ETHNOPRED: a Novel Machine Learning Method for Accurate Continental and Sub-continental Ancestry Identification and Population Stratification Correction
- Mohsen Hajiloo, Dept of Computing Science
- Yadav Sapkota
- John Mackey, Cross Cancer Institute
- Paula Robson
- Russ Greiner, Dept of Computing Science; PI of AICML
- Sambasivarao Damaraju, Cross Cancer Institute
Abstract
Background
Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification.
Results
We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual`s continental and sub-continental ancestry. To predict an individual`s continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of more than 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control`s lambda from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% +/- 2.4%, 95.6% +/- 3.9%, 95.6% +/- 2.1%, 98.3% +/- 2.0%, and 95.9% +/- 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver.
Conclusions
ETHNOPRED is a novel technique for producing classifiers that can identify an individual`s continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values.
Citation
M. Hajiloo, Y. Sapkota, J. Mackey, P. Robson, R. Greiner, S. Damaraju. "ETHNOPRED: a Novel Machine Learning Method for Accurate Continental and Sub-continental Ancestry Identification and Population Stratification Correction". BMC Bioinformatics, 14(61), February 2013.| Keywords: | Machine Learning, Ancestry, SNPs, GWAS, Population Stratification Correction, Ensemble Learning, HapMap Project |
| Category: | In Journal |
| Web Links: | DOI |
| Paper Link |
BibTeX
@article{Hajiloo+al:13,
author = {Mohsen Hajiloo and Yadav Sapkota and John Mackey and Paula Robson
and Russ Greiner and Sambasivarao Damaraju},
title = {ETHNOPRED: a Novel Machine Learning Method for Accurate Continental
and Sub-continental Ancestry Identification and Population Stratification
Correction},
Volume = "14",
Number = "61",
journal = {BMC Bioinformatics},
year = 2013,
}Last Updated: February 10, 2020Submitted by Sabina P
