Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Phenome-wide association study on miRNA-related sequence variants: the UK Biobank

BACKGROUND

Genetic variants in the coding region could directly affect the structure and expression levels of genes and proteins. However, the importance of variants in the non-coding region, such as microRNAs (miRNAs), remain to be elucidated. Genetic variants in miRNA-related sequences could affect their biogenesis or functionality and ultimately affect disease risk. Yet, their implications and pleiotropic effects on many clinical conditions remain unknown.

METHODS

Here, we utilised genotyping and hospital records data in the UK Biobank (N = 423,419) to investigate associations between 346 genetic variants in miRNA-related sequences and a wide range of clinical diagnoses through phenome-wide association studies. Further, we tested whether changes in blood miRNA expression levels could affect disease risk through colocalisation and Mendelian randomisation analysis.

RESULTS

We identified 122 associations for six variants in the seed region of miRNAs, nine variants in the mature region of miRNAs, and 27 variants in the precursor miRNAs. These included associations with hypertension, dyslipidaemia, immune-related disorders, and others. Nineteen miRNAs were associated with multiple diagnoses, with six of them associated with multiple disease categories. The strongest association was reported between rs4285314 in the precursor of miR-3135b and celiac disease risk (odds ratio (OR) per effect allele increase = 0.37, P = 1.8 × 10-162). Colocalisation and Mendelian randomisation analysis highlighted potential causal role of miR-6891-3p in dyslipidaemia.

CONCLUSIONS

Our study demonstrates the pleiotropic effect of miRNAs and offers insights to their possible clinical importance.

Role of leisure sedentary behavior on type 2 diabetes and glycemic homeostasis: a Mendelian randomization analysis

Purpose

Utilize Mendelian randomization (MR) to examine the impact of leisure sedentary behavior (LSB) on the prevalence of type 2 diabetes mellitus (T2D) and glycemic homeostasis impairment, as well as to identify potential mediating pathways involved in these associations.

Methods

We chose genetic variants linked to LSB from a large genome-wide association study (GWAS) to use as instrumental variables (IVs). Then, we used a two-sample MR study to investigate the link between LSB and T2D and glycemic homeostasis. Multivariate MR (MVMR) and mediation analysis were also used to look at possible mediating paths.

Results

MR analysis showed a genetical link between leisure TV watching and T2D (OR 1.64, 95% CI 1.39-1.93, P< 0.001) and impaired Glycemic Homeostasis, while leisure computer use seemed to protect against T2D prevalence (OR 0.65, 95% CI 0.50-0.84, P< 0.001). It was found that leisure TV watching increases the risk of T2D through higher BMI (mediation effect 0.23, 95% CI 0.11-0.35, P< 0.001), higher triglycerides (mediation effect 0.07, 95% CI 0.04-0.11, P< 0.001), and less education (mediation effect 0.16, 95% CI 0.08-0.24, P< 0.001). Sensitivity and heterogeneity analyses further substantiated the robustness of these findings. Reverse MR analysis did not yield significant results.

Conclusion

This study shows LSB is linked to a higher rate of T2D and impaired glycemic homeostasis through obesity, lipid metabolism disorders, and reduced educational attainment.

Dissecting causal associations of type 2 diabetes with 111 types of ocular conditions: a Mendelian randomization study

Background

Despite the well-established findings of a higher incidence of retina-related eye diseases in patients with diabetes, there is less investigation into the causal relationship between diabetes and non-retinal eye conditions, such as age-related cataracts and glaucoma.

Methods

We performed Mendelian randomization (MR) analysis to examine the causal relationship between type 2 diabetes mellitus (T2DM) and 111 ocular diseases. We employed a set of 184 single nucleotide polymorphisms (SNPs) that reached genome-wide significance as instrumental variables (IVs). The primary analysis utilized the inverse variance-weighted (IVW) method, with MR-Egger and weighted median (WM) methods serving as supplementary analyses.

Results

The results revealed suggestive positive causal relationships between T2DM and various ocular conditions, including "Senile cataract" (OR= 1.07; 95% CI: 1.03, 1.11; P=7.77×10-4), "Glaucoma" (OR= 1.08; 95% CI: 1.02, 1.13; P=4.81×10-3), and "Disorders of optic nerve and visual pathways" (OR= 1.10; 95% CI: 0.99, 1.23; P=7.01×10-2).

Conclusion

Our evidence supports a causal relationship between T2DM and specific ocular disorders. This provides a basis for further research on the importance of T2DM management and prevention strategies in maintaining ocular health.

A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions

Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations' variations, supported by genotypes' principal component analysis. Results show that the Egyptian's reference metabolic genes were clustered together with the Europeans', Ad-Mixed Americans', and South-Asians'. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations.

Causal influence of muscle weakness on cardiometabolic diseases and osteoporosis

The causal roles of muscle weakness in cardiometabolic diseases and osteoporosis remain elusive. This two-sample Mendelian randomization (MR) study aims to explore the causal roles of muscle weakness in the risk of cardiometabolic diseases and osteoporosis. 15 single nucleotide polymorphisms (SNPs, P < 5 × 10-8) associated with muscle weakness were used as instrumental variables. Genetic predisposition to muscle weakness led to increased risk of coronary artery disease (inverse variance weighted [IVW] analysis, beta-estimate: 0.095, 95% confidence interval [CI]: 0.023 to 0.166, standard error [SE]:0.036, P-value = 0.009) and reduced risk of heart failure (weight median analysis, beta-estimate: - 0.137, 95% CI - 0.264 to - 0.009, SE:0.065, P-value = 0.036). In addition, muscle weakness may reduce the estimated bone mineral density (eBMD, weight median analysis, beta-estimate: - 0.059, 95% CI - 0.110 to - 0.008, SE:0.026, P-value = 0.023). We found no MR associations between muscle weakness and atrial fibrillation, type 2 diabetes or fracture. This study provides robust evidence that muscle weakness is causally associated with the incidence of coronary artery disease and heart failure, which may provide new insight to prevent and treat these two cardiometabolic diseases.

Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases

Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.

The influence of insulin-related genetic variants on fetal growth, fetal blood flow, and placental weight in a prospective pregnancy cohort

The fetal insulin hypothesis proposes that low birthweight and type 2 diabetes (T2D) in adulthood may be two phenotypes of the same genotype. In this study we aimed to explore this theory further by testing the effects of GWAS-identified genetic variants related to insulin release and sensitivity on fetal growth and blood flow from week 20 of gestation to birth and on placental weight at birth. We calculated genetic risk scores (GRS) of first phase insulin release (FPIR), fasting insulin (FI), combined insulin resistance and dyslipidaemia (IR + DLD) and insulin sensitivity (IS) in a study population of 665 genotyped newborns. Two-dimensional ultrasound measurements with estimation of fetal weight and blood flow were carried out at week 20, 25, and 32 of gestation in all 665 pregnancies. Birthweight and placental weight were registered at birth. Associations between the GRSs and fetal growth, blood flow and placental weight were investigated using linear mixed models. The FPIR GRS was directly associated with fetal growth from week 20 to birth, and both the FI GRS, IR + DLD GRS, and IS GRS were associated with placental weight at birth. Our findings indicate that insulin-related genetic variants might primarily affect fetal growth via the placenta.

Unraveling genetic causality between type 2 diabetes and pulmonary tuberculosis on the basis of Mendelian randomization

BACKGROUND

The comorbidity rate between type 2 diabetes mellitus (T2DM) and pulmonary tuberculosis (PTB) is high and imposes enormous strains on healthcare systems. However, whether T2DM is causally associated with PTB is unknown owing to limited evidence from prospective studies. Consequently, the present study aimed to clarify the genetic causality between T2DM and PTB on the basis of Mendelian randomization (MR) analysis.

METHODS

Genetic variants for T2DM and PTB were obtained from the IEU OpenGWAS project. The inverse variance weighted method was used as the main statistical analysis method and was supplemented with MR-Egger, weighted median, simple mode, and weighted mode methods. Heterogeneity was analyzed using Cochran's Q statistic. Horizontal pleiotropy was assessed using the MR-PRESSO global test and MR-Egger regression. Robustness of the results was verified using the leave-one-out method.

RESULTS

A total of 152 independent single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) to assess the genetic causality between T2DM and PTB. Patients with T2DM had a higher risk of PTB at the genetic level (odds ratio (OR) for MR-Egger was 1.550, OR for weighted median was 1.540, OR for inverse variance weighted was 1.191, OR for simple mode was 1.629, OR for weighted mode was 1.529). There was no horizontal pleiotropy or heterogeneity among IVs. The results were stable when removing the SNPs one by one.

CONCLUSIONS

This is the first comprehensive MR analysis that revealed the genetic causality between T2DM and PTB in the East Asian population. The study provides convincing evidence that individuals with T2DM have a higher risk of developing PTB at the genetic level. This offers a significant basis for joint management of concurrent T2DM and PTB in clinical practice.

Roles of gut microbiota in atrial fibrillation: insights from Mendelian randomization analysis and genetic data from over 430,000 cohort study participants

BACKGROUND

Gut microbiota imbalances have been suggested as a contributing factor to atrial fibrillation (AF), but the causal relationship is not fully understood.

OBJECTIVES

To explore the causal relationships between the gut microbiota and AF using Mendelian randomization (MR) analysis.

METHODS

Summary statistics were from genome-wide association studies (GWAS) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) (the Dutch Microbiome Project) and two large meta-GWASs of AF. The significant results were validated in FinnGen cohort and over 430,000 UK Biobank participants. Mediation MR analyses were conducted for AF risk factors, including type 2 diabetes, coronary artery disease (CAD), body mass index (BMI), blood lipids, blood pressure, and obstructive sleep apnea, to explore the potential mediation effect of these risk factors in between the gut microbiota and AF.

RESULTS

Two microbial taxa causally associated with AF: species Eubacterium ramulus (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.04-1.12, P = 0.0001, false discovery rate (FDR) adjusted p-value = 0.023) and genus Holdemania (OR 1.15, 95% CI 1.07-1.25, P = 0.0004, FDR adjusted p-value = 0.042). Genus Holdemania was associated with incident AF risk in the UK Biobank. The proportion of mediation effect of species Eubacterium ramulus via CAD was 8.05% (95% CI 1.73% - 14.95%, P = 0.008), while the proportion of genus Holdemania on AF via BMI was 12.01% (95% CI 5.17% - 19.39%, P = 0.0005).

CONCLUSIONS

This study provided genetic evidence to support a potential causal mechanism between gut microbiota and AF and suggested the mediation role of AF risk factors.

