Summary

Aging is a major risk factor for chronic disease in the human population, but there is little human data on gene expression alterations that accompany the process. We examined human peripheral blood leucocyte in-vivo RNA in a large-scale transcriptomic microarray study (subjects aged 30 to 104 years). We tested associations between probe expression intensity and advancing age (adjusting for confounding factors), initially in a discovery set (n = 458), following-up findings in a replication set (n=240). We confirmed expression of key results by real-time PCR. Of 16,571 expressed probes, only 295 (2%) were robustly associated with age. Just six probes were required for a highly efficient model for distinguishing between young and old (Area Under the Curve in replication set; 95%). The focussed nature of age-related gene expression may therefore provide potential biomarkers of aging. Similarly, only 7 of 1065 biological or metabolic pathways were age-associated, in Gene Set Enrichment Analysis (GSEA), notably including the processing of messenger RNAs (mRNAs); (p<0.002, FDR q<0.05). This is supported by our observation of age-associated disruption to the balance of alternatively-expressed isoforms for selected genes, suggesting that modification of mRNA processing may be a feature of human aging.

Summary

In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10−11). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10−4). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.

Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and ‘genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ∼4000 and ∼133 000 individuals.

OBJECTIVE

Common genetic variants in GCK and TCF7L2 are associated with higher fasting glucose and type 2 diabetes in nonpregnant populations. However, their associations with glucose levels from oral glucose tolerance tests (OGTTs) in pregnancy have not been assessed in a large sample. We hypothesized that these variants are associated with quantitative measures of glycemia in pregnancy.

RESEARCH DESIGN AND METHODS

We analyzed the associations between variants rs1799884 (GCK) and rs7903146 (TCF7L2) and OGTT outcomes at 24–32 weeks' gestation in 3,811 mothers of European (U.K. and Australia) and 1,706 mothers of Asian (Thailand) ancestry from the HAPO cohort. We also tested associations with offspring birth anthropometrics.

RESULTS

The maternal GCK variant was associated with higher fasting glucose in Europeans (P = 0.001) and Thais (P < 0.0001), 1-h glucose in Europeans (P = 0.001), and 2-h glucose in Thais (P = 0.005). It was also associated with higher European offspring birth weight, fat mass, and skinfold thicknesses (P < 0.05). The TCF7L2 variant was associated with all three maternal glucose outcomes (P = 0.03, P < 0.0001, and P < 0.0001 for fasting and 1-h and 2-h glucose, respectively) in the Europeans but not in the Thais (P > 0.05). In both populations, both variants were associated with higher odds of gestational diabetes mellitus according to the new International Association of Diabetes and Pregnancy Study Groups recommendations (P = 0.001–0.08).

CONCLUSIONS

Maternal GCK and TCF7L2 variants are associated with glucose levels known to carry an increased risk of adverse pregnancy outcome in women without overt diabetes. Further studies will be important to determine the variance in maternal glucose explained by all known genetic variants.

A recent genome-wide association (GWA) study of late-onset Alzheimer's disease (LOAD) identified 15 novel single nucleotide polymorphisms (SNPs) independent of ApoE. We hypothesized that variants associated with LOAD are also associated with poor cognitive function in elderly populations. We measured additive associations between the five most strongly associated LOAD SNPs and grouped Mini Mental State Examination (MMSE) scores. Variants were genotyped in respondents (mean age 79yrs) from the Oxford Healthy Aging project (OHAP) and other sites of the MRC Cognitive Function and Aging Study (MRC-CFAS). In adjusted ordinal logistic models, two variants were associated with poorer cognitive function: rs11622883 (OR=1.14, 95%CI: 1.01 to 1.28, p=0.040) and rs505058 (OR=1.29, 95% CI: 1.02 to 1.64, p=0.036). These SNPs are close to a SERPINA gene cluster and within LMNA respectively. The mechanisms underlying the associations with cognitive impairment and LOAD require further elucidation, but both genes are interesting candidates for involvement in age-related cognitive impairment.

