Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders

Lian Deng; Chao Zhang; Kai Yuan; Yang Gao; Yuwen Pan; Xueling Ge; Yaoxi He; Yuan Yuan; Yan Lu; Xiaoxi Zhang; Hao Chen; Haiyi Lou; Xiaoji Wang; Dongsheng Lu; Jiaojiao Liu; Lei Tian; Qidi Feng; Asifullah Khan; Yajun Yang; Zi-Bing Jin; Jian Yang; Fan Lu; Jia Qu; Longli Kang; Bing Su; Shuhua Xu

doi:10.1093/nsr/nwz108

Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders

Natl Sci Rev. 2019 Nov;6(6):1201-1222. doi: 10.1093/nsr/nwz108. Epub 2019 Aug 7.

Authors

Lian Deng¹, Chao Zhang¹, Kai Yuan¹, Yang Gao^{1

2}, Yuwen Pan¹, Xueling Ge¹, Yaoxi He³, Yuan Yuan¹, Yan Lu¹, Xiaoxi Zhang^{1

2}, Hao Chen¹, Haiyi Lou¹, Xiaoji Wang¹, Dongsheng Lu¹, Jiaojiao Liu^{1

2}, Lei Tian¹, Qidi Feng¹, Asifullah Khan¹, Yajun Yang⁴, Zi-Bing Jin⁵, Jian Yang^{5

6}, Fan Lu⁵, Jia Qu⁵, Longli Kang⁷, Bing Su^{3

8}, Shuhua Xu^{1

2

8

9

10}

Affiliations

¹ Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
² School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
³ State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.
⁴ State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China.
⁵ The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China.
⁶ Institute for Molecular Bioscience, Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
⁷ Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China.
⁸ Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
⁹ Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China.
¹⁰ Human Phenome Institute, Fudan University, Shanghai 201203, China.

Abstract

Human genetic adaptation to high altitudes (>2500 m) has been extensively studied over the last few years, but few functional adaptive genetic variants have been identified, largely owing to the lack of deep-genome sequencing data available to previous studies. Here, we build a list of putative adaptive variants, including 63 missense, 7 loss-of-function, 1,298 evolutionarily conserved variants and 509 expression quantitative traits loci. Notably, the top signal of selection is located in TMEM247, a transmembrane protein-coding gene. The Tibetan version of TMEM247 harbors one high-frequency (76.3%) missense variant, rs116983452 (c.248C > T; p.Ala83Val), with the T allele derived from archaic ancestry and carried by >94% of Tibetans but absent or in low frequencies (<3%) in non-Tibetan populations. The rs116983452-T is strongly and positively correlated with altitude and significantly associated with reduced hemoglobin concentration (p = 5.78 × 10^-5), red blood cell count (p = 5.72 × 10^-7) and hematocrit (p = 2.57 × 10^-6). In particular, TMEM247-rs116983452 shows greater effect size and better predicts the phenotypic outcome than any EPAS1 variants in association with adaptive traits in Tibetans. Modeling the interaction between TMEM247-rs116983452 and EPAS1 variants indicates weak but statistically significant epistatic effects. Our results support that multiple variants may jointly deliver the fitness of the Tibetans on the plateau, where a complex model is needed to elucidate the adaptive evolution mechanism.

Keywords: Tibetan; adaptive genetic variant; archaic ancestry; expression quantitative traits loci (eQTL); hemoglobin concentration; high-altitude adaptation; hypoxia; next-generation sequencing (NGS); tissue-specific expression.