The Role of PPARγ Gene Polymorphisms, Gut Microbiota in Type 2 Diabetes: Current Progress and Future Prospects

Over the past decade, there has been a significant increase in studies investigating the relationship between the polymorphisms of the Peroxisome Proliferator-Activated Receptor gamma (PPARγ) gene and Type 2 Diabetes (T2D). PPARγ, a critical transcription factor, plays a central role in lipid metabolism, insulin resistance, and inflammatory response. Concurrently, the influence of gut microbiota on the development of T2D has gained increasing attention, especially their role in affecting host metabolism, such as lipid metabolism and the PPARγ signaling pathway. This review provides a comprehensive analysis of recent studies on PPARγ gene polymorphisms and their association with T2D, with a specific emphasis on the implications of gut microbiota and their interaction with PPARγ pathways. We also discuss the potential of manipulating gut microbiota and targeting PPARγ gene polymorphisms in T2D management. By deepening our understanding of these relationships, we aim to pave the way for novel preventative and therapeutic strategies for T2D.

Exploring the molecular mechanism underlying the psoriasis and T2D by using microarray data analysis

Although a large number of evidence has identified that psoriasis is significantly correlated with type 2 diabetes (T2D), the common molecular mechanism of its occurrence remains unclear. Our study aims to further elucidate the mechanism of the occurrence of this complication. We obtained the gene expression data of psoriasis (GSE30999) and T2D (GSE28829) from the Gene Expression Omnibus (GEO) dataset. Then the common differentially expressed genes (DEGs) of T2D and psoriasis were identified. After that, we performed three types of analyses about these DEGs, including functional enrichment analysis, protein-protein interaction (PPI) network and module manufacture, hub genes identification and co-expression analysis. 132 common DEGs (14 upregulated genes and 118 downregulated genes) were identified for subsequent a series of analyses. Function enrichment analysis demonstrated that Rap1 signaling pathway, PI3K-Akt signaling pathway, and cGMP-PKG signaling pathway may play a significant role in pathogenesis of psoriasis and T2D. Finally, 3 important hub genes were selected by utilizing cytoHubba, including SNRPN, GNAS, IGF2. Our work reveals the potential common signaling pathways of psoriasis and T2D. These Hub genes and common signaling pathways provide insights for further investigation of molecular mechanism about psoriasis and T2D.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on β cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on β cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with β cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.

Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

ARTICLE HIGHLIGHTS

A PPARγ/long noncoding RNA axis regulates adipose thermoneutral remodeling in mice

Interplay between energy-storing white adipose cells and thermogenic beige adipocytes contributes to obesity and insulin resistance. Irrespective of specialized niche, adipocytes require the activity of the nuclear receptor PPARγ for proper function. Exposure to cold or adrenergic signaling enriches thermogenic cells though multiple pathways that act synergistically with PPARγ; however, the molecular mechanisms by which PPARγ licenses white adipose tissue to preferentially adopt a thermogenic or white adipose fate in response to dietary cues or thermoneutral conditions are not fully elucidated. Here, we show that a PPARγ/long noncoding RNA (lncRNA) axis integrates canonical and noncanonical thermogenesis to restrain white adipose tissue heat dissipation during thermoneutrality and diet-induced obesity. Pharmacologic inhibition or genetic deletion of the lncRNA Lexis enhances uncoupling protein 1-dependent (UCP1-dependent) and -independent thermogenesis. Adipose-specific deletion of Lexis counteracted diet-induced obesity, improved insulin sensitivity, and enhanced energy expenditure. Single-nuclei transcriptomics revealed that Lexis regulates a distinct population of thermogenic adipocytes. We systematically map Lexis motif preferences and show that it regulates the thermogenic program through the activity of the metabolic GWAS gene and WNT modulator TCF7L2. Collectively, our studies uncover a new mode of crosstalk between PPARγ and WNT that preserves white adipose tissue plasticity.

Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors

OBJECTIVE

Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge.

METHODS

Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci.

RESULTS

Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells.

CONCLUSIONS

These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.

The Associations of Genetically Predicted Plasma Alanine with Coronary Artery Disease and its Risk Factors: A Mendelian Randomization Study

BACKGROUND

Alanine is an amino acid commonly used as a nutritional supplement and plays a key role in the glucose-alanine cycle. Plasma alanine has been associated in observational studies with a higher risk of coronary artery disease (CAD) and unhealthier lipid profiles. However, evidence from large randomized controlled trials is lacking.

OBJECTIVES

Using Mendelian randomization (MR), we assessed the unconfounded associations of plasma alanine with CAD and CAD risk factors.

METHODS

We applied single nucleotide polymorphisms that were strongly (P < 5 ×10-8) associated with plasma alanine as genetic instruments to large genome-wide association studies of CAD (63,108 cases; 296,901 controls), diabetes (90,612 cases; 583,493 controls), glucose (515,538 participants), lipids (low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol, triglycerides, total cholesterol, and apolipoprotein B) (>1.1 million participants), blood pressure (BP) (757,601 participants), and body mass index (682,137 participants). Given the potential sex disparity, we also conducted sex-specific analyses. MR estimates per standard deviation increase in alanine concentrations were obtained using inverse variance weighting followed by sensitivity analyses using weighted median, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier, and MR-Robust Adjusted Profile Score.

RESULTS

Genetically predicted plasma alanine was not associated with CAD but with a higher risk of diabetes (odds ratio [OR]: 1.35; 95% confidence interval [CI]: 1.06, 1.72), higher glucose (β: 0.11; 95% CI: 0.02, 0.19), LDL cholesterol (β: 0.08; 95% CI: 0.04, 0.12), triglycerides (β: 0.25; 95% CI: 0.13, 0.38), total cholesterol (β: 0.14; 95% CI: 0.08, 0.20), apolipoprotein B (β: 0.12; 95% CI: 0.03, 0.21), and BP (β: 1.17; 95% CI: 0.31, 2.04 for systolic BP: β: 0.97; 95% CI: 0.49, 1.45 for diastolic BP) overall. The positive associations of serum alanine with LDL cholesterol and triglycerides were more notable in women than in men.

