Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology

Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.

Update on Animal Models of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a complex disease affecting up to 15% of women of reproductive age. Women with PCOS suffer from reproductive dysfunctions with excessive androgen secretion and irregular ovulation, leading to reduced fertility and pregnancy complications. The syndrome is associated with a wide range of comorbidities including type 2 diabetes, obesity, and psychiatric disorders. Despite the high prevalence of PCOS, its etiology remains unclear. To understand the pathophysiology of PCOS, how it is inherited, and how to predict PCOS, and prevent and treat women with the syndrome, animal models provide an important approach to answering these fundamental questions. This minireview summarizes recent investigative efforts on PCOS-like rodent models aiming to define underlying mechanisms of the disease and provide guidance in model selection. The focus is on new genetic rodent models, on a naturally occurring rodent model, and provides an update on prenatal and peripubertal exposure models.

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning

MOTIVATION

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

RESULTS

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.

AVAILABILITY AND IMPLEMENTATION

The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Causal effects of genetically predicted type 2 diabetes mellitus on blood lipid profiles and concentration of particle-size-determined lipoprotein subclasses: A two-sample Mendelian randomization study

Background

Observational studies have shown inconsistent results of the associations between type 2 diabetes mellitus (T2DM) and blood lipid profiles, while there is also a lack of evidence from randomized controlled trials (RCTs) for the causal effects of T2DM on blood lipid profiles and lipoprotein subclasses.

Objectives

Our study aimed at investigating the causal effects of T2DM on blood lipid profiles and concentration of particle-size-determined lipoprotein subclasses by using the two-sample Mendelian randomization (MR) method.

Methods

We obtained genetic variants for T2DM and blood lipid profiles including high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), triglycerides (TG), and total cholesterol (TC) from international genome-wide association studies (GWASs). Two-sample MR method was applied to explore the potential causal effects of genetically predicted T2DM on blood lipid profiles based on different databases, respectively, and results from each MR analysis were further meta-analyzed to obtain the summary results. The causal effects of genetically predicted T2DM on the concentration of different subclasses of lipoproteins that are determined by particle size were also involved in MR analysis.

Results

Genetically predicted 1-unit higher log odds of T2DM had a significant causal effect on a higher level of TG (estimated β coefficient: 0.03, 95% confidence interval [CI]: 0.00 to 0.06) and lower level of HDL-C (estimated β coefficient: −0.09, 95% CI: −0.11 to −0.06). The causality of T2DM on the level of TC or LDL-C was not found (estimated β coefficient: −0.01, 95% CI: −0.02 to 0.01 for TC and estimated β coefficient: 0.01, 95% CI: −0.01 to 0.02 for LDL-C). For different sizes of lipoprotein particles, 1-unit higher log odds of T2DM was causally associated with higher level of small LDL particles, and lower level of medium HDL particles, large HDL particles, and very large HDL particles.

Conclusion

Evidence from our present study showed causal effects of T2DM on the level of TG, HDL-C, and concentration of different particle sizes of lipoprotein subclasses comprehensively, which might be particularly helpful in illustrating dyslipidemia experienced by patients with T2DM, and further indicate new treatment targets for these patients to prevent subsequent excessive cardiovascular events from a genetic point of view.

ExPRSweb: An online repository with polygenic risk scores for common health-related exposures

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

Advances in multi-omics study of biomarkers of glycolipid metabolism disorder

Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.

Sodium-glucose cotransporter 1 inhibition and gout: Mendelian randomisation study

OBJECTIVE

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce serum urate, but their efficacy depends on renal function which is often impaired in people with gout. SGLT1 is primarily expressed in the small intestine and its inhibition may be a more suitable therapeutic target. We aimed to investigate the association of genetically proxied SGLT1i with gout risk, serum urate levels and cardiovascular safety using Mendelian randomisation (MR).

METHODS

Leveraging data from a genome-wide association study of 344,182 individuals in the UK Biobank, we identified a missense variant in the SLC5A1 gene that associated with glycated haemoglobin (HbA1c) to proxy SGLT1i. Outcome genetic data comprised 13,179 gout cases and 750,634 controls, 457,690 individuals for serum urate levels, and up to 977,323 individuals for cardiovascular safety outcomes. We applied the Wald ratio method and investigated potential genetic confounding using colocalization.