The identification of multiple signals at individual loci could explain additional phenotypic variance (‘missing heritability’) of common traits, and help identify causal genes. We examined gene expression levels as a model trait because of the large number of strong genetic effects acting in cis. Using expression profiles from 613 individuals, we performed genome-wide single nucleotide polymorphism (SNP) analyses to identify cis-expression quantitative trait loci (eQTLs), and conditional analysis to identify second signals. We examined patterns of association when accounting for multiple SNPs at a locus and when including additional SNPs from the 1000 Genomes Project. We identified 1298 cis-eQTLs at an approximate false discovery rate 0.01, of which 118 (9%) showed evidence of a second independent signal. For this subset of 118 traits, accounting for two signals resulted in an average 31% increase in phenotypic variance explained (Wilcoxon P< 0.0001). The association of SNPs with cis gene expression could increase, stay similar or decrease in significance when accounting for linkage disequilibrium with second signals at the same locus. Pairs of SNPs increasing in significance tended to have gene expression increasing alleles on opposite haplotypes, whereas pairs of SNPs decreasing in significance tended to have gene expression increasing alleles on the same haplotypes. Adding data from the 1000 Genomes Project showed that apparently independent signals could be potentially explained by a single association signal. Our results show that accounting for multiple variants at a locus will increase the variance explained in a substantial fraction of loci, but that allelic heterogeneity will be difficult to define without resequencing loci and functional work.

Previous studies identified 180 single nucleotide polymorphisms (SNPs) associated with adult height, explaining ∼10% of the variance. The age at which these begin to affect growth is unclear. We modelled the effect of these SNPs on birth length and childhood growth. A total of 7768 participants in the Avon Longitudinal Study of Parents and Children had data available. Individual growth trajectories from 0 to 10 years were estimated using mixed-effects linear spline models and differences in trajectories by individual SNPs and allelic score were determined. The allelic score was associated with birth length (0.026 cm increase per ‘tall’ allele, SE = 0.003, P = 1 × 10−15, equivalent to 0.017 SD). There was little evidence of association between the allelic score and early infancy growth (0–3 months), but there was evidence of association between the allelic score and later growth. This association became stronger with each consecutive growth period, per ‘tall’ allele per month effects were 0.015 SD (3 months–1 year, SE = 0.004), 0.023 SD (1–3 years, SE = 0.003) and 0.028 SD (3–10 years, SE = 0.003). By age 10, the mean height difference between individuals with ≤170 versus ≥191 ‘tall’ alleles (the top and bottom 10%) was 4.7 cm (0.8 SD), explaining ∼5% of the variance. There was evidence of associations with specific growth periods for some SNPs (rs3791675, EFEMP1 and rs6569648, L3MBTL3) and supportive evidence for previously reported age-dependent effects of HHIP and SOCS2 SNPs. SNPs associated with adult height influence birth length and have an increasing effect on growth from late infancy through to late childhood. By age 10, they explain half the height variance (∼5%) of that explained in adults (∼10%).

Objective

Common genetic variants in GCK and TCF7L2 are associated with higher fasting glucose and type 2 diabetes in non-pregnant populations. However, their associations with glucose levels from oral glucose tolerance tests (OGTT) in pregnancy have not been assessed in a large sample. We hypothesized that these variants are associated with quantitative measures of glycemia in pregnancy.

Research Design and Methods

We analyzed the associations between variants rs1799884 (GCK) and rs7903146 (TCF7L2) and OGTT outcomes at 24-32 weeks gestation from 3811 mothers of European (UK, Australia) and 1706 mothers of Asian (Thailand) ancestry from the HAPO cohort. We also tested associations with offspring birth anthropometrics.

Results

The maternal GCK variant was associated with higher fasting glucose in Europeans (P=0.001) and Thais (P<0.0001), 1-hour glucose in Europeans (P=0.001), and 2-hour glucose in Thais (P=0.005). It was also associated with higher European offspring birth weight, fat mass and skinfold thicknesses (P<0.05). The TCF7L2 variant was associated with all three maternal glucose outcomes (P=0.03, P<0.0001 and P<0.0001 for fasting, 1-hour and 2-hour glucose, respectively) in the Europeans, but not in the Thais (P>0.05). In both populations, both variants were associated with higher odds of gestational diabetes according to the new IADPSG recommendations (P=0.001-0.08).

Conclusions

Maternal GCK and TCF7L2 variants are associated with glucose levels known to carry an increased risk of adverse pregnancy outcome in women without overt diabetes. Further studies will be important to determine the variance in maternal glucose explained by all known genetic variants.