CONCLUSIONS

Alanine or factors affecting alanine may have causal effects on diabetes, blood glucose, lipid profiles, and BP but not on CAD. Further studies are needed to clarify possible mechanisms.

An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets

Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology.

ARTICLE HIGHLIGHTS

Additional Evidence for the Relationship Between Type 2 Diabetes and Stroke Through Observational and Genetic Analyses

While type 2 diabetes mellitus (T2DM) is commonly considered a putative causal risk factor for stroke, the effect of stroke on T2DM remains unclear. The intrinsic link underlying T2DM and stroke has not been thoroughly examined. We aimed to evaluate the phenotypic and genetic relationships underlying T2DM and stroke. We evaluated phenotypic associations using data from the UK Biobank (N = 472,050). We then investigated genetic relationships by leveraging genomic data in European ancestry for T2DM, with and without adjusting (adj) for BMI (T2DM: n = 74,124 case subjects/824,006 control subjects; T2DMadjBMI: n = 50,409 case subjects/523,897 control subjects), and for stroke (n = 73,652 case subjects/1,234,808 control subjects). We performed additional analyses using genomic data in East Asian ancestry for T2DM (n = 77,418 case subjects/356,122 control subjects) and for stroke (n = 27,413 case subjects/237,242 control subjects). Observational analyses suggested a significantly increased hazard of stroke among individuals with T2DM (hazard ratio 2.28 [95% CI 1.97-2.64]), but a slightly increased hazard of T2DM among individuals with stroke (1.22 [1.03-1.45]) which attenuated to 1.14 (0.96-1.36) in sensitivity analysis. A positive global T2DM-stroke genetic correlation was observed (rg = 0.35; P = 1.46 × 10-27), largely independent of BMI (T2DMadjBMI-stroke: rg = 0.27; P = 3.59 × 10-13). This was further corroborated by 38 shared independent loci and 161 shared expression-trait associations. Mendelian randomization analyses suggested a putative causal effect of T2DM on stroke in Europeans (odds ratio 1.07 [95% CI 1.06-1.09]), which remained significant in East Asians (1.03 [1.01-1.06]). Conversely, despite a putative causal effect of stroke on T2DM also observed in Europeans (1.21 [1.07-1.37]), it attenuated to 1.04 (0.91-1.19) in East Asians. Our study provides additional evidence to underscore the significant relationship between T2DM and stroke.

ARTICLE HIGHLIGHTS

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

Transcriptome-wide association study-derived genes as potential visceral adipose tissue-specific targets for type 2 diabetes

AIMS/HYPOTHESIS

This study aimed to assess the causal relationship between visceral obesity and type 2 diabetes and subsequently to screen visceral adipose tissue (VAT)-specific targets for type 2 diabetes.

METHODS

We examined the causal relationship between VAT and type 2 diabetes using bidirectional Mendelian randomisation (MR) followed by multivariable MR. We conducted a transcriptome-wide association study (TWAS) leveraging prediction models and a large-scale type 2 diabetes genome-wide association study (74,124 cases and 824,006 controls) to identify candidate genes in VAT and used summary-data-based MR (SMR) and co-localisation analysis to map causal genes. We performed enrichment and single-cell RNA-seq analyses to determine the cell-specific localisation of the TWAS-identified genes. We also conducted knockdown experiments in 3T3-L1 pre-adipocytes.

RESULTS

MR analyses showed a causal relationship between genetically increased VAT mass and type 2 diabetes (inverse-variance weighted OR 2.48 [95% CI 2.21, 2.79]). Ten VAT-specific candidate genes were associated with type 2 diabetes after Bonferroni correction, including five causal genes supported by SMR and co-localisation: PABPC4 (1p34.3); CCNE2 (8q22.1); HAUS6 (9p22.1); CWF19L1 (10q24.31); and CCDC92 (12q24.31). Combined with enrichment analyses, clarifying cell-type specificity with single-cell RNA-seq data indicated that most TWAS-identified candidate genes appear more likely to be associated with adipocytes in VAT. Knockdown experiments suggested that Pabpc4 likely contributes to regulating differentiation and energy metabolism in 3T3-L1 adipocytes.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic basis and biological processes of the association between VAT accumulation and type 2 diabetes and warrant investigation through further functional studies to validate these VAT-specific candidate genes.

Splicing across adipocyte differentiation is highly dynamic and impacted by metabolic phenotype

Adipose tissue dysfunction underlies many of the metabolic complications associated with obesity. A better understanding of the gene regulation differences present in metabolically unhealthy adipose tissue can provide insights into the mechanisms underlying adipose tissue dysfunction. Here, we used RNA-seq data collected from a differentiation time course of lean, obese, and obese with type 2 diabetes (T2D) individuals to characterize the role of alterative splicing in adipocyte differentiation and function. We found that splicing was highly dynamic across adipocyte differentiation in all three cohorts, and that the dynamics of splicing were significantly impacted by metabolic phenotype. We also found that there was very little overlap between genes that were differentially spliced in adipocyte differentiation and those that were differentially expressed, positioning alternative splicing as a largely independent gene regulatory mechanism whose impact would be missed when looking at gene expression changes alone. To assess the impact of alternative splicing across adipocyte differentiation on genetic risk for metabolic diseases, we integrated the differential splicing results generated here with genome-wide association study results for body mass index and T2D, and found that variants associated with T2D were enriched in regions that were differentially spliced in early differentiation. These findings provide insight into the role of alternative splicing in adipocyte differentiation and can serve as a resource to guide future variant-to-function studies.

eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk

The impact of genetic regulatory variation active in early pancreatic development on adult pancreatic disease and traits is not well understood. Here, we generate a panel of 107 fetal-like iPSC-derived pancreatic progenitor cells (iPSC-PPCs) from whole genome-sequenced individuals and identify 4065 genes and 4016 isoforms whose expression and/or alternative splicing are affected by regulatory variation. We integrate eQTLs identified in adult islets and whole pancreas samples, which reveal 1805 eQTL associations that are unique to the fetal-like iPSC-PPCs and 1043 eQTLs that exhibit regulatory plasticity across the fetal-like and adult pancreas tissues. Colocalization with GWAS risk loci for pancreatic diseases and traits show that some putative causal regulatory variants are active only in the fetal-like iPSC-PPCs and likely influence disease by modulating expression of disease-associated genes in early development, while others with regulatory plasticity likely exert their effects in both the fetal and adult pancreas by modulating expression of different disease genes in the two developmental stages.

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with ≥2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

Identification of MicroRNAs Associated with Prediabetic Status in Obese Women

MicroRNAs (miRNAs) recently emerged as means of communication between insulin-sensitive tissues to mediate diabetes development and progression, and as such they present a valuable proxy for epigenetic alterations associated with type 2 diabetes. In order to identify miRNA markers for the precursor of diabetes called prediabetes, we applied a translational approach encompassing analysis of human plasma samples, mouse tissues and an in vitro validation system. MiR-652-3p, miR-877-5p, miR-93-5p, miR-130a-3p, miR-152-3p and let-7i-5p were increased in plasma of women with impaired fasting glucose levels (IFG) compared to those with normal fasting glucose and normal glucose tolerance (NGT). Among these, let-7i-5p and miR-93-5p correlated with fasting blood glucose levels. Human data were then compared to miRNome data obtained from islets of Langerhans and adipose tissue of 10-week-old female New Zealand Obese mice, which differ in their degree of hyperglycemia and liver fat content. Similar to human plasma, let-7i-5p was increased in adipose tissue and islets of Langerhans of diabetes-prone mice. As predicted by the in silico analysis, overexpression of let-7i-5p in the rat β-cell line INS-1 832/12 resulted in downregulation of insulin signaling pathway components (Insr, Rictor, Prkcb, Clock, Sos1 and Kcnma1). Taken together, our integrated approach highlighted let-7i-5p as a potential regulator of whole-body insulin sensitivity and a novel marker of prediabetes in women.

An integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D associated genes and regulatory signals

Genome wide association studies (GWAS) have identified over 100 signals associated with type 1 diabetes (T1D). However, translating any given T1D GWAS signal into mechanistic insights, including putative causal variants and the context (cell type and cell state) in which they function, has been limited. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in healthy and autoantibody+ (AAB+) human islets, as well as islets under multiple T1D stimulatory conditions. We broadly nominate effector cell types for all T1D GWAS signals. We further nominated higher-resolution contexts, including effector cell types, regulatory elements, and genes for three independent T1D risk variants acting through islet cells within the pancreas at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we created isogenic gene knockouts DLK1-/-, RASGRP1-/-, and TOX-/-, and the corresponding regulatory region knockout, RASGRP1Δ, and DLK1Δ hESCs. Loss of RASGRP1 or DLK1, as well as knockout of the regulatory region of RASGRP1 or DLK1, increased β cell apoptosis. Additionally, pancreatic β cells derived from isogenic hESCs carrying the risk allele of rs3783355A/A exhibited increased β cell death. Finally, RNA-seq and ATAC-seq identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are associated with T1D. Together, this work reports an integrative approach for combining single cell multi-omics, GWAS, and isogenic hESC-derived β-like cells to prioritize the T1D associated signals and their underlying context-specific cell types, genes, SNPs, and regulatory elements, to illuminate biological functions and molecular mechanisms.

Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction

One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease-variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help to complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we have taken advantage of High-Performance Computing technologies to tackle this problem using a combination of machine learning methods and statistical approaches. As a result, we have created a containerized framework that uses Multifactor Dimensionality Reduction to detect pairs of variants associated with Type 2 Diabetes (T2D). This methodology has been tested in the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we have identified 104 significant pairs, two of which exhibit a potential functional relationship with T2D.

Evolution of the search for a common mechanism of congenital risk of coronary heart disease and type 2 diabetes mellitus in the chromosomal locus 9p21.3

9.21.3 chromosomal locus predisposes to coronary heart disease (CHD) and type 2 diabetes mellitus (DM2), but their overall pathological mechanism and clinical applicability remain unclear. The review uses publications of the study results of 9.21.3 chromosomal locus in association with CHD and DM2, which are important for changing the focus of clinical practice. The eligibility criteria are full-text articles published in the PubMed database (MEDLINE) up to December 31, 2022. A total of 56 publications were found that met the inclusion criteria. Using the examples of the progressive stages in understanding the role of the chromosomal locus 9p.21.3, scientific ideas were grouped, from a fragmentary study of independent pathological processes to a systematic study of the overall development of CHD and DM2. The presented review can become a source of new scientific hypotheses for further studies, the results of which can determine the general mechanism of the congenital risk of CHD and DM2 and change the focus of clinical practice.

