Dataset Information

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

ABSTRACT: With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

SUBMITTER: Bae S

PROVIDER: S-EPMC5287118 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

Bae Sunghwan S Choi Sungkyoung S Kim Sung Min SM Park Taesung T

Genomics & informatics 20161230 4

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction model ...[more]

PMID: 28154505

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Project description:Background/objectiveBody mass index (BMI) is a surrogate measure of adiposity but does not distinguish fat from lean or bone mass. The genetic determinants of BMI are thought to predominantly influence adiposity but this has not been confirmed. Here we characterise the association between BMI-related genetic variants and body composition in adults.Subjects/methodsAmong 9667 adults aged 29-64 years from the Fenland study, a genetic risk score for BMI (BMI-GRS) was calculated for each individual as the weighted sum of BMI-increasing alleles across 96 reported BMI-related variants. Associations between the BMI-GRS and body composition, estimated by dual-energy X-ray absorptiometry (DXA) scans, were examined using age-adjusted linear regression models, separately by sex.ResultsThe BMI-GRS was positively associated with all fat, lean and bone variables. Across body regions, associations of the greatest magnitude were observed for adiposity variables, for example, for each s.d. increase in BMI-GRS predicted BMI, we observed a 0.90 s.d. (95% confidence interval (CI): 0.71, 1.09) increase in total fat mass for men (P=3.75 × 10-21) and a 0.96 s.d. (95% CI: 0.77, 1.16) increase for women (P=6.12 × 10-22). Associations of intermediate magnitude were observed with lean variables, for example, total lean mass: men: 0.68 s.d. (95% CI: 0.49, 0.86; P=1.91 × 10-12); women: 0.85 s.d. (95% CI: 0.65, 1.04; P=2.66 × 10-17) and of a lower magnitude with bone variables, for example, total bone mass: men: 0.39 s.d. (95% CI: 0.20, 0.58; P=5.69 × 10-5); women: 0.45 s.d. (95% CI: 0.26, 0.65; P=3.96 × 106). Nominally significant associations with BMI were observed for 28 single-nucleotide polymorphisms. All 28 were positively associated with fat mass and 13 showed adipose-specific effects.ConclusionsIn adults, genetic susceptibility to elevated BMI influences adiposity more than lean or bone mass. This mirrors the association between BMI and body composition. The BMI-GRS can be used to model the effects of measured BMI and adiposity on health and other outcomes.

Dataset Information

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

Publications

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

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