Two recent studies of the FTO gene provide more information on how it affects body mass index.

OBJECTIVE

Mutations in the HNF1A gene are the most common cause of maturity-onset diabetes of the young (MODY). There is a substantial variation in the age at diabetes diagnosis, even within families where diabetes is caused by the same mutation. We investigated the hypothesis that common polygenic variants that predispose to type 2 diabetes might account for the difference in age at diagnosis.

RESEARCH DESIGN AND METHODS

Fifteen robustly associated type 2 diabetes variants were successfully genotyped in 410 individuals from 203 HNF1A-MODY families, from two study centers in the U.K. and Norway. We assessed their effect on the age at diagnosis both individually and in a combined genetic score by summing the number of type 2 diabetes risk alleles carried by each patient.

RESULTS

We confirmed the effects of environmental and genetic factors known to modify the age at HNF1A-MODY diagnosis, namely intrauterine hyperglycemia (−5.1 years if present, P = 1.6 × 10−10) and HNF1A mutation position (−5.2 years if at least two isoforms affected, P = 1.8 × 10−2). Additionally, our data showed strong effects of sex (females diagnosed 3.0 years earlier, P = 6.0 × 10−4) and age at study (0.3 years later diagnosis per year increase in age, P = 4.7 × 10−38). There were no strong individual single nucleotide polymorphism effects; however, in the combined genetic score model, each additional risk allele was associated with 0.35 years earlier diabetes diagnosis (P = 5.1 × 10−3).

CONCLUSIONS

We show that type 2 diabetes risk variants of modest effect sizes reduce the age at diagnosis in HNF1A-MODY. This is one of the first studies to demonstrate that clinical characteristics of a monogenic disease can be modified by common polygenic variants.

Background

Levels of the proinflammatory cytokine interleukin-18 (IL-18) are raised in old age and are associated with reduced physical functioning. Previous studies have indicated that the C allele of the rs5744256 polymorphism in the IL-18 gene is strongly associated with reduced circulating IL-18 levels. This variant has previously been associated with improved locomotor performance in old age, but the finding requires independent replication.

Methods

We examined the association between the IL-18 polymorphism rs5744256 and physical functioning in three cohorts with a total of 4,107 participants aged 60–85 years: the English Longitudinal Study of Ageing, Caerphilly, and Boyd Orr. We meta-analyzed (N = 6,141) the results with data from the original paper reporting this association: Iowa-Established Populations for Epidemiological Study of the Elderly and InCHIANTI cohorts. Physical functioning was assessed by timed walks or the get up and go test. As locomotor performance tests differed between the cohorts and the distributions of times to complete the test (in seconds) were positively skewed, we used the reciprocal transformation and computed study-specific z scores.

Results

Based on the three new studies, the estimated linear regression coefficient per C allele was 0.011 (95% confidence interval [95% CI]: −0.04 to 0.06). A meta-analysis that pooled the data from all studies showed weak evidence of an effect, with a regression coefficient of 0.047 (95% CI: 0.010 to 0.083).

Conclusions

We did not replicate an association between the IL-18 rs5744256 polymorphism and the physical function in people aged 60–85 years. However, pooling data from all studies suggested a weak association of the C allele of the rs5744256 single nucleotide polymorphism on improving walking times in old age.

Background

Variation in the effects of genetic variants on physiological traits over time or with age may alter the trajectories of these traits. However, few studies have investigated this possibility for variants associated with type 2 diabetes or obesity, and these show little consensus. We aimed to characterise the possible longitudinal associations of common diabetes-susceptibility variants in the KCNJ11, PPARG, TCF7L2, IGF2BP2, CDKAL1, SLC30A8 and HHEX gene loci, with fasting glucose level; and of an obesity-associated variant in the FTO gene, with body mass index (BMI).

Methods

The study analysed data from the Busselton Health Study (n = 4,554). Cross-sectional association analyses included family data and used the total association test. Longitudinal association analyses of unrelated participant data (n = 2,864) used linear mixed-effects models.

Results

In cross-sectional analyses, we observed associations of the T allele at the IGF2BP2 single nucleotide polymorphism (SNP) rs4402960 with raised fasting glucose (p = 0.045), and the A allele at the FTO SNP rs9939609 with raised BMI (p = 0.003). Longitudinal analyses showed no significant associations between SNPs and changes in fasting glucose or BMI in the same individuals, either over mean follow-up times of 18.7 and 21.8 years respectively, or with age during adulthood.