Single-cell DNA methylation and 3D genome architecture in the human brain

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, combined allele-specific expression (cASE) analysis in human islets revealed multiple variants that influence SLC30A8 expression. Epigenomic mapping identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighbouring genes. Deletions of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowered the expression of SLC30A8 and several neighbouring genes, and improved insulin secretion. Whilst down-regulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21 or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes

Recent genome-wide association studies have established that most complex disease-associated loci are found in noncoding regions where defining their function is nontrivial. In this study, we leverage a modular massively parallel reporter assay (MPRA) to uncover sequence features linked to context-specific regulatory activity. We screened enhancer activity across a panel of 198-bp fragments spanning over 10k type 2 diabetes- and metabolic trait-associated variants in the 832/13 rat insulinoma cell line, a relevant model of pancreatic beta cells. We explored these fragments' context sensitivity by comparing their activities when placed up-or downstream of a reporter gene, and in combination with either a synthetic housekeeping promoter (SCP1) or a more biologically relevant promoter corresponding to the human insulin gene ( INS ). We identified clear effects of MPRA construct design on measured fragment enhancer activity. Specifically, a subset of fragments (n = 702/11,656) displayed positional bias, evenly distributed across up- and downstream preference. A separate set of fragments exhibited promoter bias (n = 698/11,656), mostly towards the cell-specific INS promoter (73.4%). To identify sequence features associated with promoter preference, we used Lasso regression with 562 genomic annotations and discovered that fragments with INS promoter-biased activity are enriched for HNF1 motifs. HNF1 family transcription factors are key regulators of glucose metabolism disrupted in maturity onset diabetes of the young (MODY), suggesting genetic convergence between rare coding variants that cause MODY and common T2D-associated regulatory variants. We designed a follow-up MPRA containing HNF1 motif-enriched fragments and observed several instances where deletion or mutation of HNF1 motifs disrupted the INS promoter-biased enhancer activity, specifically in the beta cell model but not in a skeletal muscle cell line, another diabetes-relevant cell type. Together, our study suggests that cell-specific regulatory activity is partially influenced by enhancer-promoter compatibility and indicates that careful attention should be paid when designing MPRA libraries to capture context-specific regulatory processes at disease-associated genetic signals.

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

Context

Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.

Objective

We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

Methods

We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.

Results

The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.

Conclusion

In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Leveraging osteoclast genetic regulatory data to identify genes with a role in osteoarthritis

There has been a growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by noncoding variants, suggesting that genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for studying the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary data-based Mendelian randomization (SMR) and colocalization analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3, and FBN2. Several OA GWAS signals demonstrated colocalization with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2, and BICRA eQTL). We also identified a number of eQTL signals colocalizing with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP, and TRIOBP. An SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1, and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harboring 2 other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA.

Shared genetic basis informs the roles of polyunsaturated fatty acids in brain disorders

The neural tissue is rich in polyunsaturated fatty acids (PUFAs), components that are indispensable for the proper functioning of neurons, such as neurotransmission. PUFA nutritional deficiency and imbalance have been linked to a variety of chronic brain disorders, including major depressive disorder (MDD), anxiety, and anorexia. However, the effects of PUFAs on brain disorders remain inconclusive, and the extent of their shared genetic determinants is largely unknown. Here, we used genome-wide association summary statistics to systematically examine the shared genetic basis between six phenotypes of circulating PUFAs (N = 114,999) and 20 brain disorders (N = 9,725-762,917), infer their potential causal relationships, identify colocalized regions, and pinpoint shared genetic variants. Genetic correlation and polygenic overlap analyses revealed a widespread shared genetic basis for 77 trait pairs between six PUFA phenotypes and 16 brain disorders. Two-sample Mendelian randomization analysis indicated potential causal relationships for 16 pairs of PUFAs and brain disorders, including alcohol consumption, bipolar disorder (BIP), and MDD. Colocalization analysis identified 40 shared loci (13 unique) among six PUFAs and ten brain disorders. Twenty-two unique variants were statistically inferred as candidate shared causal variants, including rs1260326 (GCKR), rs174564 (FADS2) and rs4818766 (ADARB1). These findings reveal a widespread shared genetic basis between PUFAs and brain disorders, pinpoint specific shared variants, and provide support for the potential effects of PUFAs on certain brain disorders, especially MDD, BIP, and alcohol consumption.

Investigating causal relationships between obesity and skin barrier function in a multi-ethnic Asian general population cohort

BACKGROUND

Skin diseases impact significantly on the quality of life and psychology of patients. Obesity has been observed as a risk factor for skin diseases. Skin epidermal barrier dysfunctions are typical manifestations across several dermatological disturbances.

OBJECTIVES

We aim to establish the association between obesity and skin physiology measurements and investigate whether obesity may play a possible causal role on skin barrier dysfunction.

METHODS

We investigated the relationship of obesity with skin physiology measurements, namely transepidermal water loss (TEWL), skin surface moisture and skin pH in an Asian population cohort (n = 9990). To assess for a possible causal association between body mass index (BMI) and skin physiology measurements, we performed Mendelian Randomization (MR), along with subsequent additional analyses to assess the potential causal impact of known socioeconomic and comorbidities of obesity on TEWL.

RESULTS

Every 1 kg/m2 increase in BMI was associated with a 0.221% (95%CI: 0.144-0.298) increase in TEWL (P = 2.82E-08), a 0.336% (95%CI: 0.148-0.524) decrease in skin moisture (P = 4.66E-04) and a 0.184% (95%CI: 0.144-0.224) decrease in pH (P = 1.36E-19), adjusting for age, gender, and ethnicity. Relationships for both TEWL and pH with BMI remained strong (Beta 0.354; 95%CI: 0.189-0.520 and Beta -0.170; 95%CI: -0.253 to -0.087, respectively) even after adjusting for known confounders, with MR experiments further supporting BMI's possible causal relationship with TEWL. Based on additional MR performed, none of the socioeconomic and comorbidities of obesity investigated are likely to have possible causal relationships with TEWL.

