A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

BTOB: Extending the Biased GWAS to Bivariate GWAS

In recent years, a number of literatures published large-scale genome-wide association studies (GWASs) for human diseases or traits while adjusting for other heritable covariate. However, it is known that these GWASs are biased, which may lead to biased genetic estimates or even false positives. In this study, we provide a method called "BTOB" which extends the biased GWAS to bivariate GWAS by integrating the summary association statistics from the biased GWAS and the GWAS for the adjusted heritable covariate. We employ the proposed BTOB method to analyze the summary association statistics from the large scale meta-GWASs for waist-to-hip ratio (WHR) and body mass index (BMI), and show that the proposed approach can help identify more susceptible genes compared with the corresponding univariate GWASs. Theoretical results and simulations also confirm the validity and efficiency of the proposed BTOB method.

Defining the underlying defect in insulin action in type 2 diabetes

Insulin resistance is one of the earliest defects in the pathogenesis of type 2 diabetes. Over the past 50 years, elucidation of the insulin signalling network has provided important mechanistic insights into the abnormalities of glucose, lipid and protein metabolism that underlie insulin resistance. In classical target tissues (liver, muscle and adipose tissue), insulin binding to its receptor initiates a broad signalling cascade mediated by changes in phosphorylation, gene expression and vesicular trafficking that result in increased nutrient utilisation and storage, and suppression of catabolic processes. Insulin receptors are also expressed in non-classical targets, such as the brain and endothelial cells, where it helps regulate appetite, energy expenditure, reproductive hormones, mood/behaviour and vascular function. Recent progress in cell biology and unbiased molecular profiling by mass spectrometry and DNA/RNA-sequencing has provided a unique opportunity to dissect the determinants of insulin resistance in type 2 diabetes and the metabolic syndrome; best studied are extrinsic factors, such as circulating lipids, amino acids and other metabolites and exosomal microRNAs. More challenging has been defining the cell-intrinsic factors programmed by genetics and epigenetics that underlie insulin resistance. In this regard, studies using human induced pluripotent stem cells and tissues point to cell-autonomous alterations in signalling super-networks, involving changes in phosphorylation and gene expression both inside and outside the canonical insulin signalling pathway. Understanding how these multi-layered molecular networks modulate insulin action and metabolism in different tissues will open new avenues for therapy and prevention of type 2 diabetes and its associated pathologies.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

Investigation of glycaemic traits in psychiatric disorders using Mendelian randomisation revealed a causal relationship with anorexia nervosa

Data from observational studies have suggested an involvement of abnormal glycaemic regulation in the pathophysiology of psychiatric illness. This may be an attractive target for clinical intervention as glycaemia can be modulated by both lifestyle factors and pharmacological agents. However, observational studies are inherently confounded, and therefore, causal relationships cannot be reliably established. We employed genetic variants rigorously associated with three glycaemic traits (fasting glucose, fasting insulin, and glycated haemoglobin) as instrumental variables in a two-sample Mendelian randomisation analysis to investigate the causal effect of these measures on the risk for eight psychiatric disorders. A significant protective effect of a natural log transformed pmol/L increase in fasting insulin levels was observed for anorexia nervosa after the application of multiple testing correction (OR = 0.48 [95% CI: 0.33-0.71]-inverse-variance weighted estimate). There was no consistently strong evidence for a causal effect of glycaemic factors on the other seven psychiatric disorders considered. The relationship between fasting insulin and anorexia nervosa was supported by a suite of sensitivity analyses, with no statistical evidence of instrument heterogeneity or horizontal pleiotropy. Further investigation is required to explore the relationship between insulin levels and anorexia.

Genetic Evidence Supporting Fibroblast Growth Factor 21 Signalling as a Pharmacological Target for Cardiometabolic Outcomes and Alzheimer's Disease

Fibroblast growth factor 21 (FGF21) is a human metabolic hormone whose effects include modification of macronutrient preference and energy homeostasis. In animal models, FGF21 has been shown to have beneficial effects on cardiometabolic outcomes, Alzheimer’s disease risk and lifespan. In this study, the single-nucleotide polymorphism rs838133 in the FGF21 gene region was leveraged to investigate the potential clinical effects of targeting FGF21. The FGF21 G allele was associated with lower intakes of total sugars and alcohol, and higher intakes of protein and fat as well as favourable with lipid levels, blood pressure traits, waist-to-hip ratio, systemic inflammation, cardiovascular outcomes, Alzheimer’s disease risk and lifespan. These findings may be used to anticipate the effects of pharmacologically increasing FGF21 signalling.

Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons

Sharing genome data in a privacy-preserving way stands as a major bottleneck in front of the scientific progress promised by the big data era in genomics. A community-driven protocol named genomic data-sharing beacon protocol has been widely adopted for sharing genomic data. The system aims to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. However, beacon protocol was recently shown to be vulnerable against membership inference attacks. In this paper, we show that privacy threats against genomic data sharing beacons are not limited to membership inference. We identify and analyze a novel vulnerability of genomic data-sharing beacons: genome reconstruction. We show that it is possible to successfully reconstruct a substantial part of the genome of a victim when the attacker knows the victim has been added to the beacon in a recent update. In particular, we show how an attacker can use the inherent correlations in the genome and clustering techniques to run such an attack in an efficient and accurate way. We also show that even if multiple individuals are added to the beacon during the same update, it is possible to identify the victim's genome with high confidence using traits that are easily accessible by the attacker (e.g., eye color or hair type). Moreover, we show how a reconstructed genome using a beacon that is not associated with a sensitive phenotype can be used for membership inference attacks to beacons with sensitive phenotypes (e.g., HIV+). The outcome of this work will guide beacon operators on when and how to update the content of the beacon and help them (along with the beacon participants) make informed decisions.

A Mendelian Randomization Study of Plasma Homocysteine Levels and Cerebrovascular and Neurodegenerative Diseases

Background: Homocysteine (Hcy) is a toxic amino acid and hyperhomocysteinemia (HHcy) was reported to be associated with both cerebrovascular disease and neurodegenerative disease. Our aim was to assess the causal link between plasma Hcy level and cerebrovascular and neurodegenerative diseases through a Mendelian randomization (MR) study.

Methods: A two-sample MR study was performed to infer the causal link. We extracted the genetic variants (SNPs) associated with plasma Hcy level from a large genome-wide association study (GWAS) meta-analysis. The main MR analysis was performed using the inverse variance-weighted method. Additional analyses were further performed using MR-Egger intercept and Cochran’s Q statistic to detect the heterogeneity or pleiotropy of our findings.

Results: Thirteen Hcy-associated SNPs were selected as instrumental variables. The results showed evidence of a causal link between plasma Hcy level and ischemic stroke (IS) caused by small artery occlusion (SAS, OR = 1.329, 95% CI 1.047–1.612, p = 0.048). Meanwhile, there was no evidence of association between plasma Hcy level and other types of IS, transient ischemic attack (TIA), or neurodegenerative disease. The MR-Egger intercept test indicated no evidence of directional pleiotropy. Results of additional MR analysis indicated that blood pressure (BP) and type 2 diabetes mellitus (T2DM) serve as influencers in the association.