Conclusions

There was no indication that the effects of common type 2 diabetes variants on fasting glucose varied with age during adulthood or over time.

Aims and Hypothesis

Circulating β-carotene levels are inversely associated with type 2 diabetes risk, but the causal direction of this association is not certain. In this study we used a Mendelian Randomization approach to provide evidence for or against the causal role of the anti-oxidant vitamin β-carotene in type 2 diabetes.

Methods

We used a common polymorphism (rs6564851) near the β-carotene 15,15'-Monooxygenase 1 (BCMO1) gene that is strongly associated with circulating β-carotene levels (P = 2×10−24) - each G allele is associated with a 0.27 standard deviation increase in levels. We used data from the InCHIANTI study and the ULSAM study to estimate the association between β-carotene levels and type 2 diabetes. We next used a triangulation approach to estimate the expected effect of rs6564851 on type 2 diabetes risk, and compared this to the observed effect using data from 4549 type 2 diabetes cases and 5579 controls from the DIAGRAM consortium.

Results

A 0.27 standard deviation increase in β-carotene levels is associated with an odds ratio of 0.90 (0.86–0.95) for type 2 diabetes in the InCHIANTI study. This association is similar to that of the ULSAM study, OR (0.90 (0.84–0.97)). In contrast there was no association between rs6564851 and type 2 diabetes (OR 0.98 (0.93–1.04, P = 0.58), and this effect size was smaller than that expected given the known associations between rs6564851 and β-carotene levels and the associations between β-carotene levels and type 2 diabetes.

Conclusion

Our Mendelian Randomization studies are in keeping with randomized controlled trials that suggest β-carotene is not causally protective against type 2 diabetes.

For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.

Background

A common variant at chromosome 9p21 (tagged by the rs1333049 or rs10757278 SNP) is strongly associated with Myocardial Infarction (MI) and major arterial aneurysms. An association with Peripheral Arterial Disease (PAD) was also reported in a sample aged <75 years, but this disappeared on removal of respondents with a MI history, resulting in an odds ratio for PAD of 1.09 (p=0.075). We aimed to estimate the association of this variant with Ankle Brachial Index (ABI) and PAD in three older populations.

Methods and Results

We used data from the InCHIANTI, Baltimore Longitudinal Study of Aging and Health, Aging and Body Composition studies. In 2,630 Caucasian individuals (mean age 76.4 years) the C allele at rs1333049 was associated with lower mean ABI measures and with increased prevalence of PAD. These associations remained after removal of baseline and incident MI cases over a 6 year follow-up for both ABI (−0.017 ABI units, 95% CI: −0.03- −0.01, p=1.3×10−4) and PAD (per allele OR: 1.29, 95% CI: 1.06–1.56, p=0.012). These associations also remained after adjustment for known atherosclerosis risk factors including Diabetes Mellitus, smoking, hypercholesterolemia and hypertension.

Conclusions

The C allele at rs1333049 is associated with an increased prevalence of Peripheral Arterial Disease and lower mean Ankle Brachial Index. This association was independent of the presence of diagnosed MI and atherosclerotic risk factors in 3 older Caucasian populations.

OBJECTIVE

Linkage of the chromosome 1q21–25 region to type 2 diabetes has been demonstrated in multiple ethnic groups. We performed common variant fine-mapping across a 23-Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.

RESEARCH DESIGN AND METHODS

In all, 5,290 single nucleotide polymorphisms (SNPs) were successfully genotyped in 3,179 type 2 diabetes case and control subjects from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q linkage. After imputation, we estimate ∼80% coverage of common variation across the region (r 2 > 0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in ∼8,500 case subjects and 12,400 control subjects.

RESULTS

Association mapping of the 23-Mb region identified two strong signals, both of which were restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, odds ratio 1.38 [95% CI 1.21–1.57], P = 1.4 × 10−6, in 999 case subjects and 1,190 control subjects); the second mapped within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, odds ratio 1.48 [1.18–1.76], P = 1.0 × 10−5, under a dominant model). However, there was no evidence for association at either signal on replication, and, across all data (>24,000 subjects), there was no indication that these variants were causally related to type 2 diabetes status.

