基于全基因组数据的AI-SNPs筛选及大陆次级区域内群体遗传结构差异研究

doi:10.16288/j.yczz.21-185

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Hereditas(Beijing) ›› 2021, Vol. 43 ›› Issue (10): 938-948.doi: 10.16288/j.yczz.21-185

• Research Article • Previous Articles Next Articles

AI-SNPs screening based on the whole genome data and research on genetic structure differences of subcontinent populations

Haoyu Wang(), Yuhan Hu(), Yueyan Cao, Qiang Zhu, Yuguo Huang, Xi Li, Ji Zhang

West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, China

Received:2021-05-26 Revised:2021-07-23 Online:2021-10-20 Published:2021-08-04
Supported by:
Supported by the National Natural Science Foundation of China Nos(81571861);Supported by the National Natural Science Foundation of China Nos(81630054)

Abstract

Abstract:

The genetic structure differences in population is one of the key elements in medical research involving multi-population samples. A set of ancestry-informative single nucleotide polymorphisms (AI-SNPs) can be utilized to analyze genetic component of a population, infer ancestral origin of individuals and pre-filter samples to reduce the impact of population genetic structure differences on medical research. However, most of the published studies were focused on revealing the differences between populations of continents or regions of a continent. In this paper, AI-SNPs were screened by calculating F_ST value in each pair of five East Asian populations: Japanese in Tokyo (JPT), Han Chinese in Beijing (CHB), Southern Han Chinese (CHS), Chinese Dai in Xishuangbanna (CDX) and Kinh in Ho Chi Minh City (KHV) in the 1000 Genomes Project phase 3 (GRCh37.p13) to analyze differences in subcontinent populations. The results demonstrate that the five East Asian populations in our study were assigned to three clusters: JPT, CHB and CHS, CDX and KHV. A set of AI-SNPs can be used for analysis of individual genetic composition and selection of representative individuals. Individuals with over 80% population representative genetic components have good representativeness of a population. This paper demonstrated the practical value of the method, which was performed to verify the ancestral composition and select representative samples with a panel of screened AI-SNPs by F_ST value, thereby reducing the influence of genetic structure differences in subcontinent populations on population-related medical research.

Key words: ancestry-informative marker, single nucleotide polymorphism (SNP), East Asian populations, genetic structure differences

Haoyu Wang, Yuhan Hu, Yueyan Cao, Qiang Zhu, Yuguo Huang, Xi Li, Ji Zhang. AI-SNPs screening based on the whole genome data and research on genetic structure differences of subcontinent populations[J]. Hereditas(Beijing), 2021, 43(10): 938-948.

Figures/Tables 9

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数据集	最大F_ST值	最小F_ST值	总位点数	F_ST≥0.25位点数	0.25>F_ST≥0.15位点数	0.15>F_ST≥0.05位点数
A_1(JPT-CHB)	0.266925	0.095475	591	1	19	571
A_2(JPT-CHS)	0.407479	0.111107	598	10	87	501
A_3(JPT-CDX)	0.788409	0.16243	630	46	584	0
A_4(JPT-KHV)	0.583637	0.141417	623	19	435	169
A_5(CHB-CHS)	0.146048	0.05001	723	0	0	723
A_6(CHB-CDX)	0.517659	0.109475	563	9	84	470
A_7(CHB-KHV)	0.25611	0.087147	670	1	18	651
A_8(CHS-CDX)	0.310909	0.081595	787	3	18	766
A_9(CHS-KHV)	0.192399	0.069052	631	0	7	624
A_10(CDX-KHV)	0.256551	0.067479	461	1	6	454

AI-SNPs screening based on the whole genome data and research on genetic structure differences of subcontinent populations

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Figures/Tables 9

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组别	JPT	CHB	CHS	CDX	KHV
C₇	18	20	13	9	6
C₈	41	20	21	20	11
C₉	38	6	9	44	41
合计/总样本数	97/104	46/103	43/105	73/93	58/99

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