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Evaluation of the classification method using ancestry SNP markers for ethnic group

Authors
Lee, Hyo JungHong, Sun PyoLee, Soong DeokRhee, Hwan SeokLee, Ji HyunJeong, Su JinLee, Jae Won
Issue Date
1월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
single nucleotide polymorphisms (SNP); allele; ethnic group; classification; Korean population
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.1, pp.1 - 9
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
26
Number
1
Start Page
1
End Page
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/68444
DOI
10.29220/CSAM.2019.26.1.001
ISSN
2287-7843
Abstract
Various probabilistic methods have been proposed for using interpopulation allele frequency differences to infer the ethnic group of a DNA specimen. The selection of the statistical method is critical because the accuracy of the statistical classification results vary. For the ancestry classification, we proposed a new ancestry evaluation method that estimate the combined ethnicity index as well as compared its performance with various classical classification methods using two real data sets. We selected 13 SNPs that are useful for the inference of ethnic origin. These single nucleotide polymorphisms (SNPs) were analyzed by restriction fragment mass polymorphism assay and followed by classification among ethnic groups. We genotyped 400 individuals from four ethnic groups (100 African-American, 100 Caucasian, 100 Korean, and 100 Mexican-American) for 13 SNPs and allele frequencies that differed among the four ethnic groups. Additionally, we applied our new method to HapMap SNP genotypes for 1,011 samples from 4 populations (African, European, East Asian, and CentralSouth Asian). Our proposed method yielded the highest accuracy among statistical classification methods. Our ethnic group classification system based on the analysis of ancestry informative SNP markers can provide a useful statistical tool to identify ethnic groups.
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LEE, JAE WON
정경대학 (통계학과)
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