Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score—the CoLaus Study

Discovery Analytics, GlaxoSmithKline, Collegeville, PA, USA.
Diabetologia (Impact Factor: 6.88). 02/2009; 52(4):600-8. DOI: 10.1007/s00125-008-1254-y
Source: PubMed

ABSTRACT Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors.
We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals.
The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles.
In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.

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Available from: Dawn M Waterworth, Aug 12, 2015
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    • "To motivate the methodological issues considered in this paper, we here provide some examples of how allele scores have been used in practice. Lin et al. [17] used an unweighted and a weighted allele score based on 15 genetic variants in the context of risk prediction, deriving weights from the data under analysis. They found that a weighted allele score provided greater discrimination than an unweighted score when used in conjunction with conventional risk factors. "
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    ABSTRACT: An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate 'weak instrument' bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score.
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    • "These findings reinforce the view of common obesity as a disorder of behaviour rather than metabolism, mediated through hypothalamic dysregulation. In contrast, equivalent studies of fat distribution, rather than overall adiposity (Lindgren et al. 2009; Heid et al. 2010; Heard-Costa et al. 2009) have highlighted candidate transcripts implicated in the regulation of adipocyte development and function. "
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    • "In some GWAS publications, there have been initial attempts to look at the predictive power of the few identified ''top-SNPs'' [e.g. Lin et al., 2009; Miyake et al., 2009; Myocardial Infarction Genetics Consortium, 2009; Zheng et al., 2008]. The International Schizophrenia Consortium [2009] combined large numbers of variants. "
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    ABSTRACT: Over the last few years, many new genetic associations have been identified by genome-wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction using GWAS, primarily motivated by the fact that individual effect sizes of variants associated with the phenotype are mostly small. However, there have also been arguments that many disease-associated variants have not yet been identified; hence, prospects for risk prediction may improve if more variants are included. From a risk prediction perspective, it is reasonable to average a larger number of predictors, of which some may have (limited) predictive power, and some actually may be noise. The idea being that when added together, the combined small signals results in a signal that is stronger than the noise from the unrelated predictors. We examine various aspects of the construction of models for the estimation of disease probability. We compare different methods to construct such models, to examine how implementation of cross-validation may influence results, and to examine which single nucleotide polymorphisms (SNPs) are most useful for prediction. We carry out our investigation on GWAS of the Welcome Trust Case Control Consortium. For Crohn's disease, we confirm our results on another GWAS. Our results suggest that utilizing a larger number of SNPs than those which reach genome-wide significance, for example using the lasso, improves the construction of risk prediction models.
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