Conclusion: The MR study found a little causal link between plasma Hcy level and SAS. The link is likely to be influenced by other risk factors like BP and T2DM.

Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.

Genetic risk scores for cardiometabolic traits in sub-Saharan African populations

BACKGROUND

There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. GRS for cardiometabolic traits were evaluated in sub-Saharan Africans in comparison with African Americans and European Americans.

METHODS

We evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n = 5200), African Americans (AA; n = 9139) and European Americans (EUR; n = 9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and the increase in discriminatory ability over traditional risk factors [age, sex and body mass index (BMI)], with adjustment for ancestry-derived principal components.

RESULTS

Across all traits, GRS showed up to a 5-fold and 20-fold greater predictive utility in EUR relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percentage increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EUR, 26.6% to 65.8% among AA and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EUR relative to AA and up to 44-fold greater in EUR relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EUR.

CONCLUSIONS

This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiple ethnic populations in genomic studies to ensure equitable clinical translation of GRS.

Causal effect of education on type 2 diabetes: A network Mendelian randomization study

BACKGROUND

The causality between education and type 2 diabetes (T2DM) remains unclear.

AIM

To identify the causality between education and T2DM and the potential metabolic risk factors [coronary heart disease (CHD), total cholesterol, low-density lipoprotein, triglycerides (TG), body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), fasting insulin, fasting glucose, and glycated hemoglobin] from summarized genome-wide association study (GWAS) data used a network Mendelian randomization (MR).

METHODS

Two-sample MR and network MR were performed to obtain the causality between education-T2DM, education-mediator, and mediator-T2DM. Summary statistics from the Social Science Genetic Association Consortium (discovery data) and Neale Lab consortium (replication data) were used for education and DIAGRAMplusMetabochip for T2DM.

RESULTS

The odds ratio for T2DM was 0.392 (95%CI: 0.263-0.583) per standard deviation increase (3.6 years) in education by the inverse variance weighted method, without heterogeneity or horizontal pleiotropy. Education was genetically associated with CHD, TG, BMI, WC, and WHR in the discovery phase, yet only the results for CHD, BMI, and WC were replicated in the replication data. Moreover, BMI was genetically associated with T2DM.

CONCLUSION

Short education was found to be associated with an increased T2DM risk. BMI might serve as a potential mediator between them.

Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function

Measures of lung function are heritable, and thus, we sought to utilise genetics to propose drug-repurposing candidates that could improve respiratory outcomes. Lung function measures were found to be genetically correlated with seven druggable biochemical traits, with further evidence of a causal relationship between increased fasting glucose and diminished lung function. Moreover, we developed polygenic scores for lung function specifically within pathways with known drug targets and investigated their relationship with pulmonary phenotypes and gene expression in independent cohorts to prioritise individuals who may benefit from particular drug-repurposing opportunities. A transcriptome-wide association study (TWAS) of lung function was then performed which identified several drug–gene interactions with predicted lung function increasing modes of action. Drugs that regulate blood glucose were uncovered through both polygenic scoring and TWAS methodologies. In summary, we provided genetic justification for a number of novel drug-repurposing opportunities that could improve lung function.

Chronic respiratory disorders like asthma affect around 600 million people worldwide. Although these illnesses are widespread, they can have several different underlying causes, making them difficult to treat. Drugs that work well on one type of respiratory disorder may be completely ineffective on another. Understanding the biological and environmental factors that cause these illnesses will allow them to be treated more effectively by tailoring therapies to each patient.

Reduced lung function is a factor in respiratory disorders and it can have many genetic causes. Studying the genes of patients with reduced lung function can reveal the genes involved, some of which may already be targets of existing drugs for other illnesses. So, could a patient’s genetics be used to repurpose existing drugs to treat their respiratory disorders?

Reay et al. combined three methods to link genetics and biological processes to the causes of reduced lung function. The results reveal several factors that could lead to new treatments. In one example, reduced lung function showed a link to genes associated with high blood sugar. As such, treatments used in diabetes might help improve lung function in some patients. Reay et al. also developed a scoring system that could predict the efficacy of a treatment based on a patient’s genetics. The study suggests that COVID-19 infection could be affected by blood sugar levels too.

Chronic respiratory disorders are a critical issue worldwide and have proven difficult to treat, but these results suggest a way to identify new therapies and target them to the right patients. The findings also support a connection between lung function and blood sugar levels. This implies that perhaps existing diabetes treatments – including diet and lifestyle changes aimed at reducing or limiting blood sugar – could be repurposed to treat respiratory disorders in some patients. The next step will be to perform clinical trials to test whether these therapies are in fact effective.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Mendelian pathway analysis of laboratory traits reveals distinct roles for ciliary subcompartments in common disease pathogenesis

Rare monogenic disorders of the primary cilium, termed ciliopathies, are characterized by extreme presentations of otherwise common diseases, such as diabetes, hepatic fibrosis, and kidney failure. However, despite a recent revolution in our understanding of the cilium's role in rare disease pathogenesis, the organelle's contribution to common disease remains largely unknown. Hypothesizing that common genetic variants within Mendelian ciliopathy genes might contribute to common complex diseases pathogenesis, we performed association studies of 16,874 common genetic variants across 122 ciliary genes with 12 quantitative laboratory traits characteristic of ciliopathy syndromes in 452,593 individuals in the UK Biobank. We incorporated tissue-specific gene expression analysis, expression quantitative trait loci, and Mendelian disease phenotype information into our analysis and replicated our findings in meta-analysis. 101 statistically significant associations were identified across 42 of the 122 examined ciliary genes (including eight novel replicating associations). These ciliary genes were widely expressed in tissues relevant to the phenotypes being studied, and eQTL analysis revealed strong evidence for correlation between ciliary gene expression levels and laboratory traits. Perhaps most interestingly, our analysis identified different ciliary subcompartments as being specifically associated with distinct sets of phenotypes. Taken together, our data demonstrate the utility of a Mendelian pathway-based approach to genomic association studies, challenge the widely held belief that the cilium is an organelle important mainly in development and in rare syndromic disease pathogenesis, and provide a framework for the continued integration of common and rare disease genetics to provide insight into the pathophysiology of human diseases of immense public health burden.

A regulatory variant at 3q21.1 confers an increased pleiotropic risk for hyperglycemia and altered bone mineral density

Skeletal and glycemic traits have shared etiology, but the underlying genetic factors remain largely unknown. To identify genetic loci that may have pleiotropic effects, we studied Genome-wide association studies (GWASs) for bone mineral density and glycemic traits and identified a bivariate risk locus at 3q21. Using sequence and epigenetic modeling, we prioritized an adenylate cyclase 5 (ADCY5) intronic causal variant, rs56371916. This SNP changes the binding affinity of SREBP1 and leads to differential ADCY5 gene expression, altering the chromatin landscape from poised to repressed. These alterations result in bone- and type 2 diabetes-relevant cell-autonomous changes in lipid metabolism in osteoblasts and adipocytes. We validated our findings by directly manipulating the regulator SREBP1, the target gene ADCY5, and the variant rs56371916, which together imply a novel link between fatty acid oxidation and osteoblast differentiation. Our work, by systematic functional dissection of pleiotropic GWAS loci, represents a framework to uncover biological mechanisms affecting pleiotropic traits.