CONCLUSION

We establish strong association of BMI with TEWL and skin pH, with MR results suggestive of a possible causal relationship of obesity with TEWL. It emphasizes the potential impact of obesity on skin barrier function and therefore opportunity for primary prevention.

Association of Genetically Predicted Insomnia With Risk of Sepsis: A Mendelian Randomization Study

Importance

Insomnia has been associated with altered inflammatory response as well as increased risk of infections and sepsis in observational studies. However, these studies are prone to bias, such as residual confounding. To further understand the potential causal association between insomnia and sepsis risk, a 2-sample Mendelian randomization (MR) approach should be explored.

Objective

To evaluate whether genetically predicted insomnia is associated with risk of sepsis.

Design, Setting, and Participants

Two-sample MR was performed to estimate the association between genetically predicted insomnia and sepsis risk. Data were obtained from a genome-wide association study identifying 555 independent genetic variants (R2 < 0.01) strongly associated with insomnia (P < 5 × 10-8). Sensitivity analyses were conducted to address bias due to pleiotropy and sample overlap, along with mediation analyses and sex-stratified analyses. The insomnia data set included 2.4 million individuals of European ancestry from the UK Biobank and 23andMe. For sepsis, 462 918 individuals of European ancestry from the UK Biobank were included. Data were extracted between February and December 2022 and analyzed between March 2022 and March 2023.

Exposure

Genetically predicted insomnia.

Main Outcome and Measure

Sepsis.

Results

There were 593 724 individuals with insomnia and 10 154 cases of sepsis. A doubling in the population prevalence of genetically predicted insomnia was associated with an odds ratio of 1.37 (95% CI, 1.19-1.57; P = 7.6 × 10-6) for sepsis. Sensitivity analyses supported this observation. One-third of the association between genetically predicted insomnia and risk of sepsis was mediated through a combination of cardiometabolic risk factors for sepsis (body mass index, type 2 diabetes, smoking, or cardiovascular disease; overall proportion, 35.2%; 95% CI, 5.1-76.9). The association between insomnia and sepsis was more pronounced among women compared with men (women: odds ratio, 1.44; 95% CI, 1.24-1.68; men: OR, 1.10; 95% CI, 0.86-1.40).

Conclusions and Relevance

The concordance between these findings and previous observational studies supports that insomnia is potentially causally associated with the risk of sepsis. Thus, insomnia is a potential preventable risk factor of sepsis that should be further investigated, also in non-European populations.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort

AIMS/HYPOTHESIS

There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution.

METHODS

Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin.

RESULTS

A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin.

CONCLUSIONS/INTERPRETATION

The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.

Large-scale plasma proteomics comparisons through genetics and disease associations

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

Lowering of Circulating Sclerostin May Increase Risk of Atherosclerosis and Its Risk Factors: Evidence From a Genome-Wide Association Meta-Analysis Followed by Mendelian Randomization

OBJECTIVE

In this study, we aimed to establish the causal effects of lowering sclerostin, target of the antiosteoporosis drug romosozumab, on atherosclerosis and its risk factors.

METHODS

A genome-wide association study meta-analysis was performed of circulating sclerostin levels in 33,961 European individuals. Mendelian randomization (MR) was used to predict the causal effects of sclerostin lowering on 15 atherosclerosis-related diseases and risk factors.

RESULTS

We found that 18 conditionally independent variants were associated with circulating sclerostin. Of these, 1 cis signal in SOST and 3 trans signals in B4GALNT3, RIN3, and SERPINA1 regions showed directionally opposite signals for sclerostin levels and estimated bone mineral density. Variants with these 4 regions were selected as genetic instruments. MR using 5 correlated cis-SNPs suggested that lower sclerostin increased the risk of type 2 diabetes mellitus (DM) (odds ratio [OR] 1.32 [95% confidence interval (95% CI) 1.03-1.69]) and myocardial infarction (MI) (OR 1.35 [95% CI 1.01-1.79]); sclerostin lowering was also suggested to increase the extent of coronary artery calcification (CAC) (β = 0.24 [95% CI 0.02-0.45]). MR using both cis and trans instruments suggested that lower sclerostin increased hypertension risk (OR 1.09 [95% CI 1.04-1.15]), but otherwise had attenuated effects.

CONCLUSION

This study provides genetic evidence to suggest that lower levels of sclerostin may increase the risk of hypertension, type 2 DM, MI, and the extent of CAC. Taken together, these findings underscore the requirement for strategies to mitigate potential adverse effects of romosozumab treatment on atherosclerosis and its related risk factors.

Efficacy of metformin targets on cardiometabolic health in the general population and non-diabetic individuals: a Mendelian randomization study

BACKGROUND

Metformin shows beneficial effects on cardiometabolic health in diabetic individuals. However, the beneficial effects in the general population, especially in non-diabetic individuals are unclear. We aim to estimate the effects of perturbation of seven metformin targets on cardiometabolic health using Mendelian randomization (MR).

METHODS

Genetic variants close to metformin-targeted genes associated with expression of the corresponding genes and glycated haemoglobin (HbA1c) level were used to proxy therapeutic effects of seven metformin-related drug targets. Eight cardiometabolic phenotypes under metformin trials were selected as outcomes (average N = 466,947). MR estimates representing the weighted average effects of the seven effects of metformin targets on the eight outcomes were generated. One-sample MR was applied to estimate the averaged and target-specific effects in 338,425 non-diabetic individuals in UK Biobank.

