Social Europe. No 2/87

BACKGROUND: DNA methylation is an important type of epigenetic modification involved in gene regulation. Although strong DNA methylation at promoters is widely recognized to be associated with transcriptional repression, many aspects of DNA methylation remain not fully understood, including the quantitative relationships between DNA methylation and expression levels, and the individual roles of promoter and gene body methylation. RESULTS: Here we present an integrated analysis of whole-genome bisulfite sequencing and RNA sequencing data from human samples and cell lines. We find that while promoter methylation inversely correlates with gene expression as generally observed, the repressive effect is clear only on genes with a very high DNA methylation level. By means of statistical modeling, we find that DNA methylation is indicative of the expression class of a gene in general, but gene body methylation is a better indicator than promoter methylation. These findings are general in that a model constructed from a sample or cell line could accurately fit the unseen data from another. We further find that promoter and gene body methylation have minimal redundancy, and either one is sufficient to signify low expression. Finally, we obtain increased modeling power by integrating histone modification data with the DNA methylation data, showing that neither type of information fully subsumes the other. CONCLUSION: Our results suggest that DNA methylation outside promoters also plays critical roles in gene regulation. Future studies on gene regulatory mechanisms and disease-associated differential methylation should pay more attention to DNA methylation at gene bodies and other non-promoter regions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-014-0408-0) contains supplementary material, which is available to authorized users

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Use of Net Reclassification Improvement (NRI) Method Confirms The Utility of Combined Genetic Risk Score to Predict Type 2 Diabetes

<div><p>Background</p><p>Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.</p><p>Methodology</p><p>We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).</p><p>Results</p><p>We observed consistent and significant associations of <i>IGF2BP2</i>, <i>WFS1</i>, <i>CDKAL1</i>, <i>SLC30A8</i>, <i>CDKN2A/B</i>, <i>HHEX</i>, <i>TCF7L2</i> and <i>KCNQ1</i> (8.5×10⁻¹⁸<<i>P</i><8.5×10⁻³), as well as nominal associations of <i>NOTCH2</i>, <i>JAZF1</i>, <i>KCNJ11</i> and <i>HNF1B</i> (0.05<<i>P</i><0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (<i>P</i><0.001).</p><p>Conclusion</p><p>In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).</p></div

Reclassification of predicted risk with the addition of weighted combined genetic score (CGS) including 8 variants (<i>P</i><0.05) in T2D subjects (upper panel) and healthy controls (lower panel).

<p>CGS: combined genetic score. Each cell refers to the number of subjects in the predicted risk categories. Subjects with higher predicted risk were more likely to be classified as cases. Similarly, subjects with lower predicted risk were more likely to be classified as controls. T2D subjects and healthy controls classified in the shaded cells indicated that they were correctly reclassified to higher and lower risk categories, respectively. The total number of subjects reclassified is 3,021 and the improvement classification rates are 4.36% and 6.99% for T2D subjects and healthy controls, respectively with a total improvement rate of 11.4% (4.36% +6.99%).</p

Clinical features of subjects with type 2 diabetes (T2D) in each quartile of the unweighted (upper panel) and weighted (lower panel) combined genetic score (CGS), respectively.

<p>Data are shown as N, %, mean±SD or median (minimum to maximum). Between-group comparisons of clinical characteristics were performed by χ² test or logistic regression analysis for categorical variables, and one-way ANOVA or linear regression analysis for continuous variables, as appropriate. Analysis for age at diagnosis was adjusted for sex, body mass index (BMI) and HbA_1c. Analysis for BMI and central obesity were adjusted for sex and age. Analysis for waist circumference (stratified by sex) was adjusted for age. Analysis for HbA_1c was adjusted for sex, age and BMI. Analysis for insulin therapy was adjusted for sex, age, smoking status, HbA_1c, baseline eGFR and drug usages (lipid lowering, blood pressure anti-hypertensive, ACE inhibitor and oral glucose lowering). Q1, quartile 1; Q2, quartile 2; Q3, quartile 3; Q4, quartile 4; T2D, type 2 diabetes.</p

Reclassification of predicted risk with the addition of unweighted combined genetic score (CGS) including 8 variants (<i>P</i><0.05) in T2D subjects (upper panel) and healthy controls (lower panel).

<p>CGS: combined genetic score. Each cell refers to the number of subjects in the predicted risk categories. Subjects with higher predicted risk were more likely to be classified as cases. Similarly, subjects with lower predicted risk were more likely to be classified as controls. T2D subjects and healthy controls classified in the shaded cells indicated that they were correctly reclassified to higher and lower risk categories, respectively. The total number of subjects reclassified is 2,891 and the improvement classification rates are 5.38% and 5.63% for T2D subjects and healthy controls, respectively with a total improvement rate of 11.0% (5.38% +5.63%).</p

Associations of single nucleotide polymorphisms (SNPs) of replicated genetic loci with type 2 diabetes and age at diagnosis in Chinese populations.

<p>The ORs (95% CIs) and <i>P</i> values for type 2 diabetes were calculated using logistic regression analysis adjusted for sex, age and BMI assuming an additive genetic model in 5882 cases and 2569 controls. The <i>βs</i> (SEs) and <i>P</i> values for age at diagnosis were calculated using linear regression analysis adjusted for sex, BMI and HbA_1c assuming an additive genetic model in 5882 cases. ORs (95%CIs) and <i>βs</i> (SEs) were reported with respect to the risk allele described in literature.</p