Systematic analysis of binding of transcription factors to noncoding variants

Many sequence variants have been linked to complex human traits and diseases1, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human transcription factors to 95,886 noncoding variants in the human genome using an ultra-high-throughput multiplex protein-DNA binding assay, termed single-nucleotide polymorphism evaluation by systematic evolution of ligands by exponential enrichment (SNP-SELEX). The resulting 828 million measurements of transcription factor-DNA interactions enable estimation of the relative affinity of these transcription factors to each variant in vitro and evaluation of the current methods to predict the effects of noncoding variants on transcription factor binding. We show that the position weight matrices of most transcription factors lack sufficient predictive power, whereas the support vector machine combined with the gapped k-mer representation show much improved performance, when assessed on results from independent SNP-SELEX experiments involving a new set of 61,020 sequence variants. We report highly predictive models for 94 human transcription factors and demonstrate their utility in genome-wide association studies and understanding of the molecular pathways involved in diverse human traits and diseases.

Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development

Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk. Generation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets. Genes associated with progenitor-specific stretch enhancers are predicted to regulate developmental processes, most notably tissue morphogenesis. Through gene editing in hESCs, we demonstrate that progenitor-specific enhancers harboring T2D-associated variants regulate cell polarity genes LAMA1 and CRB2. Knockdown of lama1 or crb2 in zebrafish embryos causes a defect in pancreas morphogenesis and impairs islet cell development. Together, our findings reveal that a subset of T2D risk variants specifically affects pancreatic developmental programs, suggesting that dysregulation of developmental processes can predispose to T2D.

Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities

Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin-glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.

Approximate conditional phenotype analysis based on genome wide association summary statistics

Because single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.

The Emerging Physiological Role of AGMO 10 Years after Its Gene Identification

The gene encoding alkylglycerol monooxygenase (AGMO) was assigned 10 years ago. So far, AGMO is the only known enzyme capable of catalysing the breakdown of alkylglycerols and lyso-alkylglycerophospholipids. With the knowledge of the genetic information, it was possible to relate a potential contribution for mutations in the AGMO locus to human diseases by genome-wide association studies. A possible role for AGMO was implicated by genetic analyses in a variety of human pathologies such as type 2 diabetes, neurodevelopmental disorders, cancer, and immune defence. Deficient catabolism of stored lipids carrying an alkyl bond by an absence of AGMO was shown to impact on the overall lipid composition also outside the ether lipid pool. This review focuses on the current evidence of AGMO in human diseases and summarises experimental evidence for its role in immunity, energy homeostasis, and development in humans and several model organisms. With the progress in lipidomics platform and genetic identification of enzymes involved in ether lipid metabolism such as AGMO, it is now possible to study the consequence of gene ablation on the global lipid pool and further on certain signalling cascades in a variety of model organisms in more detail.

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease

Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery. By integrating gene expression, genotype, metabolomic, and clinical data, we identify a glucose and lipid determining (GLD) regulatory network showing inverse relationships with lipid and glucose traits. Master regulators of the GLD network also impact lipid and glucose levels in inverse directions. Experimental inhibition of one of the GLD network master regulators, lanosterol synthase (LSS), in mice confirms the inverse relationships to glucose and lipid levels as predicted by our model and provides mechanistic insights.

Causal Effect of the Triglyceride-Glucose Index and the Joint Exposure of Higher Glucose and Triglyceride With Extensive Cardio-Cerebrovascular Metabolic Outcomes in the UK Biobank: A Mendelian Randomization Study

Background: The causal evidence of the triglyceride-glucose (TyG) index, as well as the joint exposure of higher glucose and triglyceride on the risk of cardio-cerebrovascular diseases (CVD), was lacking. Methods: A comprehensive factorial Mendelian randomization (MR) was performed in the UK Biobank cohort involving 273,368 individuals with European ancestry to assess and quantify these effects. The factorial MR, MR-PRESSO, MR-Egger, meta-regression, sensitivity analysis, positive control, and external verification were utilized. Outcomes include major outcomes [overall CVD, ischemic heart diseases (IHD), and cerebrovascular diseases (CED)] and minor outcomes [angina pectoris (AP), acute myocardial infarction (AMI), chronic IHD (CIHD), heart failure (HF), hemorrhagic stroke (HS), and ischemic stroke (IS)]. Results: The TyG index significantly increased the risk of overall CVD [OR (95% CI): 1.20 (1.14-1.25)], IHD [OR (95% CI): 1.22 (1.15-1.29)], CED [OR (95% CI): 1.14 (1.05-1.23)], AP [OR (95% CI): 1.29 (1.20-1.39)], AMI [OR (95% CI): 1.27 (1.16-1.39)], CIHD [OR (95% CI): 1.21 (1.13-1.29)], and IS [OR (95% CI): 1.22 (1.06-1.40)]. Joint exposure to genetically higher GLU and TG was significantly associated with a higher risk of overall CVD [OR (95% CI): 1.17 (1.12-1.23)] and IHD [OR (95% CI): 1.22 (1.16-1.29)], but not with CED. The effect of GLU and TG was independent of each other genetically and presented dose-response effects in bivariate meta-regression analysis. Conclusions: Lifelong genetic exposure to higher GLU and TG was jointly associated with higher cardiac metabolic risk while the TyG index additionally associated with several cerebrovascular diseases. The TyG index could serve as a more sensitive pre-diagnostic indicator for CVD while the joint GLU and TG could offer a quantitative risk for cardiac metabolic outcomes.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Genetics of Smoking and Risk of Atherosclerotic Cardiovascular Diseases: A Mendelian Randomization Study

IMPORTANCE

Smoking is associated with atherosclerotic cardiovascular disease, but the relative contribution to each subtype (coronary artery disease [CAD], peripheral artery disease [PAD], and large-artery stroke) remains less well understood.

OBJECTIVE

To determine the association between genetic liability to smoking and risk of CAD, PAD, and large-artery stroke.

DESIGN, SETTING, AND PARTICIPANTS

Mendelian randomization study using summary statistics from genome-wide associations of smoking (UK Biobank; up to 462 690 individuals), CAD (Coronary Artery Disease Genome Wide Replication and Meta-analysis plus the Coronary Artery Disease Genetics Consortium; up to 60 801 cases, 123 504 controls), PAD (VA Million Veteran Program; up to 24 009 cases, 150 983 controls), and large-artery stroke (MEGASTROKE; up to 4373 cases, 406 111 controls). This study was conducted using summary statistic data from large, previously described cohorts. Review of those publications does not reveal the total recruitment dates for those cohorts. Data analyses were conducted from August 2019 to June 2020.

EXPOSURES

Genetic liability to smoking (as proxied by genetic variants associated with lifetime smoking index).

MAIN OUTCOMES AND MEASURES

Risk (odds ratios [ORs]) of CAD, PAD, and large-artery stroke.

