27th International Congress of International Society for Forensic Genetics
PREDICTION OF ASIAN ETHNIC SUBGROUPS USING HID-ION AMPLISEQTM ANCESTRY
Lee. JH1, Kim. YK2, Cho. S3, Kim. MY3, DH. Shin3,4, Ann. JJ2, Ha. EH2, Lee. SD1,2
1Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea
2Department of Information Statistics, Yonsei University, Wonju, Korea
3Institute of Forensic Science, Seoul National University College of Medicine, Seoul, Korea
4Department of Anatomy, Seoul National University, College of Medicine, Seoul, South Korea
e-mail : email@example.com
Prediction of ethnicity in Asia can be approached in a variety of ways, but the method using Ancestry Informative Markers (AIMs) provides additional information. The HID-Ion AmpliSeqTM Ancestry Panel is a forensic multiplex platform consisted of 165 autosomal markers designed to provide biogeographic ancestry information.
In present study, we have investigated 750 unrelated Asians, from southern China (n=99), Beijing (n=100), Japan (n=101), Korean (n=100), Vietnam (n=100), Nepal (n=100), India (n=51), and Pakistan (n=99). The Torrent Server and the HID SNP genotyper plugin provide the calculated ethnicity probability and likelihood ratio. However, a variety of statistical approaches are needed when considering that Asians are closely related geographically and historically and there is not enough data available. For this reason, several statistical techniques have been tried and compared with the results provided by the plugin. Also, we applied various statistical algorithms for ethnic that classify Northeast Asian, Southeast Asian (Vietnamese) and Southwest Asian using SNP data from panel.
This research is meaningful in terms of the sub-classification of Asian people and applicability without major changes whenever new population is added. Furthermore, if further research is continued from the viewpoint of usability when considered together with Lineage markers, it is expected that it will provide more comprehensive information.
1. KK Kidd, WC Speed, AJ Pakstis et al: Progress toward an efficient panel of SNPs for ancestry inference. Forensic Science International: Genetics. 2014; 10: 23–32
2. P Larranaga, B Calvo, R Santana et al: Machine learning in bioinformatics. Briefings in bioinformatics. 2006;7(1): 86-112.