Evidence map›Paper›PMID 32081902›Full record

ArticleJournal of human genetics2020

Ancestry inference and admixture component estimations of Chinese Kazak group based on 165 AIM-SNPs via NGS platform.

Tong Xie, Chunmei Shen, Chao Liu, Yating Fang, Yuxin Guo, Qiong Lan, Lingxiang Wang, Jianye Ge, Yongsong Zhou, Shaoqing Wen and 2 more

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Article in Journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Tong XieMulti-Omics Innovative Research Center of Forensic Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, PR China.
Chunmei ShenInstitute of Brain and Behavioral Sciences, College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, PR China.
Chao LiuGuangzhou Forensic Science Institute, Guangdong Province Key Laboratory of Forensic Genetics, Guangzhou, Guangdong, PR China.
Yating FangMulti-Omics Innovative Research Center of Forensic Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, PR China.
Yuxin GuoKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Qiong LanMulti-Omics Innovative Research Center of Forensic Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, PR China.
Lingxiang WangMOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, PR China.
Jianye GeCenter for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA.
Yongsong ZhouMulti-Omics Innovative Research Center of Forensic Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, PR China.
Shaoqing WenInstitute of Archaeological Science, Fudan University, Shanghai, PR China.
Qing YangThermo Fisher Scientific, Waltham, MN, USA.
Bofeng ZhuMulti-Omics Innovative Research Center of Forensic Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, PR China. zhubofeng7372@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the biogeographical ancestries of populations and unknown individuals based on ancestry-informative markers (AIMs) has been widely applied in providing DNA clues to criminal investigations, correcting the factor of population stratification in genome-wide association studies (GWAS), and working as the basis of predicting the externally visible characteristics (EVCs) of individuals. The present study chose Chinese Xinjiang Kazak (XJK) group as research object using a 165 AIM-SNPs panel via next generation sequencing (NGS) technology to reveal its ancestral information and genetic background by referencing the populations' data from 1000 Genomes Phase 3. After the Bonferroni correction, there were no significant deviations at the 165 AIM-SNP loci except two loci with homozygote in the studied XJK group. Ancestry information inference and populations genetic analyses were conducted basing on multiplex statistical methods such as forensic statistical parameter analyses, estimation of the success ratios with cross-validation, population tree, principal component analysis (PCA), and genetic structure analysis. The present results revealed that XJK group had the admixed ancestral components of East Asian and European populations with the ratio of about 62:37.

Indexed as

Asian PeopleGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingPolymorphism, Single NucleotideChinaHumansWhite People

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.