Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have emerged as a powerful approach for identifying susceptibility loci associated with polygenetic diseases such as type 2 diabetes mellitus (T2DM). However, it is still a daunting task to prioritize single nucleotide polymorphisms (SNPs) from GWAS for further replication in different population. Several recent studies have shown that genetic variation often affects gene-expression at proximal (<it>cis</it>) as well as distal (<it>trans</it>) genomic locations by different mechanisms such as altering rate of transcription or splicing or transcript stability.</p> <p>Methods</p> <p>To prioritize SNPs from GWAS, we combined results from two GWAS related to T2DM, the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC), with genome-wide expression data from pancreas, adipose tissue, liver and skeletal muscle of individuals with or without T2DM or animal models thereof to identify T2DM susceptibility loci.</p> <p>Results</p> <p>We identified 1,170 SNPs associated with T2DM with <it>P </it>< 0.05 in both GWAS and 243 genes that were located in the vicinity of these SNPs. Out of these 243 genes, we identified 115 differentially expressed in publicly available gene expression profiling data. Notably five of them, <it>IGF2BP2</it>, <it>KCNJ11</it>, <it>NOTCH2</it>, <it>TCF7L2 </it>and <it>TSPAN8</it>, have subsequently been shown to be associated with T2DM in different populations. To provide further validation of our approach, we reversed the approach and started with 26 known SNPs associated with T2DM and related traits. We could show that 12 (57%) (<it>HHEX</it>, <it>HNF1B</it>, <it>IGF2BP2</it>, <it>IRS1</it>, <it>KCNJ11</it>, <it>KCNQ1</it>, <it>NOTCH2</it>, <it>PPARG</it>, <it>TCF7L2</it>, <it>THADA</it>, <it>TSPAN8 </it>and <it>WFS1</it>) out of 21 genes located in vicinity of these SNPs were showing aberrant expression in T2DM from the gene expression profiling studies.</p> <p>Conclusions</p> <p>Utilizing of gene expression profiling data from different tissues of individuals with or without T2DM or animal models thereof is a powerful tool for prioritizing SNPs from WGAS for further replication studies.</p

The insertion/deletion variation in the α(2B)-adrenoceptor does not seem to modify the risk for acute myocardial infarction, but may modify the risk for hypertension in sib-pairs from families with type 2 diabetes

BACKGROUND: An insertion/deletion polymorphism in the α(2B)-adrenoceptor (AR) has been associated with the risk for acute myocardial infarction (AMI) and sudden cardiac death. In this study we tested whether this polymorphism is associated with the risk for AMI among members of families with type 2 diabetes. METHODS: 154 subjects with a history of AMI were matched for age and sex with one of their siblings who did not have a history of AMI. The prevalence of the genotypes of the α(2B)-AR insertion/deletion polymorphism was compared between the siblings using McNemar's test. We also explored the data to see whether this genetic variation affects the risk for hypertension by using logistic regression models in the two subpopulations of subjects, with and without a history of AMI. RESULTS: Among all study subjects, 73 (24%) carried the α(2B)-AR deletion/deletion genotype, 103 (33%) carried the insertion/insertion genotype, and 132 (43%) were heterozygous. The distribution of genotypes of the α(2B)-AR insertion/deletion variation in the group of subjects with a history of AMI and their phenotype-discordant siblings did not statistically significantly differ from that expected by random distribution (p = 0.52): the deletion/deletion genotype was carried by 34 subjects with AMI (22%), and by 39 subjects without AMI (25%). Neither did we observe any significant difference in deletion allele frequencies of the α(2B)-AR insertion/deletion polymorphism between patients with a history of AMI (0.44) and their sib-pair controls (0.46, p = 0.65). In an exploratory analysis, the α(2B)-AR deletion/deletion genotype was associated with increased odds for hypertension compared with subjects carrying any of the other genotypes. CONCLUSIONS: The deletion/deletion genotype of the α(2B)-AR does not emerge in this study as a risk factor for AMI among members of families with type 2 diabetes; however, it might be involved in the development of hypertension

Whole Grain, Bran, and Germ Intake and Risk of Type 2 Diabetes: A Prospective Cohort Study and Systematic Review

Background: Control of body weight by balancing energy intake and energy expenditure is of major importance for the prevention of type 2 diabetes, but the role of specific dietary factors in the etiology of type 2 diabetes is less well established. We evaluated intakes of whole grain, bran, and germ in relation to risk of type 2 diabetes in prospective cohort studies. Methods and Findings: We followed 161,737 US women of the Nurses' Health Studies (NHSs) I and II, without history of diabetes, cardiovascular disease, or cancer at baseline. The age at baseline was 37–65 y for NHSI and 26–46 y for NHSII. Dietary intakes and potential confounders were assessed with regularly administered questionnaires. We documented 6,486 cases of type 2 diabetes during 12–18 y of follow-up. Other prospective cohort studies on whole grain intake and risk of type 2 diabetes were identified in searches of MEDLINE and EMBASE up to January 2007, and data were independently extracted by two reviewers. The median whole grain intake in the lowest and highest quintile of intake was, respectively, 3.7 and 31.2 g/d for NHSI and 6.2 and 39.9 g/d for NHSII. After adjustment for potential confounders, the relative risks (RRs) for the highest as compared with the lowest quintile of whole grain intake was 0.63 (95% confidence interval [CI] 0.57–0.69) for NHSI and 0.68 (95% CI 0.57–0.81) for NHSII (both: p-value, test for trend <0.001). After further adjustment for body mass index (BMI), these RRs were 0.75 (95% CI 0.68–0.83; p-value, test for trend <0.001) and 0.86 (95% CI 0.72–1.02; p-value, test for trend 0.03) respectively. Associations for bran intake were similar to those for total whole grain intake, whereas no significant association was observed for germ intake after adjustment for bran. Based on pooled data for six cohort studies including 286,125 participants and 10,944 cases of type 2 diabetes, a two-serving-per-day increment in whole grain consumption was associated with a 21% (95% CI 13%–28%) decrease in risk of type 2 diabetes after adjustment for potential confounders and BMI. Conclusions: Whole grain intake is inversely associated with risk of type 2 diabetes, and this association is stronger for bran than for germ. Findings from prospective cohort studies consistently support increasing whole grain consumption for the prevention of type 2 diabetes

