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- Guey, Lin T., et al.
(author)
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Power in the Phenotypic Extremes: A Simulation Study of Power in Discovery and Replication of Rare Variants
- 2011
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In: Genetic Epidemiology. - : Wiley. - 0741-0395. ; 35:4, s. 236-246
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Journal article (peer-reviewed)abstract
- Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype-suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol. 35: 236-246, 2011. (c) 2011 Wiley-Liss, Inc.
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- Lyssenko, Valeriya, et al.
(author)
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Clinical risk factors, DNA variants, and the development of type 2 diabetes.
- 2008
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In: New England Journal of Medicine. - 0028-4793. ; 359:21, s. 2220-2232
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Journal article (peer-reviewed)abstract
- BACKGROUND: Type 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors. We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. METHODS: We genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects. Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years. We also studied the effect of genetic variants on changes in insulin secretion and action over time. RESULTS: Strong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action. Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function. The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiver-operating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P=1.0x10(-4)). The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. CONCLUSIONS: As compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes. The value of genetic factors increased with an increasing duration of follow-up.
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| 5. |
- Saxena, Richa, et al.
(author)
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Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge
- 2010
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:2, s. 142-148
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Journal article (peer-reviewed)abstract
- Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).
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| 6. |
- Saxena, Richa, et al.
(author)
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Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
- 2007
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In: Science. - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 316:5829, s. 1331-1336
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Journal article (peer-reviewed)abstract
- New strategies for prevention and treatment of type 2 diabetes (T2D) require improved insight into disease etiology. We analyzed 386,731 common single-nucleotide polymorphisms (SNPs) in 1464 patients with T2D and 1467 matched controls, each characterized for measures of glucose metabolism, lipids, obesity, and blood pressure. With collaborators (FUSION and WTCCC/UKT2D), we identified and confirmed three loci associated with T2D - in a noncoding region near CDKN2A and CDKN2B, in an intron of IGF2BP2, and an intron of CDKAL1 - and replicated associations near HHEX and in SLC30A8 found by a recent whole-genome association study. We identified and confirmed association of a SNP in an intron of glucokinase regulatory protein (GCKR) with serum triglycerides. The discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genome-wide association studies to provide potentially important clues to the pathogenesis of common diseases.
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| 7. |
- Voight, Benjamin F., et al.
(author)
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Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis
- 2010
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:7, s. 579-589
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Journal article (peer-reviewed)abstract
- By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 x 10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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| 8. |
- Yasuda, Kazuki, et al.
(author)
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Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus
- 2008
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 40:9, s. 1092-1097
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Journal article (peer-reviewed)abstract
- We carried out a multistage genome-wide association study of type 2 diabetes mellitus in Japanese individuals, with a total of 1,612 cases and 1,424 controls and 100,000 SNPs. The most significant association was obtained with SNPs in KCNQ1, and dense mapping within the gene revealed that rs2237892 in intron 15 showed the lowest P value (6.7 x 10(-13), odds ratio (OR) = 1.49). The association of KCNQ1 with type 2 diabetes was replicated in populations of Korean, Chinese and European ancestry as well as in two independent Japanese populations, and meta-analysis with a total of 19,930 individuals (9,569 cases and 10,361 controls) yielded a P value of 1.7 x 10(-42) (OR = 1.40; 95% CI = 1.34-1.47) for rs2237892. Among control subjects, the risk allele of this polymorphism was associated with impairment of insulin secretion according to the homeostasis model assessment of beta-cell function or the corrected insulin response. Our data thus implicate KCNQ1 as a diabetes susceptibility gene in groups of different ancestries.
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| 9. |
- Locke, Adam E, et al.
(author)
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Genetic studies of body mass index yield new insights for obesity biology.
- 2015
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 518:7538, s. 197-401
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Journal article (peer-reviewed)abstract
- Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
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