutation rates from whole-genome sequencing cohorts, outlines a framework for imputing Y-STRs, vastly expands the number of identified loci with high discriminative power and provides the first chromosome-wide characterization of the mutation rates of dinucleotide short tandem repeats.

How should fitness be measured to determine which phenotype or “strategy” is uninvadable when evolution occurs in subdivided populations subject to local demographic and environmental heterogeneity? Several invasion fitness measures, such as basic reproductive number, lifetime dispersal success of a local lineage, or inclusive fitness have been proposed to address this question, but the relationships between them and their generality remains unclear. Here, we ascertain uninvadability (all mutant strategies always go extinct) in terms of the growth rate of a mutant allele arising as a single copy in a population. We show from this growth rate that uninvadability is equivalently characterized by at least three conceptually distinct invasion fitness measures: (i) lineage fitness, giving the average personal fitness of a randomly sampled mutant lineage member; (ii) inclusive fitness, giving a reproductive value weighted average of the direct fitness cost and relatedness weighted indirect fitness benefits accruing to a randomly sampled mutant lineage member; and (iii) three types of reproductive numbers, giving lifetime success of a local lineage. Our analysis connects approaches that have been deemed different, generalizes the exact version of inclusive fitness to class-structured populations, and provides a biological interpretation of selection on a mutant allele under arbitrary strength of selection.

Adaptation depends on the rates, effects, and interactions of many mutations. We analyzed 264 genomes from 12 Escherichia coli populations to characterize their dynamics over 50,000 generations. The trajectories for genome evolution in populations that retained the ancestral mutation rate fit a model where most fixed mutations are beneficial, the fraction of beneficial mutations declines as fitness rises, and neutral mutations accumulate at a constant rate. We also compared these populations to lines evolved under a mutation-accumulation regime that minimizes selection. Nonsynonymous mutations, intergenic mutations, insertions, and deletions are overrepresented in the long-term populations, supporting the inference that most fixed mutations are favored by selection. These results illuminate the shifting balance of forces that govern genome evolution in populations adapting to a new environment.

Background: Gene-gene and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human associations studies remains challenging for myriad reasons. In the case of gene-gene interactions, the large number of potential interacting pairs presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies. Results: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry (θ) in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, identifying nine interactions that were significant at a threshold of . We replicate two of these interactions and show that a third has previously been identified in a genetic interaction screen for rheumatoid arthritis. Conclusion: We show that genetic ancestry can be a useful proxy for unknown and unmeasured environmental exposures with which it is correlated

Abstract. Reads from paired-end and mate-pair libraries are often utilized to find structural variation in genomes, and one common approach is to use their fragment length for detection. After aligning read-pairs to the reference, read-pair distances are analyzed for statistically significant deviations. However, previously proposed methods are based on a simplified model of observed fragment lengths that does not agree with data. We show how this model limits statistical analysis of identifying variants and propose a new model, by adapting a model we have previously introduced for contig scaffolding, which agrees with data. From this model we derive an improved improved null hypothesis that, when applied in the variant caller CLEVER, reduces the number of false positives and corrects a bias that contributes to more deletion calls than insertion calls. A reference implementation is freely available at https://github.com/ksahlin/GetDistr.

Software for rapid, accurate, and comprehensive microbial profiling of metagenomic sequence data on a desktop will play an important role in large scale clinical use of metagenomic data. Here we describe LMAT-ML (Livermore Metagenomics Analysis Toolkit-Marker Library) which can be run with 24 GB of DRAM memory, an amount available on many clusters, or with 16 GB DRAM plus a 24 GB low cost commodity flash drive (NVRAM), a cost effective alternative for desktop or laptop users. We compared results from LMAT with five other rapid, low-memory tools for metagenome analysis for 131 Human Microbiome Project samples, and assessed discordant calls with BLAST. All the tools except LMAT-ML reported overly specific or incorrect species and strain resolution of reads that were in fact much more widely conserved across species, genera, and even families. Several of the tools misclassified reads from synthetic or vector sequence as microbial or human reads as viral. We attribute the high numbers of false positive and false negative calls to a limited reference database with inadequate representation of known diversity. Our comparisons with real world samples show that LMAT-ML is the only tool tested that classifies the majority of reads, and does so with high accuracy.