RESULTS

Genetic liability to smoking was associated with increased risk of PAD (OR, 2.13; 95% CI, 1.78-2.56; P = 3.6 × 10-16), CAD (OR, 1.48; 95% CI, 1.25-1.75; P = 4.4 × 10-6), and stroke (OR, 1.40; 95% CI, 1.02-1.92; P = .04). Genetic liability to smoking was associated with greater risk of PAD than risk of large-artery stroke (ratio of ORs, 1.52; 95% CI, 1.05-2.19; P = .02) or CAD (ratio of ORs, 1.44; 95% CI, 1.12-1.84; P = .004). The association between genetic liability to smoking and atherosclerotic cardiovascular diseases remained independent from the effects of smoking on traditional cardiovascular risk factors.

CONCLUSIONS AND RELEVANCE

In this mendelian randomization analysis of data from large studies of atherosclerotic cardiovascular diseases, genetic liability to smoking was a strong risk factor for CAD, PAD, and stroke, although the estimated association was strongest between smoking and PAD. The association between smoking and atherosclerotic cardiovascular disease was independent of traditional cardiovascular risk factors.

Genetic Correlation and Bidirectional Causal Association Between Type 2 Diabetes and Pulmonary Function

BACKGROUND

Observational studies have shown possible bidirectional association between type 2 diabetes (T2D) and pulmonary function, but the causality is not well defined. The purpose of this study is to investigate genetic correlation and causal relationship of T2D and glycemic traits with pulmonary function.

METHODS

By leveraging summary statistics from large-scale genome-wide association studies, linkage disequilibrium score regression was first implemented to quantify genetic correlations between T2D, glycemic traits, and several spirometry indices. Then both univariable and multivariable Mendelian randomization analyses along with multiple pleiotropy-robust methods were performed in two directions to assess the causal nature of these relationships.

RESULTS

Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) showed significant genetic correlations with T2D and fasting insulin levels and suggestive genetic correlations with fasting glucose and hemoglobin A1c. In Mendelian randomization analyses, genetically predicted higher FEV1 (OR = 0.77; 95% CI = 0.63, 0.94) and FVC (OR = 0.82; 95% CI = 0.68, 0.99) were significantly associated with lower risk of T2D. Conversely, genetic predisposition to higher risk of T2D exhibited strong association with reduced FEV1 (beta = -0.062; 95% CI = -0.100, -0.024) and FEV1 (beta = -0.088; 95% CI = -0.126, -0.050) and increased FEV1/FVC ratio (beta = 0.045; 95% CI = 0.012, 0.078). We also found a suggestive causal effect of fasting glucose on pulmonary function and of pulmonary function on fasting insulin and proinsulin.

CONCLUSIONS

The present study provided supportive evidence for genetic correlation and bidirectional causal association between T2D and pulmonary function. Further studies are warranted to clarify possible mechanisms related to lung dysfunction and T2D, thus offering a new strategy for the management of the two comorbid diseases.

A Polygenic Score for Type 2 Diabetes Risk Is Associated With Both the Acute and Sustained Response to Sulfonylureas

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

A cross-platform approach identifies genetic regulators of human metabolism and health

In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.

Glial and Neuronal Neuroglian, Semaphorin-1a and Plexin A Regulate Morphological and Functional Differentiation of Drosophila Insulin-Producing Cells

The insulin-producing cells (IPCs), a group of 14 neurons in the Drosophila brain, regulate numerous processes, including energy homeostasis, lifespan, stress response, fecundity, and various behaviors, such as foraging and sleep. Despite their importance, little is known about the development and the factors that regulate morphological and functional differentiation of IPCs. In this study, we describe the use of a new transgenic reporter to characterize the role of the Drosophila L1-CAM homolog Neuroglian (Nrg), and the transmembrane Semaphorin-1a (Sema-1a) and its receptor Plexin A (PlexA) in the differentiation of the insulin-producing neurons. Loss of Nrg results in defasciculation and abnormal neurite branching, including ectopic neurites in the IPC neurons. Cell-type specific RNAi knockdown experiments reveal that Nrg, Sema-1a and PlexA are required in IPCs and glia to control normal morphological differentiation of IPCs albeit with a stronger contribution of Nrg and Sema-1a in glia and of PlexA in the IPCs. These observations provide new insights into the development of the IPC neurons and identify a novel role for Sema-1a in glia. In addition, we show that Nrg, Sema-1a and PlexA in glia and IPCs not only regulate morphological but also functional differentiation of the IPCs and that the functional deficits are likely independent of the morphological phenotypes. The requirements of nrg, Sema-1a, and PlexA in IPC development and the expression of their vertebrate counterparts in the hypothalamic-pituitary axis, suggest that these functions may be evolutionarily conserved in the establishment of vertebrate endocrine systems.

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus

BACKGROUND

Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

METHODS

To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

RESULTS

None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

CONCLUSIONS

This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

Association of physical activity and sedentary behavior with type 2 diabetes and glycemic traits: a two-sample Mendelian randomization study

INTRODUCTION

Observational studies suggest that physical activity lowers and sedentary behavior increases the risk of type 2 diabetes. Despite of some supportive trial data for physical activity, it is largely unresolved whether these relations are causal or due to bias.

OBJECTIVE

We investigated the associations between accelerometer-based physical activity and sedentary behavior with type 2 diabetes and several glycemic traits using two-sample Mendelian randomization analysis.

RESEARCH DESIGN AND METHODS

Single nucleotide polymorphisms (SNPs) associated at p<5×10^-8 with accelerometer-based physical activity average accelerations, vigorous physical activity (fraction of accelerations >425 milligravities), and sedentary behavior (metabolic equivalent task ≤1.5) in a genome-wide analysis of the UK Biobank served as instrumental variables.

OUTCOMES

Type 2 diabetes, hemoglobin A1c (HbA1c), fasting glucose, homeostasis model assessment of beta-cell function (HOMA-B), and homeostasis model assessment of insulin resistance (HOMA-IR).

RESULTS

Physical activity and sedentary behavior were unrelated to type 2 diabetes, HbA1c, fasting glucose, HOMA-B, and HOMA-IR. The inverse variance weighted ORs per SD increment for the association between average accelerations and vigorous physical activity with type 2 diabetes were 1.00 (95% CI 0.94 to 1.07, p=0.948) and 0.83 (95% CI 0.56 to 1.23, p=0.357), respectively. These results were confirmed by sensitivity analyses using alternative MR-methods to test the robustness of our findings.

CONCLUSIONS

Based on these results, genetically predicted objectively measured average or vigorous physical activity and sedentary behavior is not associated with type 2 diabetes risk or with glycemic traits in the general population. Further research is required to deepen the understanding of the biological pathways of physical activity.

Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.

Genetic Liability to Cannabis Use Disorder and COVID-19 Hospitalization

Behavioral and life style factors plausibly play a role in likelihood of being hospitalized for COVID-19. Genetic vulnerability to hospitalization after SARS-CoV2 infection may partially relate to comorbid behavioral risk factors, especially the use of combustible psychoactive substances. Paralleling the COVID-19 crisis has been increasingly permissive laws for recreational cannabis use. Cannabis Use Disorder (CUD) is a psychiatric disorder that is heritable and genetically correlated with respiratory disease, independent of tobacco smoking. By leveraging genome-wide association summary statistics of CUD and COVID-19, we find that at least 1/3 rd of the genetic vulnerability to COVID-19 overlaps with genomic liability to CUD (rg=.34, p=0.0003). Genetic causality as a potential mechanism of risk could not be excluded. The association between CUD and COVID-19 remained when accounting for genetics of trying marijuana, tobacco smoking (ever smoking regularly, cigarettes per day, smoking cessation, age of smoking initiation), BMI, fasting glucose, forced expiration volume, education attainment, and Townsend deprivation index. Heavy problematic cannabis use may increase chances of hospitalization due to COVID-19 respiratory complications. Curbing excessive cannabis use may be an essential strategy in COVID-19 mitigation.

Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study

BACKGROUND

Cross-sectional observational studies have reported obesity and cardiometabolic co-morbidities as important predictors of coronavirus disease 2019 (COVID-19) hospitalization. The causal impact of these risk factors is unknown at present.

METHODS

We conducted multivariable logistic regression to evaluate the observational associations between obesity traits (body mass index [BMI], waist circumference [WC]), quantitative cardiometabolic parameters (systolic blood pressure [SBP], serum glucose, serum glycated hemoglobin [HbA1c], low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol and triglycerides [TG]) and SARS-CoV-2 positivity in the UK Biobank cohort. One-sample MR was performed by using the genetic risk scores of obesity and cardiometabolic traits constructed from independent datasets and the genotype and phenotype data from the UK Biobank. Two-sample MR was performed using the summary statistics from COVID-19 host genetics initiative. Cox proportional hazard models were fitted to assess the risk conferred by different genetic quintiles of causative exposure traits.

RESULTS

The study comprised 1,211 European participants who were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 387,079 participants who were either untested or tested negative between 16 March 2020 to 31 May 2020. Observationally, higher BMI, WC, HbA1c and lower HDL-cholesterol were associated with higher odds of COVID-19 infection. One-sample MR analyses found causal associations between higher genetically determined BMI and LDL cholesterol and increased risk of COVID-19 (odds ratio [OR]: 1.15, confidence interval [CI]: 1.05-1.26 and OR: 1.58, CI: 1.21-2.06, per 1 standard deviation increment in BMI and LDL cholesterol respectively). Two-sample MR produced concordant results. Cox models indicated that individuals in the higher genetic risk score quintiles of BMI and LDL were more predisposed to COVID-19 (hazard ratio [HR]: 1.24, CI: 1.03-1.49 and HR: 1.37, CI: 1.14-1.65, for the top vs the bottom quintile for BMI and LDL cholesterol, respectively).

CONCLUSION

We identified causal associations between BMI, LDL cholesterol and susceptibility to COVID-19. In particular, individuals in higher genetic risk categories were predisposed to SARS-CoV-2 infection. These findings support the integration of BMI into the risk assessment of COVID-19 and allude to a potential role of lipid modification in the prevention and treatment.

Genetic Studies of Gestational Diabetes and Glucose Metabolism in Pregnancy

PURPOSE OF REVIEW

In this review, we summarize studies investigating genetics of gestational diabetes mellitus (GDM) and glucose metabolism in pregnancy. We describe these studies in the context of the larger body of literature on type 2 diabetes (T2D) and glycemic trait genomics.

RECENT FINDINGS

We reviewed 23 genetic association studies for GDM and performed a meta-analysis, which revealed variants at eight T2D loci significantly associated with GDM after the Bonferroni correction. These studies suggest that GDM and T2D share a number of genetic risk loci. Only two unbiased genome-wide association studies (GWASs) have successfully revealed genetic associations for GDM and related glycemic traits in pregnancy. A GWAS for GDM in Korean women identified two loci (near CDKAL1 and MTNR1B) known to be associated with T2D, though the association of the MTNR1B locus with GDM appears to be stronger than that for T2D. A multi-ethnic GWAS for glycemic traits in pregnancy identified two novel loci (near HKDC1 and BACE2) which appear to be associated with post-load glucose and fasting c-peptide specifically in pregnant women. There are ongoing efforts to use this genetic information, in the form of polygenic scores, to predict risk of GDM and postpartum T2D. The body of literature examining genetic associations with GDM is limited, especially when compared to the available literature on T2D and glycemic trait genomics. Additional genetic discovery for glucose metabolism in pregnant women will require larger pregnancy cohorts and international collaborative efforts. Studies on the clinical implications of these findings are also warranted.

Analysis of Glucocorticoid-Related Genes Reveal CCHCR1 as a New Candidate Gene for Type 2 Diabetes

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10-14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

Meta-analysis investigating the role of interleukin-6 mediated inflammation in type 2 diabetes

BACKGROUND

Evidence from animal models and observational epidemiology points to a role for chronic inflammation, in which interleukin 6 (IL-6) is a key player, in the pathophysiology of type 2 diabetes (T2D). However, it is unknown whether IL-6 mediated inflammation is implicated in the pathophysiology of T2D.

METHODS

We performed a meta-analysis of 15 prospective studies to investigate associations between IL-6 levels and incident T2D including 5,421 cases and 31,562 non-cases. We also estimated the association of a loss-of-function missense variant (Asp358Ala) in the IL-6 receptor gene (IL6R), previously shown to mimic the effects of IL-6R inhibition, in a large trans-ethnic meta-analysis of six T2D case-control studies including 260,614 cases and 1,350,640 controls.

FINDINGS

In a meta-analysis of 15 prospective studies, higher levels of IL-6 (per log pg/mL) were significantly associated with a higher risk of incident T2D (1·24 95% CI, 1·17, 1·32; P = 1 × 10-12). In a trans-ethnic meta-analysis of 260,614 cases and 1,350,640 controls, the IL6R Asp358Ala missense variant was associated with lower odds of T2D (OR, 0·98; 95% CI, 0·97, 0·99; P = 2 × 10-7). This association was not due to diagnostic misclassification and was consistent across ethnic groups. IL-6 levels mediated up to 5% of the association between higher body mass index and T2D.

INTERPRETATION

Large-scale human prospective and genetic data provide evidence that IL-6 mediated inflammation is implicated in the etiology of T2D but suggest that the impact of this pathway on disease risk in the general population is likely to be small.

FUNDING

The EPICNorfolk study has received funding from the Medical Research Council (MRC) (MR/N003284/1, MC-UU_12015/1 and MC_PC_13048) and Cancer Research UK (C864/A14136). The Fenland Study is funded by the MRC (MC_UU_12015/1 and MC_PC_13046).

The Protein Phosphatase 1 Complex Is a Direct Target of AKT that Links Insulin Signaling to Hepatic Glycogen Deposition

Insulin-stimulated hepatic glycogen synthesis is central to glucose homeostasis. Here, we show that PPP1R3G, a regulatory subunit of protein phosphatase 1 (PP1), is directly phosphorylated by AKT. PPP1R3G phosphorylation fluctuates with fasting-refeeding cycle and is required for insulin-stimulated dephosphorylation, i.e., activation of glycogen synthase (GS) in hepatocytes. In this study, we demonstrate that knockdown of PPP1R3G significantly inhibits insulin response. The introduction of wild-type PPP1R3G, and not phosphorylation-defective mutants, increases hepatic glycogen deposition, blood glucose clearance, and insulin sensitivity in vivo. Mechanistically, phosphorylated PPP1R3G displays increased binding for, and promotes dephosphorylation of, phospho-GS. Furthermore, PPP1R3B, another regulatory subunit of PP1, binds to the dephosphorylated GS, thereby relaying insulin stimulation to hepatic glycogen deposition. Importantly, this PP1-mediated signaling cascade is independent of GSK3. Therefore, we reveal a regulatory axis consisting of insulin/AKT/PPP1R3G/PPP1R3B that operates in parallel to the GSK3-dependent pathway, controlling glycogen synthesis and glucose homeostasis in insulin signaling.

