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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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Principal Components 1 and 2, derived from 1,000 ethnicity informative SNPs for European origin.
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pone.0123818.g001: Principal Components 1 and 2, derived from 1,000 ethnicity informative SNPs for European origin.

Mentions: Participants in the two cohorts that were included in this study were born between 1890 and 1968, and the age at either last contact or death was 74±12 (mean ± standard deviation). The total number of T2D cases was 939 out of the 5,245 subjects; since FHS is an ongoing study, many study participants do not have diabetes yet. The incidence of diabetes in participants with last contact at age <65 was 8%, while it was 26% for participants with last contact at age > 83 years. The 939 cases showed a first record of diabetes at 63±24 years of age [50]. The proportion of subjects in the population with diabetes between cohorts was 30.2% in the Original cohort and 13.0% in the Offspring cohort (paternal and offspring generations, respectively). This difference reflects the shorter observational time of the Offspring cohort, whose subjects have an age at the last contact time (or death time) of 69±10, whereas this age is 87±8 for the Original cohort. The proportion of people that had diabetes was 20.5% in males (45% of the sample) and 15.7% in females (55% of the sample). This difference in proportions is in accordance with what has been observed in the literature where males have a higher incidence of diabetes [50]. In the study we also included principal components derived from ethnicity informative SNPs to account for population structure; Fig 1 shows a scatter plot with the ethnicity-informed marker-derived PC 1 and 2.


Assessment of whole-genome regression for type II diabetes.

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

Principal Components 1 and 2, derived from 1,000 ethnicity informative SNPs for European origin.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123818.g001: Principal Components 1 and 2, derived from 1,000 ethnicity informative SNPs for European origin.
Mentions: Participants in the two cohorts that were included in this study were born between 1890 and 1968, and the age at either last contact or death was 74±12 (mean ± standard deviation). The total number of T2D cases was 939 out of the 5,245 subjects; since FHS is an ongoing study, many study participants do not have diabetes yet. The incidence of diabetes in participants with last contact at age <65 was 8%, while it was 26% for participants with last contact at age > 83 years. The 939 cases showed a first record of diabetes at 63±24 years of age [50]. The proportion of subjects in the population with diabetes between cohorts was 30.2% in the Original cohort and 13.0% in the Offspring cohort (paternal and offspring generations, respectively). This difference reflects the shorter observational time of the Offspring cohort, whose subjects have an age at the last contact time (or death time) of 69±10, whereas this age is 87±8 for the Original cohort. The proportion of people that had diabetes was 20.5% in males (45% of the sample) and 15.7% in females (55% of the sample). This difference in proportions is in accordance with what has been observed in the literature where males have a higher incidence of diabetes [50]. In the study we also included principal components derived from ethnicity informative SNPs to account for population structure; Fig 1 shows a scatter plot with the ethnicity-informed marker-derived PC 1 and 2.

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