CONCLUSIONS

Detailed fine-mapping of the 23-Mb region of replicated linkage has failed to identify common variant signals contributing to the observed signal. Future studies should focus on identification of causal alleles of lower frequency and higher penetrance.

OBJECTIVE

Recent genome-wide association studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.

RESEARCH DESIGN AND METHODS

We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases.

RESULTS

After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top Padj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.

CONCLUSIONS

Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.

For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.

Background

The TNF/LTA locus has been a long-standing T2D candidate gene. Several studies have examined association of TNF/LTA SNPs with T2D but the majority have been small-scale and produced no convincing evidence of association. The purpose of this study is to examine T2D association of tag SNPs in the TNF/LTA region capturing the majority of common variation in a large-scale sample set of UK/Irish origin.

Methods

This study comprised a case-control (1520 cases and 2570 control samples) and a family-based component (423 parent-offspring trios). Eleven tag SNPs (rs928815, rs909253, rs746868, rs1041981 (T60N), rs1800750, rs1800629 (G-308A), rs361525 (G-238A), rs3093662, rs3093664, rs3093665, and rs3093668) were selected across the TNF/LTA locus and genotyped using a fluorescence-based competitive allele specific assay. Quality control of the obtained genotypes was performed prior to single- and multi-point association analyses under the additive model.

Results

We did not find any consistent SNP associations with T2D in the case-control or family-based datasets.

Conclusions

The present study, designed to analyse a set of tag SNPs specifically selected to capture the majority of common variation in the TNF/LTA gene region, found no robust evidence for association with T2D. To investigate the presence of smaller effects of TNF/LTA gene variation with T2D, a large-scale meta-analysis will be required.

OBJECTIVE

To investigate whether variation in the melatonin receptor 1B gene (MTNR1B), recently identified as a common genetic determinant of fasting glucose levels in healthy, diabetes free individuals is associated with measures of beta-cell function and whole-body insulin sensitivity.

RESEARCH DESIGN AND METHODS

A total of 1,276 healthy individuals of European ancestry were studied at 19 centres of the RISC study. Whole-body insulin sensitivity (M/I) was assessed by hyperinsulinaemic-euglycemic clamp and indices of beta-cell function were derived from a 75-g oral glucose tolerance test (including 30-min insulin response and glucose sensitivity). We studied rs10830963 in MTNR1B using additive genetic models, adjusting for age, sex, and recruitment centre.

RESULTS

The minor (G) allele of rs10830963 in MTNR1B (frequency 0.30 in HapMap CEU; 0.29 in RISC participants) was associated with higher levels of fasting plasma glucose (standardized beta (95% CI) 0.17 (0.085; 0.25) per G allele; p=5.8×10e-5), consistent with recent observations. In addition, the G-allele was significantly associated with lower early insulin response (−0.19 (−0.28; −0.10); p=1.7×10e-5), as well as with decreased beta-cell glucose sensitivity (−0.11 (−0.20; −0.027); p=0.010). No associations were observed with clamp assessed insulin sensitivity (p=0.15) or different measures of body size (all p-values >0.7).

CONCLUSIONS

Genetic variation in MTNR1B is associated with defective early insulin response and decreased beta-cell glucose sensitivity, which may contribute to the higher glucose levels of non-diabetic individuals carrying the minor G allele of rs10830963 in MTNR1B.

OBJECTIVE—This study examined how differences in the BMI distribution of type 2 diabetic case subjects affected genome-wide patterns of type 2 diabetes association and considered the implications for the etiological heterogeneity of type 2 diabetes.

RESEARCH DESIGN AND METHODS—We reanalyzed data from the Wellcome Trust Case Control Consortium genome-wide association scan (1,924 case subjects, 2,938 control subjects: 393,453 single-nucleotide polymorphisms [SNPs]) after stratifying case subjects (into “obese” and “nonobese”) according to median BMI (30.2 kg/m2). Replication of signals in which alternative case-ascertainment strategies generated marked effect size heterogeneity in type 2 diabetes association signal was sought in additional samples.

