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Fast and robust detection of ancestral selective sweeps
Xiaoheng Cheng1,2, Cheng Xu1, Michael DeGiorgio2,3,4
1Huck Institutes of Life Sciences, Pennsylvania State University, University Park, PA, USA.
Molecular Ecology
|November 8, 2017
Summary
We developed a new method, the ancestral branch statistic (ABS), to detect positive selection in ancestral populations. ABS identifies adaptive genes in human evolution, like EDAR and SLC35F3, and is available as open-source software.
Area of Science:
- Population Genetics
- Evolutionary Biology
- Genomics
Background:
- Detecting positive selection in ancestral populations is challenging.
- Existing methods may not be optimal for identifying selection in direct ancestors of multiple descendant populations.
Purpose of the Study:
- Introduce the ancestral branch statistic (ABS), a novel four-population summary statistic.
- Identify genes under positive selection in ancestral human populations.
Main Methods:
- Developed the ancestral branch statistic (ABS).
- Simulated ABS performance against the 3P-CLR method.
- Applied ABS to human genomic data (contemporary East Asian and ancient European).
- Provided open-source software (CalcABS) for genomic scans.
Main Results:
- ABS performs comparably or better than 3P-CLR, especially under model violations.
- Identified known (EDAR) and novel (SLC35F3) candidates for positive selection in ancestral East Asians.
- Confirmed known candidates (MCM6/LCT, SLC45A2, HERC2) and identified novel candidates (SLC30A9, CYP1A2) in ancestral Europeans.
Conclusions:
- ABS is an effective tool for detecting positive selection in ancestral populations.
- The study identified novel genes potentially involved in human adaptation.
- Open-source software facilitates future research in evolutionary genomics.
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