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BMI prediction within a Korean population.
PeerJ 2017
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.
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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