Inferring the population structure and admixture history of three Hmong-Mien-speaking Miao tribes from southwest China based on Genome-wide SNP genotyping

2021 ◽  
pp. 1-19
Author(s):  
Ting Luo ◽  
Rui Wang ◽  
Chuan-Chao Wang
2014 ◽  
Vol 31 (11) ◽  
pp. 2929-2940 ◽  
Author(s):  
Takehiro Sato ◽  
Shigeki Nakagome ◽  
Chiaki Watanabe ◽  
Kyoko Yamaguchi ◽  
Akira Kawaguchi ◽  
...  

animal ◽  
2017 ◽  
Vol 11 (10) ◽  
pp. 1680-1688 ◽  
Author(s):  
A. Kominakis ◽  
A.L. Hager-Theodorides ◽  
A. Saridaki ◽  
G. Antonakos ◽  
G. Tsiamis

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoyun Bin ◽  
Rui Wang ◽  
Youyi Huang ◽  
Rongyao Wei ◽  
Kongyang Zhu ◽  
...  

Sui people, which belong to the Tai-Kadai-speaking family, remain poorly characterized due to a lack of genome-wide data. To infer the fine-scale population genetic structure and putative genetic sources of the Sui people, we genotyped 498,655 genome-wide single-nucleotide polymorphisms (SNPs) using SNP arrays in 68 Sui individuals from seven indigenous populations in Guizhou province and Guangxi Zhuang Autonomous Region in Southwest China and co-analyzed with available East Asians via a series of population genetic methods including principal component analysis (PCA), ADMIXTURE, pairwise Fst genetic distance, f-statistics, qpWave, and qpAdm. Our results revealed that Guangxi and Guizhou Sui people showed a strong genetic affinity with populations from southern China and Southeast Asia, especially Tai-Kadai- and Hmong-Mien-speaking populations as well as ancient Iron Age Taiwan Hanben, Gongguan individuals supporting the hypothesis that Sui people came from southern China originally. The indigenous Tai-Kadai-related ancestry (represented by Li), Northern East Asian-related ancestry, and Hmong-Mien-related lineage contributed to the formation processes of the Sui people. We identified the genetic substructure within Sui groups: Guizhou Sui people were relatively homogeneous and possessed similar genetic profiles with neighboring Tai-Kadai-related populations, such as Maonan. While Sui people in Yizhou and Huanjiang of Guangxi might receive unique, additional gene flow from Hmong-Mien-speaking populations and Northern East Asians, respectively, after the divergence within other Sui populations. Sui people could be modeled as the admixture of ancient Yellow River Basin farmer-related ancestry (36.2–54.7%) and ancient coastal Southeast Asian-related ancestry (45.3–63.8%). We also identified the potential positive selection signals related to the disease susceptibility in Sui people via integrated haplotype score (iHS) and number of segregating sites by length (nSL) scores. These genomic findings provided new insights into the demographic history of Tai-Kadai-speaking Sui people and their interaction with neighboring populations in Southern China.


2017 ◽  
Vol 109 (3) ◽  
pp. 272-282 ◽  
Author(s):  
Martin Helmkampf ◽  
Thomas K Wolfgruber ◽  
M Renee Bellinger ◽  
Roshan Paudel ◽  
Michael B Kantar ◽  
...  

2021 ◽  
Author(s):  
Caoqi Fan ◽  
Nicholas Mancuso ◽  
Charleston W.K. Chiang

The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM generally does not take advantage of linkage information and does not reflect the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed using genealogies inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to genotyping data from a population sample from Northern and Eastern Finland, we find that clustering analysis using the eGRM reveals population structure driven by subpopulations that would not be apparent using the canonical GRM, and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.


BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Slim Ben Jemaa ◽  
Mekki Boussaha ◽  
Mondher Ben Mehdi ◽  
Jun Heon Lee ◽  
Seung-Hwan Lee

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