Genetic variation in the body mass index of adult survivors of childhood acute lymphoblastic leukemia: A report from the Childhood Cancer Survivor Study and the St. Jude Lifetime Cohort

BACKGROUND

Treatment characteristics such as cranial radiation therapy (CRT) do not fully explain adiposity risk in childhood acute lymphoblastic leukemia (ALL) survivors. This study was aimed at characterizing genetic variation related to adult body mass index (BMI) among survivors of childhood ALL.

METHODS

Genetic associations of BMI among 1458 adult survivors of childhood ALL (median time from diagnosis, 20 years) were analyzed by multiple approaches. A 2-stage genome-wide association study in the Childhood Cancer Survivor Study (CCSS) and the St. Jude Lifetime Cohort Study (SJLIFE) was performed. BMI was a highly polygenic trait in the general population. Within the known loci, the BMI percent variance explained was estimated, and additive interactions (chi-square test) with CRT in the CCSS were evaluated. The role of DNA methylation in CRT interaction was further evaluated in a subsample of ALL survivors.

RESULTS

In a meta-analysis of the CCSS and SJLIFE, 2 novel loci associated with adult BMI among survivors of childhood ALL (LINC00856 rs575792008 and EMR1 rs62123082; P_Meta < 5E-8) were identified. It was estimated that the more than 700 known loci explained 6.2% of the variation in adult BMI in childhood ALL survivors. Within the known loci, significant main effects for 23 loci and statistical interactions with CRT at 9 loci (P < 7.0E-5) were further identified. At 2 CRT-interacting loci, DNA methylation patterns may have differed by age.

CONCLUSIONS

Adult survivors of childhood ALL have genetic heritability for BMI similar to that observed in the general population. This study provides evidence that treatment with CRT can modify the effect of genetic variants on adult BMI in childhood ALL survivors.

LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

MOTIVATION

Much effort has been made toward understanding the genetic architecture of complex traits and diseases. In the past decade, fruitful GWAS findings have highlighted the important role of regulatory variants and pervasive pleiotropy. Because of the accumulation of GWAS data on a wide range of phenotypes and high-quality functional annotations in different cell types, it is timely to develop a statistical framework to explore the genetic architecture of human complex traits by integrating rich data resources.

RESULTS

In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM's performance and compared it with related methods. Then, we applied it to analyze 44 GWASs with 9 genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits.

AVAILABILITY AND IMPLEMENTATION

The LPM package, all simulation codes and real datasets in this study are available at https://github.com/mingjingsi/LPM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Influence of multiple apolipoprotein A-I and B genetic variations on insulin resistance and metabolic syndrome in obstructive sleep apnea

Background

The relationships between apolipoprotein A-I (APOA-I), apolipoprotein B (APOB) with insulin resistance, metabolic syndrome (MetS) are unclear in OSA. We aimed to evaluate whether the multiple single nucleotide polymorphism (SNP) variants of APOA-I and APOB exert a collaborative effect on insulin resistance and MetS in OSA.

Methods

Initially, 12 APOA-I SNPs and 30 APOB SNPs in 5259 subjects were examined. After strict screening, four APOA-I SNPs and five APOB SNPs in 4007 participants were included. For each participant, the genetic risk score (GRS) was calculated based on the cumulative effect of multiple genetic variants of APOA-I and APOB. Logistic regression analyses were used to evaluate the relationships between APOA-I/APOB genetic polymorphisms, insulin resistance, and MetS in OSA.

Results

Serum APOB levels increased the risk of insulin resistance and MetS adjusting for age, gender and BMI [odds ratio (OR = 3.168, P < 0.001; OR = 6.098, P < 0.001, respectively]. APOA-I GRS decreased the risk of insulin resistance and MetS after adjustments (OR = 0.917, P = 0.001; OR = 0.870, P < 0.001, respectively). APOB GRS had no association with insulin resistance (OR = 1.364, P = 0.610), and had weak association with MetS after adjustments (OR = 1.072, P = 0.042). In addition, individuals in the top quintile of the APOA-I genetic score distribution had a lower risk of insulin resistance and MetS after adjustments (OR = 0.761, P = 0.007; OR = 0.637, P < 0.001, respectively).

Conclusions

In patients with OSA, cumulative effects of APOA-I genetic variations decreased the risk of insulin resistance and MetS, whereas multiple APOB genetic variations had no associations with insulin resistance and weak association with MetS.

Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS

In spite of the tremendous success of genome-wide association studies (GWAS) in identifying genetic variants associated with complex traits and common diseases, many more are yet to be discovered. Hence, it is always desirable to improve the statistical power of GWAS. Paralleling with the intensive efforts of integrating GWAS with functional annotations or other omic data, we propose leveraging other published GWAS summary data to boost statistical power for a new/focus GWAS; the traits of the published GWAS may or may not be genetically correlated with the target trait of the new GWAS. Building on weighted hypothesis testing with a solid theoretical foundation, we develop a novel and effective method to construct single-nucleotide polymorphism (SNP)-specific weights based on 22 published GWAS data sets with various traits, detecting sometimes dramatically increased numbers of significant SNPs and independent loci as compared to the standard/unweighted analysis. For example, by integrating a schizophrenia GWAS summary data set with 19 other GWAS summary data sets of nonschizophrenia traits, our new method identified 1,585 genome-wide significant SNPs mapping to 15 linkage disequilibrium-independent loci, largely exceeding 818 significant SNPs in 13 independent loci identified by the standard/unweighted analysis; furthermore, using a later and larger schizophrenia GWAS summary data set as the validation data, 1,423 (out of 1,585) significant SNPs identified by the weighted analysis, compared to 705 (out of 818) by the unweighted analysis, were confirmed, while all 15 and 13 independent loci were also confirmed. Similar conclusions were reached with lipids and Alzheimer's disease (AD) traits. We conclude that the proposed approach is simple and cost-effective to improve GWAS power.

Four pleiotropic loci associated with fat mass and lean mass

BACKGROUND

Fat mass and lean mass are two biggest components of body mass. Both fat mass and lean mass are under strong genetic determinants and are correlated.

METHODS

We performed a bivariate genome-wide association meta-analysis of (lean adjusted) leg fat mass and (fat adjusted) leg lean mass in 12,517 subjects from 6 samples, and followed by in silico replication in large-scale UK biobank cohort sample (N = 370 097).

