A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data.

Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverages the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole-genome polymorphism data sets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

Identifying and Classifying Shared Selective Sweeps from Multilocus Data.

Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying signatures of selection in the ancestor that are shared among its descendants is important to contextualize the timing of a sweep, but few methods exist for this purpose. We introduce the statistic SS-H12, which can identify genomic regions under shared positive selection across populations and is based on the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. SS-H12 is distinct from comparable statistics because it requires a minimum of only two populations, and properly identifies and differentiates between independent convergent sweeps and true ancestral sweeps, with high power and robustness to a variety of demographic models. Furthermore, we can apply SS-H12 in conjunction with the ratio of statistics we term [Formula: see text] and [Formula: see text] to further classify identified shared sweeps as hard or soft. Finally, we identified both previously reported and novel shared sweep candidates from human whole-genome sequences. Previously reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-Europeans, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan Africans involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses.

Detection and Classification of Hard and Soft Sweeps from Unphased Genotypes by Multilocus Genotype Identity.

Positive natural selection can lead to a decrease in genomic diversity at the selected site and at linked sites, producing a characteristic signature of elevated expected haplotype homozygosity. These selective sweeps can be hard or soft. In the case of a hard selective sweep, a single adaptive haplotype rises to high population frequency, whereas multiple adaptive haplotypes sweep through the population simultaneously in a soft sweep, producing distinct patterns of genetic variation in the vicinity of the selected site. Measures of expected haplotype homozygosity have previously been used to detect sweeps in multiple study systems. However, these methods are formulated for phased haplotype data, typically unavailable for nonmodel organisms, and some may have reduced power to detect soft sweeps due to their increased genetic diversity relative to hard sweeps. To address these limitations, we applied the H12 and H2/H1 statistics proposed in 2015 by Garud et al., which have power to detect both hard and soft sweeps, to unphased multilocus genotypes, denoting them as G12 and G2/G1. G12 (and the more direct expected homozygosity analog to H12, denoted G123) has comparable power to H12 for detecting both hard and soft sweeps. G2/G1 can be used to classify hard and soft sweeps analogously to H2/H1, conditional on a genomic region having high G12 or G123 values. The reason for this power is that, under random mating, the most frequent haplotypes will yield the most frequent multilocus genotypes. Simulations based on parameters compatible with our recent understanding of human demographic history suggest that expected homozygosity methods are best suited for detecting recent sweeps, and increase in power under recent population expansions. Finally, we find candidates for selective sweeps within the 1000 Genomes CEU, YRI, GIH, and CHB populations, which corroborate and complement existing studies.

Admixture and Ancestry Inference from Ancient and Modern Samples through Measures of Population Genetic Drift.

Methods that leverage the information about population history contained within the increasingly abundant genetic sequences of extant and extinct hominid populations are diverse in form and versatile in application. Here, we review key methods recently developed to detect and quantify admixture and ancestry in modern human populations. We begin with an overview of the f- and D-statistics, covering their conceptual principles and important applications, as well as any extensions developed for them. We then cover a combination of more recent and more complex methods for admixture and ancestry inference, discussing tests for direct ancestry between two populations, quantification of admixture in large data sets, and determination of admixture dates. These methods have revolutionized our understanding of human population history and highlight its complexity. Therefore, we emphasize that current methods may not capture this population history in its entirety but nonetheless provide a reasonable picture that is supported by data from multiple methods and from the historical record.

An Unbiased Estimator of Gene Diversity with Improved Variance for Samples Containing Related and Inbred Individuals of any Ploidy.

Gene diversity, or expected heterozygosity (H), is a common statistic for assessing genetic variation within populations. Estimation of this statistic decreases in accuracy and precision when individuals are related or inbred, due to increased dependence among allele copies in the sample. The original unbiased estimator of expected heterozygosity underestimates true population diversity in samples containing relatives, as it only accounts for sample size. More recently, a general unbiased estimator of expected heterozygosity was developed that explicitly accounts for related and inbred individuals in samples. Though unbiased, this estimator's variance is greater than that of the original estimator. To address this issue, we introduce a general unbiased estimator of gene diversity for samples containing related or inbred individuals, which employs the best linear unbiased estimator of allele frequencies, rather than the commonly used sample proportion. We examine the properties of this estimator, [Formula: see text] relative to alternative estimators using simulations and theoretical predictions, and show that it predominantly has the smallest mean squared error relative to others. Further, we empirically assess the performance of [Formula: see text] on a global human microsatellite dataset of 5795 individuals, from 267 populations, genotyped at 645 loci. Additionally, we show that the improved variance of [Formula: see text] leads to improved estimates of the population differentiation statistic, [Formula: see text] which employs measures of gene diversity within its calculation. Finally, we provide an R script, BestHet, to compute this estimator from genomic and pedigree data.

Characteristics, phenotype, and transmission of Wolbachia in the sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), and its parasitoid Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae).

Wolbachia is a common intracellular bacterial endosymbiont of insects, causing a variety of effects including reproductive manipulations such as cytoplasmic incompatibility (CI). In this study, we characterized Wolbachia in the whitefly Bemisia tabaci and in the whitefly parasitoid Eretmocerus sp. nr. emiratus. We also tested for horizontal transmission of Wolbachia between and within trophic levels, and we determined the phenotype of Wolbachia in E. sp. nr. emiratus. Using multilocus sequence typing and phylogenetic analyses, we found that B. tabaci and E. sp. nr. emiratus each harbor a different and unique strain of Wolbachia. Both strains belong to the phylogenetic supergroup B. No evidence for horizontal transmission of Wolbachia between and within trophic levels was found in our study system. Finally, crossing results were consistent with a CI phenotype; when Wolbachia-infected E. sp. nr. emiratus males mate with uninfected females, wasp progeny survival dropped significantly, and the number of females was halved. This is the first description of CI caused by Wolbachia in the economically important genus Eretmocerus. Our study underscores the expectation that horizontal transmission events occur rarely in the dynamics of secondary symbionts such as Wolbachia, and highlights the importance of understanding the effects of symbionts on the biology of natural enemies.