Population inference based on mitochondrial DNA control region data by the nearest neighbors algorithm

Fu Chi Yang, Bill Tseng, Chun Yen Lin, Yu Jen Yu, Adrian Linacre, James Chun I. Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Population and geographic assignment are frequently undertaken using DNA sequences on the mitochondrial genome. Assignment to broad continental populations is common, although finer resolution to subpopulations can be less accurate due to shared genetic ancestry at a local level and members of different ancestral subpopulations cohabiting the same geographic area. This study reports on the accuracy of population and subpopulation assignment by using the sequence data obtained from the 3070 mitochondrial genomes and applying the K-nearest neighbors (KNN) algorithm. These data also included training samples used for continental and population assignment comprised of 1105 Europeans (including Austria, France, Germany, Spain, and England and Caucasian countries), 374 Africans (including North and East Africa and non-specific area (Pan-Africa)), and 1591 Asians (including Japan, Philippines, and Taiwan). Subpopulations included in this study were 1153 mitochondrial DNA (mtDNA) control region sequences from 12 subpopulations in Taiwan (including Han, Hakka, Ami, Atayal, Bunun, Paiwan, Puyuma, Rukai, Saisiyat, Tsou, Tao, and Pingpu). Additionally, control region sequence data from a further 50 samples, obtained from the Sigma Company, were included after they were amplified and sequenced. These additional 50 samples acted as the “testing samples” to verify the accuracy of the population. In this study, based on genetic distances as genetic metric, we used the KNN algorithm and the K-weighted-nearest neighbors (KWNN) algorithm weighted by genetic distance to classify individuals into continental populations, and subpopulations within the same continent. Accuracy results of ethnic inferences at the level of continental populations and of subpopulations among KNN and KWNN algorithms were obtained. The training sample set achieved an overall accuracy of 99 to 82% for assignment to their continental populations with K values from 1 to 101. Population assignment for subpopulations with K assignments from 1 to 5 reached an accuracy of 77 to 54%. Four out of 12 Taiwanese populations returned an accuracy of assignment of over 60%, Ami (66%), Atayal (67%), Saisiyat (66%), and Tao (80%). For the testing sample set, results of ethnic prediction for continental populations with recommended K values as 5, 10, and 35, based on results of the training sample set, achieved overall an accuracy of 100 to 94%. This study provided an accurate method in population assignment for not only continental populations but also subpopulations, which can be useful in forensic and anthropological studies.

Original languageEnglish
Pages (from-to)1191-1199
Number of pages9
JournalInternational Journal of Legal Medicine
Volume135
Issue number4
Early online date14 Feb 2021
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Control region
  • mtDNA
  • Nearest neighbors algorithm
  • Population inference

Fingerprint

Dive into the research topics of 'Population inference based on mitochondrial DNA control region data by the nearest neighbors algorithm'. Together they form a unique fingerprint.

Cite this