RESULTS

We identified four loci that were significant at the genome-wide significance (GWS, α = 5.0 × 10-8) level at the discovery meta-analysis, and successfully replicated in the replication sample: 2q36.3 (rs1024137, pdiscovery = 3.32 × 10-8, preplication = 4.07 × 10-13), 5q13.1 (rs4976033, pdiscovery = 1.93 × 10-9, preplication = 6.35 × 10-7), 12q24.31 (rs4765528, pdiscovery = 7.19 × 10-12, preplication = 1.88 × 10-11) and 18q21.32 (rs371326986, pdiscovery = 9.04 × 10-9, preplication = 2.35 × 10-95). The above four pleiotropic loci may play a pleiotropic role for fat mass and lean mass development.

CONCLUSIONS

Our findings further enhance the understanding of the genetic association between fat mass and lean mass and provide a new theoretical basis for their understanding.

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

Acetylator Genotype-Dependent Dyslipidemia in Rats Congenic for N-Acetyltransferase 2

Recent reports suggest that arylamine N-acetyltransferases (NAT1 and/or NAT2) serve important roles in regulation of energy utility and insulin sensitivity. We investigated the interaction between diet (control vs. high-fat diet) and acetylator phenotype (rapid vs. slow) using previously established congenic rat lines (in F344 background) that exhibit rapid or slow Nat2 (orthologous to human NAT1) acetylator genotypes. Male and female rats of each genotype were fed control or high-fat (Western-style) diet for 26 weeks. We then examined diet- and acetylator genotype-dependent changes in body and liver weights, systemic glucose tolerance, insulin sensitivity, and plasma lipid profile. Male and female rats on the high fat diet weighed approximately 10% more than rats on the control diet and the percentage liver to body weight was consistently higher in rapid than slow acetylator rats. Rapid acetylator rats were more prone to develop dyslipidemia overall (i.e., higher triglyceride; higher LDL; and lower HDL), compared to slow acetylator rats. Total cholesterol (TC)-to-HDL ratios were significantly higher and HDL-to-LDL ratios were significantly lower in rapid acetylator rats. Our data suggest that rats with rapid systemic Nat2 (NAT1 in humans) genotype exhibited higher dyslipidemia conferring risk for metabolic syndrome and cardiovascular dysfunction.

Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes

Metformin is the first-line pharmacotherapy for managing type 2 diabetes (T2D). However, many patients with T2D do not respond to or tolerate metformin well. Currently, there are no phenotypes that successfully predict glycemic response to, or tolerance of, metformin. We explored whether blood-based epigenetic markers could discriminate metformin response and tolerance by analyzing genome-wide DNA methylation in drug-naïve patients with T2D at the time of their diagnosis. DNA methylation of 11 and 4 sites differed between glycemic responders/nonresponders and metformin-tolerant/intolerant patients, respectively, in discovery and replication cohorts. Greater methylation at these sites associated with a higher risk of not responding to or not tolerating metformin with odds ratios between 1.43 and 3.09 per 1-SD methylation increase. Methylation risk scores (MRSs) of the 11 identified sites differed between glycemic responders and nonresponders with areas under the curve (AUCs) of 0.80 to 0.98. MRSs of the 4 sites associated with future metformin intolerance generated AUCs of 0.85 to 0.93. Some of these blood-based methylation markers mirrored the epigenetic pattern in adipose tissue, a key tissue in diabetes pathogenesis, and genes to which these markers were annotated to had biological functions in hepatocytes that altered metformin-related phenotypes. Overall, we could discriminate between glycemic responders/nonresponders and participants tolerant/intolerant to metformin at diagnosis by measuring blood-based epigenetic markers in drug-naïve patients with T2D. This epigenetics-based tool may be further developed to help patients with T2D receive optimal therapy.

Risk of pre-eclampsia in patients with a maternal genetic predisposition to common medical conditions: a case-control study

OBJECTIVE

To assess whether women with a genetic predisposition to medical conditions known to increase pre-eclampsia risk have an increased risk of pre-eclampsia in pregnancy.

DESIGN

Case-control study.

SETTING AND POPULATION

Pre-eclampsia cases (n = 498) and controls (n = 1864) in women of European ancestry from five US sites genotyped on a cardiovascular gene-centric array.

METHODS

Significant single-nucleotide polymorphisms (SNPs) from 21 traits in seven disease categories (cardiovascular, inflammatory/autoimmune, insulin resistance, liver, obesity, renal and thrombophilia) with published genome-wide association studies (GWAS) were used to create a genetic instrument for each trait. Multivariable logistic regression was used to test the association of each continuous scaled genetic instrument with pre-eclampsia. Odds of pre-eclampsia were compared across quartiles of the genetic instrument and evaluated for significance.

MAIN OUTCOME MEASURES

Genetic predisposition to medical conditions and relationship with pre-eclampsia.

RESULTS

An increasing burden of risk alleles for elevated diastolic blood pressure (DBP) and increased body mass index (BMI) were associated with an increased risk of pre-eclampsia (DBP, overall OR 1.11, 95% CI 1.01-1.21, P = 0.025; BMI, OR 1.10, 95% CI 1.00-1.20, P = 0.042), whereas alleles associated with elevated alkaline phosphatase (ALP) were protective (OR 0.89, 95% CI 0.82-0.97, P = 0.008), driven primarily by pleiotropic effects of variants in the FADS gene region. The effect of DBP genetic loci was even greater in early-onset pre-eclampsia cases (at <34 weeks of gestation, OR 1.30, 95% CI 1.08-1.56, P = 0.005). For other traits, there was no evidence of an association.

CONCLUSIONS

These results suggest that the underlying genetic architecture of pre-eclampsia may be shared with other disorders, specifically hypertension and obesity.

TWEETABLE ABSTRACT

A genetic predisposition to increased diastolic blood pressure and obesity increases the risk of pre-eclampsia.

Causal relationships between NAFLD, T2D and obesity have implications for disease subphenotyping

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D) and obesity are epidemiologically correlated with each other but the causal inter-relationships between them remain incompletely understood. We aimed to explore the causal relationships between the 3 diseases.

METHODS

Using both UK Biobank and publicly available genome-wide association study data, we performed a 2-sample bidirectional Mendelian randomization analysis to test the causal inter-relationships between NAFLD, T2D, and obesity. Transgenic mice expressing the human PNPLA3-I148M isoforms (TghPNPLA3-I148M) were used as an example to validate causal effects and explore underlying mechanisms.

RESULTS

Genetically driven NAFLD significantly increased the risk of T2D and central obesity but not insulin resistance or generalized obesity, while genetically driven T2D, body mass index and WHRadjBMI causally increased NAFLD risk. The animal study focusing on PNPLA3 corroborated these causal effects: compared to the TghPNPLA3-I148I controls, the TghPNPLA3-I148M mice developed glucose intolerance and increased visceral fat, but maintained normal insulin sensitivity, reduced body weight, and decreased circulating total cholesterol. Mechanistically, the TghPNPLA3-I148M mice demonstrated decreased pancreatic insulin but increased glucagon secretion, which was associated with increased pancreatic inflammation. In addition, transcription of hepatic cholesterol biosynthesis pathway genes was significantly suppressed, while transcription of thermogenic pathway genes was activated in subcutaneous and brown adipose tissues but not in visceral fat in TghPNPLA3-I148M mice.

