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 Embedded Image and Embedded Image 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.
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This content will become publicly available on July 1, 2024
Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics
Abstract Natural selection leaves a spatial pattern along the genome, with a haplotype distribution distortion near the selected locus that fades with distance. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and deep learning architectures. However, better predictions may be attainable by improving the way in which features are extracted from these summary statistics. We apply wavelet transform, multitaper spectral analysis, and S-transform to summary statistic arrays to achieve this goal. Each analysis method converts one-dimensional summary statistic arrays to two-dimensional images of spectral analysis, allowing simultaneous temporal and spectral assessment. We feed these images into convolutional neural networks and consider combining models using ensemble stacking. Our modeling framework achieves high accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets of varying sweep strength, softness, and timing. A scan of central European whole-genome sequences recapitulated well-established sweep candidates and predicted novel cancer-associated genes as sweeps with high support. Given that this modeling framework is also robust to missing genomic segments, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.
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- NSF-PAR ID:
- 10451213
- Editor(s):
- Kim, Yuseob
- Date Published:
- Journal Name:
- Molecular Biology and Evolution
- Volume:
- 40
- Issue:
- 7
- ISSN:
- 0737-4038
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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