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BMI prediction within a Korean population

BACKGROUND: Body Mass Index (BMI) is widely regarded as an important clinical trait for obesity and other diseases such as Type 2 diabetes, coronary heart disease, and osteoarthritis. METHODS: This study uses 6,011 samples of genotype data from ethnic Korean subjects. The data was retrieved from the...

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Autores principales: Lee, Jin Sol, Cheong, Hyun Sub, Shin, Hyoung-Doo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493974/
https://www.ncbi.nlm.nih.gov/pubmed/28674662
http://dx.doi.org/10.7717/peerj.3510
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author Lee, Jin Sol
Cheong, Hyun Sub
Shin, Hyoung-Doo
author_facet Lee, Jin Sol
Cheong, Hyun Sub
Shin, Hyoung-Doo
author_sort Lee, Jin Sol
collection PubMed
description BACKGROUND: Body Mass Index (BMI) is widely regarded as an important clinical trait for obesity and other diseases such as Type 2 diabetes, coronary heart disease, and osteoarthritis. METHODS: This study uses 6,011 samples of genotype data from ethnic Korean subjects. The data was retrieved from the Korea Association Resource. To identify the BMI-related markers within the Korean population, we collected genome-wide association study (GWAS) markers using a GWAS catalog and also obtained other markers from nearby regions. Of the total 6,011 samples, 5,410 subjects were used as part of a single nucleotide polymorphism (SNP) selection set in order to identify the overlapping BMI-associated SNPs within a 10-fold cross validation. RESULTS: We selected nine SNPs (rs12566985 (FPGT-TNNI3K), rs6545809 (ADCY3), rs2943634 (located near LOC646736), rs734597 (located near TFAP2B), rs11030104 (BDNF), rs7988412 (GTF3A), rs2241423 (MAP2K5), rs7202116 (FTO), and rs6567160 (located near LOC105372152) to assist in BMI prediction. The calculated weighted genetic risk scores based on the selected 9 SNPs within the SNP selection set were applied to the final validation set consisting of 601 samples. Our results showed upward trends in the BMI values (P < 0.0001) within the 10-fold cross validation process for R(2) > 0.22. These trends were also observed within the validation set for all subjects, as well as within the validation sets divided by gender (P < 0.0001, R(2) > 0.46). DISCUSSION: The set of nine SNPs identified in this study may be useful for prospective predictions of BMI.
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spelling pubmed-54939742017-07-03 BMI prediction within a Korean population Lee, Jin Sol Cheong, Hyun Sub Shin, Hyoung-Doo PeerJ Genetics BACKGROUND: Body Mass Index (BMI) is widely regarded as an important clinical trait for obesity and other diseases such as Type 2 diabetes, coronary heart disease, and osteoarthritis. METHODS: This study uses 6,011 samples of genotype data from ethnic Korean subjects. The data was retrieved from the Korea Association Resource. To identify the BMI-related markers within the Korean population, we collected genome-wide association study (GWAS) markers using a GWAS catalog and also obtained other markers from nearby regions. Of the total 6,011 samples, 5,410 subjects were used as part of a single nucleotide polymorphism (SNP) selection set in order to identify the overlapping BMI-associated SNPs within a 10-fold cross validation. RESULTS: We selected nine SNPs (rs12566985 (FPGT-TNNI3K), rs6545809 (ADCY3), rs2943634 (located near LOC646736), rs734597 (located near TFAP2B), rs11030104 (BDNF), rs7988412 (GTF3A), rs2241423 (MAP2K5), rs7202116 (FTO), and rs6567160 (located near LOC105372152) to assist in BMI prediction. The calculated weighted genetic risk scores based on the selected 9 SNPs within the SNP selection set were applied to the final validation set consisting of 601 samples. Our results showed upward trends in the BMI values (P < 0.0001) within the 10-fold cross validation process for R(2) > 0.22. These trends were also observed within the validation set for all subjects, as well as within the validation sets divided by gender (P < 0.0001, R(2) > 0.46). DISCUSSION: The set of nine SNPs identified in this study may be useful for prospective predictions of BMI. PeerJ Inc. 2017-06-29 /pmc/articles/PMC5493974/ /pubmed/28674662 http://dx.doi.org/10.7717/peerj.3510 Text en ©2017 Lee et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Genetics
Lee, Jin Sol
Cheong, Hyun Sub
Shin, Hyoung-Doo
BMI prediction within a Korean population
title BMI prediction within a Korean population
title_full BMI prediction within a Korean population
title_fullStr BMI prediction within a Korean population
title_full_unstemmed BMI prediction within a Korean population
title_short BMI prediction within a Korean population
title_sort bmi prediction within a korean population
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493974/
https://www.ncbi.nlm.nih.gov/pubmed/28674662
http://dx.doi.org/10.7717/peerj.3510
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