ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction. Hajiloo, M., Sapkota, Y., Mackey, J. R, Robson, P., Greiner, R., & Damaraju, S. BMC Bioinformatics, 14(1):61, 2013. ![ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{hajiloo_ethnopred:_2013,
title = {{ETHNOPRED}: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction},
volume = {14},
url = {http://www.biomedcentral.com/1471-2105/14/61},
doi = {10.1016/j.ajhg.2007.11.004},
abstract = {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.},
language = {English},
number = {1},
journal = {BMC Bioinformatics},
author = {Hajiloo, Mohsen and Sapkota, Yadav and Mackey, John R and Robson, Paula and Greiner, Russell and Damaraju, Sambasivarao},
year = {2013},
pages = {61},
}
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