Prediction of cholesterol ratios within a Korean population

Author:

Lee Jin Sol12,Cheong Hyun Sub3,Shin Hyoung Doo123ORCID

Affiliation:

1. Department of Life Science, Sogang University, Baekbumro 35, Mapo-gu, Seoul 04107, Republic of Korea

2. Research Institute for Basic Science, Sogang University, Mapo-gu, Seoul, 121-742, Republic of Korea

3. Department of Genetic Epidemiology, SNP Genetics, Inc., Taihard building 1007, Sogang University, Baekbumro 35, Mapo-gu, Seoul, Republic of Korea

Abstract

Cholesterol ratios (total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-c) and triglyceride (TG)/HDL-c) have been suggested as better indicators to predict various clinical features such as insulin resistance and heart disease. Therefore, we aimed to build a single nucleotide polymorphism (SNP) set to predict constitutional lipid metabolism. The genotype data of 7795 samples were obtained from the Korea Association Resource. Among the total of 7795 samples, 7016 subjects were used to perform 10-fold cross-validation. We selected the SNPs that showed significance constantly throughout all 10 cross-validation sets; another 779 samples were used as the final validation set. After performing the 10-fold cross-validation, the six SNPs ( rs4420638 ( APOC1 ), rs12421652 ( BUD13 ) , rs17411126 ( LPL ) , rs6589566 ( ZPR1 ) , rs16940212 ( LOC101928635 ) and rs10852765 ( ABCA8 )) were finally selected for predicting cholesterol ratios. The weighted genetic risk scores (wGRS) were calculated based on the regression slopes of the six selected SNPs. Our results showed upward trends of wGRS for both the TC/HDL-c and TG/HDL-c ratios within the 10-fold cross-validation. Similarly, the wGRS of the six SNPs also showed upward trends in analyses using the SNP selection set and final validation set. The selected six SNPs can be used to explain both the TC/HDL-c and TG/HDL-c ratios. Our results may be useful for the prospective predictions of cholesterol-related diseases.

Funder

Ministry of Education, Science and Technology

Publisher

The Royal Society

Subject

Multidisciplinary

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database;Proceedings of the 13th International Joint Conference on Computational Intelligence;2021

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