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Assessment of whole-genome regression for type II diabetes.

Vazquez AI, Klimentidis YC, Dhurandhar EJ, Veturi YC, Paérez-Rodríguez P - PLoS ONE (2015)

Bottom Line: We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066).Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information.Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States of America.

ABSTRACT
Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.

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SNP estimated effects ordered by effect for G-BLUP.This is re-parameterized with a Bayesian Ridge Regression. Dots show the effects of the 65 SNPs are and are on a gray scale; the darker the dot, the more significant is its association with the response.
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pone.0123818.g002: SNP estimated effects ordered by effect for G-BLUP.This is re-parameterized with a Bayesian Ridge Regression. Dots show the effects of the 65 SNPs are and are on a gray scale; the darker the dot, the more significant is its association with the response.

Mentions: Fig 2 shows the estimated SNP effect with G-BLUP model (re parameterized as a Bayesian Ridge Regression) and the dots highlight where the effects of the 65 SNPs of the M-65SNPs model are. It can be observed that the highly significant markers were not always the SNPs with the greatest effect. Models BC and BLASSO produced estimated effects of the SNP with similar shrinkage than one observed in Fig 2; BA however had convergence problems in the full data analysis.


Assessment of whole-genome regression for type II diabetes.

Vazquez AI, Klimentidis YC, Dhurandhar EJ, Veturi YC, Paérez-Rodríguez P - PLoS ONE (2015)

SNP estimated effects ordered by effect for G-BLUP.This is re-parameterized with a Bayesian Ridge Regression. Dots show the effects of the 65 SNPs are and are on a gray scale; the darker the dot, the more significant is its association with the response.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401705&req=5

pone.0123818.g002: SNP estimated effects ordered by effect for G-BLUP.This is re-parameterized with a Bayesian Ridge Regression. Dots show the effects of the 65 SNPs are and are on a gray scale; the darker the dot, the more significant is its association with the response.
Mentions: Fig 2 shows the estimated SNP effect with G-BLUP model (re parameterized as a Bayesian Ridge Regression) and the dots highlight where the effects of the 65 SNPs of the M-65SNPs model are. It can be observed that the highly significant markers were not always the SNPs with the greatest effect. Models BC and BLASSO produced estimated effects of the SNP with similar shrinkage than one observed in Fig 2; BA however had convergence problems in the full data analysis.

Bottom Line: We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066).Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information.Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States of America.

ABSTRACT
Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.

Show MeSH
Related in: MedlinePlus