FINDINGS

Genetically proxied averaged effects of five metformin targets, equivalent to a 0.62% reduction of HbA1c level, was associated with 37.8% lower risk of coronary artery disease (CAD) (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.46-0.84), lower levels of body mass index (BMI) (β = -0.22, 95% CI = -0.35 to -0.09), systolic blood pressure (SBP) (β = -0.19, 95% CI = -0.28 to -0.09) and diastolic blood pressure (DBP) levels (β = -0.29, 95% CI = -0.39 to -0.19). One-sample MR suggested that the seven metformin targets showed averaged and target-specific beneficial effects on BMI, SBP and DBP in non-diabetic individuals.

INTERPRETATION

This study showed that perturbation of seven metformin targets has beneficial effects on BMI and blood pressure in non-diabetic individuals. Clinical trials are needed to investigate whether similar effects can be achieved with metformin medications.

FUNDING

Funding information is provided in the Acknowledgements.

Genetically proxied antidiabetic drugs targets and stroke risk

BACKGROUND

Previous studies have assessed the association between antidiabetic drugs and stroke risk, but the results are inconsistent. Mendelian randomization (MR) was used to assess effects of antidiabetic drugs on stroke risk.

METHODS

We selected blood glucose-lowering variants in genes encoding antidiabetic drugs targets from genome-wide association studies (GWAS). A two-sample MR and Colocalization analyses were applied to examine associations between antidiabetic drugs and the risk of stroke. For antidiabetic agents that had effect on stroke risk, an independent blood glucose GWAS summary data was used for further verification.

RESULTS

Genetic proxies for sulfonylureas targets were associated with reduced risk of any stroke (OR=0.062, 95% CI 0.013-0.295, P=4.65×10－4) and any ischemic stroke (OR=0.055, 95% CI 0.010-0.289, P=6.25×10－4), but not with intracranial hemorrhage. Colocalization supported shared casual variants for blood glucose with any stroke and any ischemic stroke within the encoding genes for sulfonylureas targets (KCNJ11 and ABCC8) (posterior probability>0.7). Furthermore, genetic variants in the targets of insulin/insulin analogues, glucagon-like peptide-1 analogues, thiazolidinediones, and metformin were not associated with the risk of any stroke, any ischemic stroke and intracranial hemorrhage. The association was consistent in the analysis of sulfonylureas with stroke risk using an independent blood glucose GWAS summary data.

CONCLUSIONS

Our findings showed that genetic proxies for sulfonylureas targets by lowering blood glucose were associated with a lower risk of any stroke and any ischemic stroke. The study might be of great significance to guide the selection of glucose-lowering drugs in individuals at high risk of stroke.

MRPS6 modulates glucose-stimulated insulin secretion in mouse islet cells through mitochondrial unfolded protein response

Lack of efficient insulin secretion from the pancreas can lead to impaired glucose tolerance (IGT), prediabetes, and diabetes. We have previously identified two IGT-associated single nucleotide polymorphisms (SNPs) rs62212118 and rs13052524 located at two overlapping genes: MRPS6 and SLC5A3. In this study, we show that MRPS6 but not SLC5A3 regulates glucose-stimulated insulin secretion (GSIS) in primary human β-cell and a mouse pancreatic insulinoma β-cell line. Data mining and biochemical studies reveal that MRPS6 is positively regulated by the mitochondrial unfolded protein response (UPRmt), but feedback inhibits UPRmt. Disruption of such feedback by MRPS6 knockdown causes UPRmt hyperactivation in high glucose conditions, hence elevated ROS levels, increased apoptosis, and impaired GSIS. Conversely, MRPS6 overexpression reduces UPRmt, mitigates high glucose-induced ROS levels and apoptosis, and enhances GSIS in an ATF5-dependent manner. Consistently, UPRmt up-regulation or down-regulation by modulating ATF5 expression is sufficient to decrease or increase GSIS. The negative role of UPRmt in GSIS is further supported by analysis of public transcriptomic data from murine islets. In all, our studies identify MRPS6 and UPRmt as novel modulators of GSIS and apoptosis in β-cells, contributing to our understanding of the molecular and cellular mechanisms of IGT, prediabetes, and diabetes.

Ensembled best subset selection using summary statistics for polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L 0 L 2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

Understanding the relationship between circulating lipids and risk of chronic kidney disease: a prospective cohort study and large-scale genetic analyses

BACKGROUND

This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR).

METHODS

We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460).

RESULTS

There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60-0.95; TG: HR = 1.08, 95%CI = 1.02-1.13) and global genetic correlations (HDL-C: [Formula: see text] = - 0.132, P = 1.00 × 10-4; TG: [Formula: see text] = 0.176; P = 2.66 × 10-5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85-0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed.

CONCLUSIONS

Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD.

Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study

Objective

With the high prevalence of pediatric obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D).

Methods

We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) of diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS).

Results

Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide ≥0.75 ng/ml (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under receiver operator curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope=0.995-0.999). Models retained high performance for predicting retained c-peptide in older youth with obesity (AUCROC 0.88-0.96), and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92).

Conclusion

Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with type 2 diabetes.

The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis

While neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. MAIN: Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects ("SNP-to-gene" mapping), (ii) genes with kidney phenotypes in mice or human ("gene-to-phenotype"), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function ("GPS tab") to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data ( https://kidneygps.ur.de/gps/ ).

CONCLUSION

With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.