Journal List > Korean J Nutr > v.43(4) > 1043831

Korean J Nutr. 2010 Aug;43(4):357-366. Korean.
Published online August 31, 2010.  https://doi.org/10.4163/kjn.2010.43.4.357
© 2010 The Korean Nutrition Society
Genetic Variants Associated with Calorie and Macronutrient Intake in a Genome-Wide Association Study
Inkyung Baik,1 Younjhin Ahn,2 Seung Ku Lee,3 Soriwul Kim,3 Bok-Ghee Han,2 and Chol Shin3,4
1Department of Foods and Nutrition, College of Natural Sciences, Kookmin University, Seoul 136-702, Korea.
2Center for Genome Science, National Institute of Health, Seoul 122-701, Korea.
3Institute of Human Genomic Study, Korea University Ansan Hospital, Ansan 425-707, Korea.
4Department of Internal Medicine, Korea University Ansan Hospital, Ansan 425-707, Korea.

To whom correspondence should be addressed. (Email: ibaik@kookmin.ac.kr )
Received April 01, 2010; Revised April 14, 2010; Accepted July 14, 2010.

Abstract

There has been no genome-wide association study (GWAS) for macronutrient intake as a quantitative trait. To explore genetic loci associated with total calorie and macronutrient intake, genome-wide association data of autosomal single nucleotide polymorphisms (SNPs) from Korean adults were analyzed. We conducted a GWAS in 3,690 men and women aged 40 to 60 years from an urban population-based cohort. At the baseline examination (June 18, 2001 through January 29, 2003), DNA samples of the study subjects were collected and analyzed for genotyping. The information of average daily consumption of total calorie, carbohydrate, protein, and fat was obtained from a semi-quantitative food frequency questionnaire and transformed by natural logarithm for analyses after adjustment of calorie intake. Using multivariate linear regression analysis adjusted for age, sex, and height, we tested for 352,021 SNPs and found weak associations, which do not reach genome-wide association significance, with calorie and macronutrient intake. However, a number of SNPs were found to have potential associations with macronutrient intake; in particular, signals in SORBS1 and those in PRKCB1 were likely associated with carbohydrate and fat intake, respectively. We observed an inverse association between the minor allele of the SNPs in these genes and the amount of consumption of carbohydrate or fat. Our GWAS identified loci and minor alleles weakly associated with macronutrient intake. Because SORBS1 and PRKCB1 are reportedly associated with the metabolism of glucose and lipid as well as with obesity-related diseases, further investigations on biological and functional roles of polymorphism of these genes in the relation to macronutrient intake are warranted.

Keywords: calorie; macronutrient; single nucleotide polymorphisms; genome-wide association study

Figures


Fig. 1
Quantile-quantile (QQ) plots of the -log10 p-values from the additive model-based analysis of genome-wide association data for average daily intake of total calorie and macronutrients among 3,690 study subjects. Amounts of carbohydrate, protein, and fat consumption have been adjusted for total calorie and log-transformed.
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Fig. 2
Association analysis of SNPs in SORBS1 gene region on chromosome 10q23.33. The panel shows p-values for the association testing of genome-wide association data with natural log transformed calorie-adjusted carbohydrate intake. The association was drawn from linear regression analysis adjusted for sex, age, and height on the basis of additive model. The Y axis is the negative log10 p-values and the × axis is position of chromosome 10q23.
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Fig. 3
Association analysis of SNPs in PRKCB1 gene region on chromosome 16p12.1. The panel shows p-values for the association testing of genome-wide association data with natural log transformed calorie-adjusted fat intake. The association was drawn from linear regression analysis adjusted for sex, age, and height on the basis of dominant model. The Y axis is the negative log10 p-values and the × axis is position of chromosome 16p12.
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Tables


Table 1
Characteristics of the study population (n = 3,690)
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Table 2
SNPs showing the smallest unadjusted p-values (< 10-5) for the association with natural log transformed calorie intake according to additive, dominant, and recessive models
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Table 3
SNPs showing the smallest unadjusted p-values (< 10-5) for the association with natural log transformed calorie-adjusted carbohydrate intake according to additive, dominant, and recessive models
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Table 4
SNPs showing the smallest unadjusted p-values (< 10-5) for the association with natural log transformed calorie-adjusted protein intake according to additive, dominant, and recessive models
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Table 5
SNPs showing the smallest unadjusted p-values (< 10-5) for the association with natural log transformed calorie-adjusted fat intake according to additive, dominant, and recessive models
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Notes

This study was supported by a grant of the Korea Centers for Disease Control and Prevention (budgets 2001-347-6111-221, 2002-347-6111-221 and the Korean Genome Analysis Project 4845-301) as well as by National Research Foundation of Korea Grant funded by the Korean Government (2009-0070038).

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