Predicting Type 2 Diabetes Based on Polymorphisms From Genome-Wide Association Studies

Hoek, Mandy van; Dehghan, Abbas; Witteman, Jacqueline C. M.; Duijn, Cornelia M. van; Uitterlinden, André G.; Oostra, Ben A.; Hofman, Albert; Sijbrands, Eric J.G.; Janssens, A. Cecile J.W.

doi:10.2337/db08-0425

Cited by 262 publications

(227 citation statements)

References 28 publications

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“…Combining the GRS with conventional risk factors slightly but significantly improved case-control discrimination for type 2 diabetes risk. Several previous studies have shown that GRS could be used as a simple proxy of an individual's genetic predisposition to type 2 diabetes [2][3][4][5][6]. Consistent with this, we found that each additional risk allele increased the risk of type 2 diabetes by approximately 18% and that participants with a GRS ≥19.0 (8.87%) had a 4.58-fold increased risk of type 2 diabetes compared with those with a GRS ≤11.0 (7.63%).…”

Section: Discussionmentioning

confidence: 99%

Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

et al. 2010

Diabetologia

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Aims/hypothesis The recent advent of genome-wide association studies has considerably accelerated the identification of type 2 diabetes loci. We aimed to investigate the combined effects of multiple genetic variants, alone or in combination with conventional risk factors, on type 2 diabetes and diabetes-related traits in Han Chinese. Methods We genotyped 17 variants in 17 loci in a population-based Han Chinese cohort including 3,210 unrelated individuals. A genetic risk score (GRS) was calculated on the basis of these variants. The discriminatory ability was assessed by the area under the receiver operating characteristics curve.Results The odds ratio for type 2 diabetes and hyperglycaemia with each GRS point (per risk allele) was 1.18 (95% CI 1.12-1.23, p=1.3×10−12 ) and 1.12 (95% CI 1.09-

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Section: Discussionmentioning

confidence: 99%

Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

et al. 2010

Diabetologia

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“…Instead, gene-environment interaction appears to play an important role, and genetic tests should undergo the same analysis of predictive power as phenotype-based tests. In most cases, a brief look at a patient's body weight as well as a brief family history of disease may tell a doctor much more about his or her risk for diabetes or myocardial infarction than a predictive genetic test (van der Net et al 2009;van Hoek et al 2008;Janssens and van Duijn 2008). Consequently, the additional value of predictive genetic testing may be quite limited, even if the genotype is significantly associated with the risk of the disease outcome.…”

Section: Do the Issues Call For Genetic Exceptionalism?mentioning

confidence: 99%

Points to consider in assessing and appraising predictive genetic tests

Rogowski

Grosse²,

John

et al. 2010

J Community Genet

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The use of predictive genetic tests is expanding rapidly. Given limited health care budgets and few national coverage decisions specifically for genetic tests, evidence of benefits and harms is a key requirement in decision making; however, assessing the benefits and harms of genetic tests raises a number of challenging issues. Frequently, evidence of medical benefits and harms is limited due to practical and ethical limitations of conducting meaningful clinical trials. Also, clinical endpoints frequently do not capture the benefit appropriately because the main purpose of many genetic tests is personal utility of knowing the test results, and costs of the tests and counseling can be insufficient indicators of the total costs of care. This study provides an overview of points to consider for the assessment of benefits and harms from genetic tests in an ethically and economically reflected manner. We discuss whether genetic tests are sufficiently exceptional to warrant exceptional methods for assessment and appraisal.

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“…However, the results of these 2 studies, and also a 3rd large study from the Netherlands [24], show that currently available genetic information provides only a negligible improvement in the prediction of T2DM compared to using routinely available clinical information. In addition to T2DM, commercial DNA tests are currently also available for myocardial infarction, atrial fibrillation, glaucoma, prostate cancer and breast cancer.…”

Section: Interaction Effect Of Genetic Polymorphisms In Glucokinase (mentioning

confidence: 99%

Population Genetics and Pharmacogenetics

Ong¹

2009

Yearbook of Pediatric Endocrinology

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A clear indication of the increasing prominence of population genetics in pediatric endocrinology is that these papers are becoming a regular feature across the other chapters of the Yearbook. For example, in other chapters this year look out for the summaries of major genetic association studies of type 1 diabetes [1][2][3], blood glucose levels [4,5] and bone mineral density [6], and also for an example of the emerging role of pharmacogenetics in a study of a vitamin D receptor genotype [7]. In the last year, genome-wide association (GWA) studies have continued to lead on new discoveries of single nucleotide polymorphisms (SNPs) in genes or chromosomal positions (loci) that had previously not been suspected of acting in the various disease or physiological pathways. Indeed, the pace of discovery has increased with the pooling of existing data into large international consortia for meta-analyses; type 1 diabetes [1], obesity [see Willer et al. below], and blood pressure [8] are examples of such highly fruitful collaborations. So what uses do these genetic discoveries have? There is widespread acceptance that the effect sizes of individual SNPs are much lower than were previously thought. For example, an odds ratio of >1.3 is now considered to be a large genetic effect, and nearly all of the 18 new SNPs for type 1 diabetes have odds ratios of <1.2 [1]. This means that such variants do not have, and possibly may never have, sufficient power to predict future disease risks in individuals [see Lyssenko et al. and Meigs et al. below]. New studies looking at using copy number variation or low-frequency polymorphisms may identify loci with larger effect sizes and greater ability to predict disease risks. The current discoveries have started to inform new animal models of disease, such as the Fto knockout mouse [9] [see chapter on Obesity and Weight Regulation], but it is far too early to expect any impact on drug discovery. For the time being, recognition of new loci for common traits (e.g. age at menarche) will hopefully inform the identification of rare mutations or chromosomal microdeletions that cause severe clinical conditions (e.g. precocious puberty or hypogonadotrophic hypogonadism). In combination with international clinical collaborations, this could inform genetic counseling for affected families [see Firth et al. below]. Secondly, by Mendelian randomization principles the use of these robust variants as 'instrumental variables' to demonstrate causal effects between exposures and disease outcomes is a very powerful new epidemiological tool [see comments on Sulem et al., Perry et al., Liu et al. and Eriksson et al. below]. Science 2009;324:387-389 Background: Genome-wide association (GWA) studies have identified genomic regions contributing to common human diseases, but they often do not identify the precise causative genes and sequence variants. Methods: In order to identify specific causative variants for type 1 diabetes (T1D), the authors resequenced exons and splice sites of 10 candidate genes in ...

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Predicting Type 2 Diabetes Based on Polymorphisms From Genome-Wide Association Studies

Cited by 262 publications

References 28 publications

Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

Points to consider in assessing and appraising predictive genetic tests

Population Genetics and Pharmacogenetics

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