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altitudes (&amp;gt;2500\u00a0m) 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\u00a0&amp;gt;\u00a0T; p.Ala83Val), with the T allele derived from archaic ancestry and carried by &amp;gt;94% of Tibetans but absent or in low frequencies (&amp;lt;3%) in non-Tibetan populations. The rs116983452-T is strongly and positively correlated with altitude and significantly associated with reduced hemoglobin concentration (p\u00a0=\u00a05.78\u00a0\u00d7\u00a010\u22125), red blood cell count (p\u00a0=\u00a05.72\u00a0\u00d7\u00a010\u22127) and hematocrit (p\u00a0=\u00a02.57\u00a0\u00d7\u00a010\u22126). 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.<\/jats:p>","DOI":"10.1093\/nsr\/nwz108","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T19:10:05Z","timestamp":1564513805000},"page":"1201-1222","source":"Crossref","is-referenced-by-count":73,"title":["Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders"],"prefix":"10.1093","volume":"6","author":[{"given":"Lian","family":"Deng","sequence":"first","affiliation":[{"name":"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, 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