Genetic history and biological adaptive landscape of the Tujia people inferred from shared haplotypes and alleles

Abstract

High-quality genomic datasets from under-representative populations are essential for population genetic analysis and medical relevance. Although the Tujia are the most populous ethnic minority in southwestern China, previous genetic studies have been fragmented and only partially reveal their genetic diversity landscape. The understanding of their fine-scale genetic structure and potentially differentiated biological adaptive features remains nascent. This study aims to explore the demographic history and genetic architecture related to the natural selection of the Tujia people, focusing on a meta-Tujia population from the central regions of the Yangtze River Basin. Population genetic analyses conducted on the meta-Tujia people indicate that they occupy an intermediate position in the East Asian North-South genetic cline. A close genetic affinity was identified between the Tujia people and neighboring Sinitic-speaking populations. Admixture models suggest that the Tujia can be modeled as a mixture of northern and southern ancestries. Estimates of f3/f4 statistics confirmed the presence of ancestral links to ancient Yellow River Basin millet farmers and the BaBanQinCen-related groups. Furthermore, population-specific natural selection signatures were explored, revealing highly differentiated functional variants between the Tujia and southern indigenous populations, including genes associated with hair morphology (e.g., EDAR) and skin pigmentation (e.g., SLC24A5). Additionally, both shared and unique selection signatures were identified among ethnically diverse but geographically adjacent populations, highlighting their extensive admixture and the biological adaptations introduced by this admixture. The study unveils significant population movements and genetic admixture among the Tujia and other ethno-linguistically diverse East Asian groups, elucidating the differentiated adaptation processes across geographically diverse populations from the current genetic landscape.