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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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Related in: MedlinePlus

Probability of diabetes for M-65SNP and G-BLUP.These are classified by the presence or absence of diabetes: a) healthy and b) diabetic people.
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pone.0123818.g003: Probability of diabetes for M-65SNP and G-BLUP.These are classified by the presence or absence of diabetes: a) healthy and b) diabetic people.

Mentions: Fig 3 shows the relationship between the probability of having diabetes for healthy (a) and diabetic (b) participants. Estimates in the scatter plots were obtained from the G-BLUP model and M-65SNPs. Other WGR (not shown) presented a strong association with the probabilities estimated with the G-BLUP. The line is a slope of one, indicating the hypothetical situation in which both models estimate the same probability of having diabetes for a person. M-65SNPs is based on fixed-effect estimates. For this model, we observe that each SNP has stronger effect than the same SNP in the G-BLUP model because a fixed effect is a value close to the conditional mean of all the samples with that SNP (jointly with deviations given by other factors); while the RKHS predicts random effects where the SNP effect is pushed towards zero. The resulting estimates for a person that has all the high-risk SNPs (of the 65) accumulate into a very large estimated overall risk, while in the RKHS, the estimated effects of these SNPs are mitigated and predicted risk is thus lower.


Assessment of whole-genome regression for type II diabetes.

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

Probability of diabetes for M-65SNP and G-BLUP.These are classified by the presence or absence of diabetes: a) healthy and b) diabetic people.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123818.g003: Probability of diabetes for M-65SNP and G-BLUP.These are classified by the presence or absence of diabetes: a) healthy and b) diabetic people.
Mentions: Fig 3 shows the relationship between the probability of having diabetes for healthy (a) and diabetic (b) participants. Estimates in the scatter plots were obtained from the G-BLUP model and M-65SNPs. Other WGR (not shown) presented a strong association with the probabilities estimated with the G-BLUP. The line is a slope of one, indicating the hypothetical situation in which both models estimate the same probability of having diabetes for a person. M-65SNPs is based on fixed-effect estimates. For this model, we observe that each SNP has stronger effect than the same SNP in the G-BLUP model because a fixed effect is a value close to the conditional mean of all the samples with that SNP (jointly with deviations given by other factors); while the RKHS predicts random effects where the SNP effect is pushed towards zero. The resulting estimates for a person that has all the high-risk SNPs (of the 65) accumulate into a very large estimated overall risk, while in the RKHS, the estimated effects of these SNPs are mitigated and predicted risk is thus lower.

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