Relationship between insulin sensitivity and gene expression in human skeletal muscle

BackgroundInsulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood.MethodsTo explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).ResultsWe identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g.PPARGC1A.ConclusionsThe muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g.SIRT2, and genes regulating autophagy and mTOR signaling, e.g.FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.Peer reviewe

A multigenerational study on phenotypic consequences of the most common causal variant of HNF1A-MODY

Correction: Volume65, Issue5 Page: 912-912 DOI: 10.1007/s00125-022-05663-z Published: MAY 2022 Early Access: MAR 2022Aims/hypothesis Systematic studies on the phenotypic consequences of variants causal of HNF1A-MODY are rare. Our aim was to assess the phenotype of carriers of a single HNF1A variant and genetic and clinical factors affecting the clinical spectrum. Methods We conducted a family-based multigenerational study by comparing heterozygous carriers of the HNF1A p.(Gly292fs) variant with the non-carrier relatives irrespective of diabetes status. During more than two decades, 145 carriers and 131 non-carriers from 12 families participated in the study, and 208 underwent an OGTT at least once. We assessed the polygenic risk score for type 2 diabetes, age at onset of diabetes and measures of body composition, as well as plasma glucose, serum insulin, proinsulin, C-peptide, glucagon and NEFA response during the OGTT. Results Half of the carriers remained free of diabetes at 23 years, one-third at 33 years and 13% even at 50 years. The median age at diagnosis was 21 years (IQR 17-35). We could not identify clinical factors affecting the age at conversion; sex, BMI, insulin sensitivity or parental carrier status had no significant effect. However, for 1 SD unit increase of a polygenic risk score for type 2 diabetes, the predicted age at diagnosis decreased by 3.2 years. During the OGTT, the carriers had higher levels of plasma glucose and lower levels of serum insulin and C-peptide than the non-carriers. The carriers were also leaner than the non-carriers (by 5.0 kg, p=0.012, and by 2.1 kg/m(2) units of BMI, p=2.2 x 10(-4), using the first adult measurements) and, possibly as a result of insulin deficiency, demonstrated higher lipolytic activity (with medians of NEFA at fasting 621 vs 441 mu mol/l, p=0.0039; at 120 min during an OGTT 117 vs 64 mu mol/l, p=3.1 x 10(-5)). Conclusions/interpretation The most common causal variant of HNF1A-MODY, p.(Gly292fs), presents not only with hyperglycaemia and insulin deficiency, but also with increased lipolysis and markedly lower adult BMI. Serum insulin was more discriminative than C-peptide between carriers and non-carriers. A considerable proportion of carriers develop diabetes after young adulthood. Even among individuals with a monogenic form of diabetes, polygenic risk of diabetes modifies the age at onset of diabetes.Peer reviewe

Interaction between the obesity-risk gene FTO and the dopamine D2 receptor gene ANKK1/TaqIA on insulin sensitivity

Variations in FTO are the strongest common genetic determinants of adiposity, and may partly act by influencing dopaminergic signalling in the brain leading to altered reward processing that promotes increased food intake. Therefore, we investigated the impact of such an interaction on body composition, and peripheral and brain insulin sensitivity. Participants from the Tubingen Family study (n = 2245) and the Malmo Diet and Cancer study (n = 2921) were genotyped for FTO SNP rs8050136 and ANKK1 SNP rs1800497. Insulin sensitivity in the caudate nucleus, an important reward area in the brain, was assessed by fMRI in 45 participants combined with intranasal insulin administration. We found evidence of an interaction between variations in FTO and an ANKK1 polymorphism that associates with dopamine (D2) receptor density. In cases of reduced D2 receptor availability, as indicated by the ANKK1 polymorphism, FTO variation was associated with increased body fat and waist circumference and reduced peripheral insulin sensitivity. Similarly, altered central insulin sensitivity was observed in the caudate nucleus in individuals with the FTO obesity-risk allele and diminished D2 receptors. The effects of variations in FTO are dependent on dopamine D2 receptor density (determined by the ANKK1 polymorphism). Carriers of both risk alleles might, therefore, be at increased risk of obesity and diabetes.Peer reviewe

Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency 1%) predicted damaging coding variation by using sequence data from 170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.Peer reviewe

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.We acknowledge all the SUMMIT partners (http://www.imi-summit.eu/) for their assistance with this project. This work was funded by the Innovative Medicine Initiative under grant agreement no. IMI/115006 (the SUMMIT consortium) and the Go-DARTS cohort was funded by the Chief Scientists Office Scotland.This is the accepted manuscript of a paper published in Kidney International (Looker et al., Kidney International, 2015 doi: 10.1038/ki.2015.199). The final version is available at http://dx.doi.org/10.1038/ki.2015.19

Evaluating drug targets through human loss-of-function genetic variation

Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development.Peer reviewe

The mutational constraint spectrum quantified from variation in 141,456 humans

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes(1). Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.Peer reviewe