- Award ID(s):
- 2001063
- NSF-PAR ID:
- 10187911
- Date Published:
- Journal Name:
- Genetics
- Volume:
- 215
- Issue:
- 1
- ISSN:
- 2027-5889
- Page Range / eLocation ID:
- 143-171
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Kim, Yuseob (Ed.)Abstract 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.more » « less
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Betancourt, Andrea (Ed.)Abstract Local adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We, therefore, used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window, or FST_MaxSNP) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that FST_MaxSNP had superior power to detect complete or mostly complete soft sweeps, but lesser power than full-window statistics to detect partial hard sweeps. Nonetheless, the power of FST_MaxSNP depended highly on sample size, and confident outliers depend on robust precautions and quality control. To investigate the relative enrichment of FST_MaxSNP outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that FST_MaxSNP had a genome-wide enrichment of outliers compared with demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that FST_MaxSNP is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.more » « less
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Abstract The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.
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