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Abstract
High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions. We further develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression. We further construct the downstream network of EPAS1, elucidating its roles in hypoxic response and angiogenesis. Collectively, we provide a systematic approach to interpret phenotype-associated noncoding variants in proper cell types and relevant dynamic conditions, to model their impact on gene regulation.
Tibetan adaptation to the high-altitude environment represents a case of natural selection during recent human evolution. Here the authors investigated the chromatin and transcriptional landscape of umbilical endothelial cells from Tibetan and Han Chinese donors and provide genome-wide characterization of the hypoxia regulatory network associated high-altitude adaptation.
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1 Kunming Institute of Zoology, Chinese Academy of Sciences, State Key Laboratory of Genetic Resources and Evolution, Kunming, China (GRID:grid.419010.d) (ISNI:0000 0004 1792 7072); Chinese Academy of Sciences, CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); Chinese Academy of Sciences, Center for Excellence in Animal Evolution and Genetics, Kunming, China (GRID:grid.9227.e) (ISNI:0000000119573309); Stanford University, Bio-X Program, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
2 Kunming Institute of Zoology, Chinese Academy of Sciences, State Key Laboratory of Genetic Resources and Evolution, Kunming, China (GRID:grid.419010.d) (ISNI:0000 0004 1792 7072); Chinese Academy of Sciences, Center for Excellence in Animal Evolution and Genetics, Kunming, China (GRID:grid.9227.e) (ISNI:0000000119573309)
3 Kunming Institute of Zoology, Chinese Academy of Sciences, State Key Laboratory of Genetic Resources and Evolution, Kunming, China (GRID:grid.419010.d) (ISNI:0000 0004 1792 7072); Chinese Academy of Sciences, Center for Excellence in Animal Evolution and Genetics, Kunming, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
4 Stanford University, Departments of Statistics, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Clemson University, Center for Human Genetics and Department of Genetics and Biochemistry, Greenwood, USA (GRID:grid.26090.3d) (ISNI:0000 0001 0665 0280)
5 School of Medicine, Tibetan University, High Altitude Medical Research Center, Lhasa, China (GRID:grid.440680.e) (ISNI:0000 0004 1808 3254)
6 Chinese Academy of Sciences, CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
7 School of Mathematical Science, Inner Mongolia University, Huhhot, China (GRID:grid.411643.5) (ISNI:0000 0004 1761 0411)
8 Stanford University, Departments of Statistics, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
9 Stanford University, Bio-X Program, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University, Departments of Statistics, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University School of Medicine, Department of Biomedical Data Science, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
10 Chinese Academy of Sciences, CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); Chinese Academy of Sciences, Center for Excellence in Animal Evolution and Genetics, Kunming, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419); University of Chinese Academy of Sciences, Chinese Academy of Sciences, Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, Hangzhou, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)