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Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction.

Klimentidis YC, Zhou J, Wineinger NE - PLoS ONE (2014)

Bottom Line: Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10(-8)).Using a less stringent threshold (p<5×10(-4)), we identify 34 additional loci with multiple associated SNPs.This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

View Article: PubMed Central - PubMed

Affiliation: Mel and Enid Zuckerman College of Public Health, Division of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, United States of America.

ABSTRACT
Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs--a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10(-8)). Using a less stringent threshold (p<5×10(-4)), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

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Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.
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pone-0113072-g002: Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.

Mentions: Considering all three loci with additional SNPs at the p<5×10−8 threshold collectively, we found that the use of the seven SNPs identified by the C/J analysis resulted in a slightly higher AUC (0.5979) than when using only the three lead SNPs (0.5803). This represents a doubling in ΔAUC over the age+ sex model (see Figure 2), although this difference is not quite statistically significant (p = 0.055), according to the DeLong test. The inclusion of all SNPs (lead and from C/J analysis) results in a statistically significant (p = 0.049), yet small, increase in AUC (see Figure 2). At the p<5×10−6 threshold, the use of 11 SNPs at 5 loci (TCF7L2, CDKN2A/B, KCNQ1, DGKB and TP53INP1), slightly, but not significantly, increased prediction accuracy (AUC = 0.5885) over a model considering only the corresponding 5 lead SNPs (AUC = 0.5779; p = 0.126). At the p<5×10−5 threshold, we observe a small increase in prediction accuracy when using the 39 SNPs identified by the C/J analysis instead of the corresponding 17 lead SNPs (AUC = 0.5892 vs. 0.5724; p = 0.079). Finally, at the p<5×10−4 threshold, the use of 120 SNPs identified by the C/J analysis and the lead SNPs results in a slightly higher and nearly statistically significant increase in AUC over that of a model which includes only the 34 lead SNPs at the corresponding loci (AUC = 0.5965 vs. 0.5858; p = 0.067).


Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction.

Klimentidis YC, Zhou J, Wineinger NE - PLoS ONE (2014)

Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231111&req=5

pone-0113072-g002: Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.
Mentions: Considering all three loci with additional SNPs at the p<5×10−8 threshold collectively, we found that the use of the seven SNPs identified by the C/J analysis resulted in a slightly higher AUC (0.5979) than when using only the three lead SNPs (0.5803). This represents a doubling in ΔAUC over the age+ sex model (see Figure 2), although this difference is not quite statistically significant (p = 0.055), according to the DeLong test. The inclusion of all SNPs (lead and from C/J analysis) results in a statistically significant (p = 0.049), yet small, increase in AUC (see Figure 2). At the p<5×10−6 threshold, the use of 11 SNPs at 5 loci (TCF7L2, CDKN2A/B, KCNQ1, DGKB and TP53INP1), slightly, but not significantly, increased prediction accuracy (AUC = 0.5885) over a model considering only the corresponding 5 lead SNPs (AUC = 0.5779; p = 0.126). At the p<5×10−5 threshold, we observe a small increase in prediction accuracy when using the 39 SNPs identified by the C/J analysis instead of the corresponding 17 lead SNPs (AUC = 0.5892 vs. 0.5724; p = 0.079). Finally, at the p<5×10−4 threshold, the use of 120 SNPs identified by the C/J analysis and the lead SNPs results in a slightly higher and nearly statistically significant increase in AUC over that of a model which includes only the 34 lead SNPs at the corresponding loci (AUC = 0.5965 vs. 0.5858; p = 0.067).

Bottom Line: Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10(-8)).Using a less stringent threshold (p<5×10(-4)), we identify 34 additional loci with multiple associated SNPs.This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

View Article: PubMed Central - PubMed

Affiliation: Mel and Enid Zuckerman College of Public Health, Division of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, United States of America.

ABSTRACT
Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs--a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10(-8)). Using a less stringent threshold (p<5×10(-4)), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

Show MeSH
Related in: MedlinePlus