CONCLUSIONS

Our study suggests that lifelong, genetically driven NAFLD causally promotes T2D with a late-onset type 1-like diabetic subphenotype and central obesity; while genetically driven T2D, obesity, and central obesity all causally increase the risk of NAFLD. This causal relationship revealed new insights into how nature and nurture drive these diseases, providing novel hypotheses for disease subphenotyping.

LAY SUMMARY

Non-alcoholic fatty liver disease, type 2 diabetes and obesity are epidemiologically correlated with each other, but their causal relationships were incompletely understood. Herein, we identified causal relationships between these conditions, which suggest that each of these closely related diseases should be further stratified into subtypes. This is important for accurate diagnosis, prevention and treatment of these diseases.

Effect of Glucagon on Ischemic Heart Disease and Its Risk Factors: A Mendelian Randomization Study

CONTEXT

Glucagon acts reciprocally with insulin to regular blood glucose. However, the effect of glucagon on cardiovascular disease has not been widely studied. It has been suggested that insulin may increase the risk of ischemic heart disease.

OBJECTIVE

To investigate whether glucagon, the main counteracting hormone of insulin, plays a role in development of ischemic heart disease.

DESIGN, SETTING, AND PARTICIPANTS

In this 2-sample Mendelian randomization study, we estimated the causal effect of glucagon on ischemic heart disease and its risk factors using the inverse-variance weighted method with multiplicative random effects and multiple sensitivity analyses. Genetic associations with glucagon and ischemic heart disease and its risk factors, including type 2 diabetes and fasting insulin, were obtained from publicly available genome-wide association studies.

MAIN OUTCOME MEASURE

Odds ratio for ischemic heart disease and its risk factors per 1 standard deviation change in genetically predicted glucagon.

RESULTS

Twenty-four single-nucleotide polymorphisms strongly (P < 5 × 10-6) and independently (r2 < 0.05) predicting glucagon were obtained. Genetically predicted higher glucagon was associated with an increased risk of ischemic heart disease (inverse-variance weighted odds ratio, 1.03; 95% confidence interval, 1.0003-1.05) but not with type 2 diabetes (inverse-variance weighted odds ratio, 0.998, 95% confidence interval, 0.97-1.03), log-transformed fasting insulin (inverse-variance weighted beta, 0.002, 95% confidence interval, -0.01 to 0.01), other glycemic traits, blood pressure, reticulocyte, or lipids.

CONCLUSION

Glucagon might have an adverse impact on ischemic heart disease. Relevance of the underlying pathway to existing and potential interventions should be investigated.

Dissecting maternal and fetal genetic effects underlying the associations between maternal phenotypes, birth outcomes, and adult phenotypes: A mendelian-randomization and haplotype-based genetic score analysis in 10,734 mother-infant pairs

BACKGROUND

Many maternal traits are associated with a neonate's gestational duration, birth weight, and birth length. These birth outcomes are subsequently associated with late-onset health conditions. The causal mechanisms and the relative contributions of maternal and fetal genetic effects behind these observed associations are unresolved.

METHODS AND FINDINGS

Based on 10,734 mother-infant duos of European ancestry from the UK, Northern Europe, Australia, and North America, we constructed haplotype genetic scores using single-nucleotide polymorphisms (SNPs) known to be associated with adult height, body mass index (BMI), blood pressure (BP), fasting plasma glucose (FPG), and type 2 diabetes (T2D). Using these scores as genetic instruments, we estimated the maternal and fetal genetic effects underlying the observed associations between maternal phenotypes and pregnancy outcomes. We also used infant-specific birth weight genetic scores as instrument and examined the effects of fetal growth on pregnancy outcomes, maternal BP, and glucose levels during pregnancy. The maternal nontransmitted haplotype score for height was significantly associated with gestational duration (p = 2.2 × 10-4). Both maternal and paternal transmitted height haplotype scores were highly significantly associated with birth weight and length (p < 1 × 10-17). The maternal transmitted BMI scores were associated with birth weight with a significant maternal effect (p = 1.6 × 10-4). Both maternal and paternal transmitted BP scores were negatively associated with birth weight with a significant fetal effect (p = 9.4 × 10-3), whereas BP alleles were significantly associated with gestational duration and preterm birth through maternal effects (p = 3.3 × 10-2 and p = 4.5 × 10-3, respectively). The nontransmitted haplotype score for FPG was strongly associated with birth weight (p = 4.7 × 10-6); however, the glucose-increasing alleles in the fetus were associated with reduced birth weight through a fetal effect (p = 2.2 × 10-3). The haplotype scores for T2D were associated with birth weight in a similar way but with a weaker maternal effect (p = 6.4 × 10-3) and a stronger fetal effect (p = 1.3 × 10-5). The paternal transmitted birth weight score was significantly associated with reduced gestational duration (p = 1.8 × 10-4) and increased maternal systolic BP during pregnancy (p = 2.2 × 10-2). The major limitations of the study include missing and heterogenous phenotype data in some data sets and different instrumental strength of genetic scores for different phenotypic traits.

CONCLUSIONS

We found that both maternal height and fetal growth are important factors in shaping the duration of gestation: genetically elevated maternal height is associated with longer gestational duration, whereas alleles that increase fetal growth are associated with shorter gestational duration. Fetal growth is influenced by both maternal and fetal effects and can reciprocally influence maternal phenotypes: taller maternal stature, higher maternal BMI, and higher maternal blood glucose are associated with larger birth size through maternal effects; in the fetus, the height- and metabolic-risk-increasing alleles are associated with increased and decreased birth size, respectively; alleles raising birth weight in the fetus are associated with shorter gestational duration and higher maternal BP. These maternal and fetal genetic effects may explain the observed associations between the studied maternal phenotypes and birth outcomes, as well as the life-course associations between these birth outcomes and adult phenotypes.

Effects of a Lifestyle-Modification Program on Blood-Glucose Regulation and Health Promotion in Diabetic Patients: A Randomized Controlled Trial

Background

We aimed to investigate the efficacy of the lifestyle intervention (LSI) program in controlling blood glucose regulation and health promotion in type 2 diabetic (T2D) patients.

Methods

Thirty adults with a diagnosed with diabetes were randomly assigned to LSI and control groups. The LSI group maintained their daily routines after participating twice in the LSI program, while control group maintained 4 weeks of daily life without participating in an intervention.

Results

HbA1c levels in the LSI group decreased significantly after participation (p = 0.025) compared with levels before the study, but there was no significant difference between the groups. The weight and body mass index (BMI) of the LSI group tended to decrease significantly compared with the control group (p = 0.054 and p = 0.055, respectively), and the waist circumference (WC) of the LSI group decreased significantly compared with that of the control group (p = 0.048). In the effects of the LSI program according to the polymorphism of GCKR genes, changes in glycated albumin (GA) (%), HbA1c, WC, BMI, and weight showed a significant decrease in the non-risk (TT genotype) GCKR group compared with the risk group (CC and TC genotype).

Conclusion

Application of the four-week LSI program to diabetics revealed positive effects on blood-glucose control and improvement in obesity indicators. In particular, the risk group with variations in the GCKR gene was associated with more genetic effects on indicators such as blood glucose and obesity than was the non-risk group.