RESULTS—In the “obese-type 2 diabetes” scan, FTO variants had the strongest type 2 diabetes effect (rs8050136: relative risk [RR] 1.49 [95% CI 1.34–1.66], P = 1.3 × 10−13), with only weak evidence for TCF7L2 (rs7901695 RR 1.21 [1.09–1.35], P = 0.001). This situation was reversed in the “nonobese” scan, with FTO association undetectable (RR 1.07 [0.97–1.19], P = 0.19) and TCF7L2 predominant (RR 1.53 [1.37–1.71], P = 1.3 × 10−14). These patterns, confirmed by replication, generated strong combined evidence for between-stratum effect size heterogeneity (FTO: PDIFF = 1.4 × 10−7; TCF7L2: PDIFF = 4.0 × 10−6). Other signals displaying evidence of effect size heterogeneity in the genome-wide analyses (on chromosomes 3, 12, 15, and 18) did not replicate. Analysis of the current list of type 2 diabetes susceptibility variants revealed nominal evidence for effect size heterogeneity for the SLC30A8 locus alone (RRobese 1.08 [1.01–1.15]; RRnonobese 1.18 [1.10–1.27]: PDIFF = 0.04).

CONCLUSIONS—This study demonstrates the impact of differences in case ascertainment on the power to detect and replicate genetic associations in genome-wide association studies. These data reinforce the notion that there is substantial etiological heterogeneity within type 2 diabetes.

Objective

Linkage of the chromosome 1q21-25 region to type 2 diabetes has been demonstrated in multiple ethnic groups. We performed common variant fine-mapping across a 23Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.

Research Design and Methods

In all, 5,290 SNPs were successfully genotyped in 3,179 T2D cases and controls from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q-linkage. Following imputation, we estimate ~80% coverage of common variation across the region (r2>0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in approximately 8,500 cases and 12,400 controls.

Results

Association mapping of the 23Mb region identified two strong signals, both restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, OR 1.38 (95% CI, 1.21-1.57), p=1.4×10-6 in 999 cases and 1,190 controls): the second within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, OR 1.48 [1.18-1.76], p=1.0×10-5, under a dominant model). However, there was no evidence for association at either signal on replication, and, across all data (>24,000 subjects), no indication that these variants were causally-related to T2D status.

Conclusion

Detailed fine-mapping of the 23Mb region of replicated linkage has failed to identify common variant signals contributing to the observed signal. Future studies should focus on identification of causal alleles of lower frequency and higher penetrance.

OBJECTIVE—Single nucleotide polymorphisms (SNPs) in the P2 promoter region of HNF4A were originally shown to be associated with predisposition for type 2 diabetes in Finnish, Ashkenazi, and, more recently, Scandinavian populations, but they generated conflicting results in additional populations. We aimed to investigate whether data from a large-scale mapping approach would replicate this association in novel Ashkenazi samples and in U.K. populations and whether these data would allow us to refine the association signal.

RESEARCH DESIGN AND METHODS—Using a dense linkage disequilibrium map of 20q, we selected SNPs from a 10-Mb interval centered on HNF4A. In a staged approach, we first typed 4,608 SNPs in case-control populations from four U.K. populations and an Ashkenazi population (n = 2,516). In phase 2, a subset of 763 SNPs was genotyped in 2,513 additional samples from the same populations.

RESULTS—Combined analysis of both phases demonstrated association between HNF4A P2 SNPs (rs1884613 and rs2144908) and type 2 diabetes in the Ashkenazim (n = 991; P < 1.6 × 10−6). Importantly, these associations are significant in a subset of Ashkenazi samples (n = 531) not previously tested for association with P2 SNPs (odds ratio [OR] ∼1.7; P < 0.002), thus providing replication within the Ashkenazim. In the U.K. populations, this association was not significant (n = 4,022; P > 0.5), and the estimate for the OR was much smaller (OR 1.04; [95%CI 0.91–1.19]).

CONCLUSIONS—These data indicate that the risk conferred by HNF4A P2 is significantly different between U.K. and Ashkenazi populations (P < 0.00007), suggesting that the underlying causal variant remains unidentified. Interactions with other genetic or environmental factors may also contribute to this difference in risk between populations.

OBJECTIVES—Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type 2 diabetes variants to differentiate between type 2 diabetic case and control subjects.

RESEARCH DESIGN AND METHODS—We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC).

RESULTS—Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally increased this to 0.80.

CONCLUSIONS—Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However, the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.