RESULTS

The rs17683430 missense variant was selected to instrument SGLT1i. Genetically proxied SGLT1i was associated with 75% reduction in gout risk (OR 0.25; 95%CI 0.06, 0.99; p = 0.048) and 32.0 μmol/L reduction in serum urate (95%CI -56.7, -7.3; p = 0.01), per 6.7 mmol/mol reduction in HbA1c. SGLT1i was associated with increased levels of low-density lipoprotein cholesterol (0.37 mmol/L; 95%CI 0.17, 0.56; p = 0.0002) but not risk of coronary heart disease, stroke, or chronic kidney disease. Colocalization did not suggest that results are attributable to genetic confounding.

CONCLUSION

SGLT1 inhibition may represent a novel therapeutic option for preventing gout in people with or without comorbid diabetes. Randomised trials are needed to formally investigate efficacy and safety.

The genetic overlap between osteoporosis and craniosynostosis

Osteoporosis is the most prevalent bone condition in the ageing population. This systemic disease is characterized by microarchitectural deterioration of bone, leading to increased fracture risk. In the past 15 years, genome-wide association studies (GWAS), have pinpointed hundreds of loci associated with bone mineral density (BMD), helping elucidate the underlying molecular mechanisms and genetic architecture of fracture risk. However, the challenge remains in pinpointing causative genes driving GWAS signals as a pivotal step to drawing the translational therapeutic roadmap. Recently, a skull BMD-GWAS uncovered an intriguing intersection with craniosynostosis, a congenital anomaly due to premature suture fusion in the skull. Here, we recapitulate the genetic contribution to both osteoporosis and craniosynostosis, describing the biological underpinnings of this overlap and using zebrafish models to leverage the functional investigation of genes associated with skull development and systemic skeletal homeostasis.

Metabolomics in Diabetic Retinopathy: From Potential Biomarkers to Molecular Basis of Oxidative Stress

Diabetic retinopathy (DR), the leading cause of blindness in working-age adults, is one of the most common complications of diabetes mellitus (DM) featured by metabolic disorders. With the global prevalence of diabetes, the incidence of DR is expected to increase. Prompt detection and the targeting of anti-oxidative stress intervention could effectively reduce visual impairment caused by DR. However, the diagnosis and treatment of DR is often delayed due to the absence of obvious signs of retina imaging. Research progress supports that metabolomics is a powerful tool to discover potential diagnostic biomarkers and therapeutic targets for the causes of oxidative stress through profiling metabolites in diseases, which provides great opportunities for DR with metabolic heterogeneity. Thus, this review summarizes the latest advances in metabolomics in DR, as well as potential diagnostic biomarkers, and predicts molecular targets through the integration of genome-wide association studies (GWAS) with metabolomics. Metabolomics provides potential biomarkers, molecular targets and therapeutic strategies for controlling the progress of DR, especially the interventions at early stages and precise treatments based on individual patient variations.

Ionically cross-linked alginate-chitosan core-shell hydrogel beads for oral delivery of insulin

Here, core-shell hydrogel beads for oral insulin delivery at intestine was reported, which was a target site for insulin absorption. The core-shell hydrogel beads were prepared using naturally derived alginate and chitosan polysaccharides by simple dropping technique. In order to effectively control leakage of insulin from core-shell hydrogel beads, insulin was embedded into the layered double hydroxides (LDHs). LDH/insulin-loaded complexes were firstly coated with chitosan, and then coated with alginate to generate core-shell hydrogel beads. The biocompatibility and angiogenic response of core-shell hydrogel beads were evaluated by direct contact of the beads with chick embryo chorioallantoic membrane, which indicates safety of the core-shell beads. The beads successfully retained the insulin within the core-shell structure at pH 1.2, indicating that insulin had a good protective effect in harsh acidic environments. Interestingly, insulin release starts at the simulated intestinal fluid (pH 6.8) and continue to release for 24 h in a sustained manner.

Replicability in cancer omics data analysis: measures and empirical explorations

In biomedical research, the replicability of findings across studies is highly desired. In this study, we focus on cancer omics data, for which the examination of replicability has been mostly focused on important omics variables identified in different studies. In published literature, although there have been extensive attention and ad hoc discussions, there is insufficient quantitative research looking into replicability measures and their properties. The goal of this study is to fill this important knowledge gap. In particular, we consider three sensible replicability measures, for which we examine distributional properties and develop a way of making inference. Applying them to three The Cancer Genome Atlas (TCGA) datasets reveals in general low replicability and significant across-data variations. To further comprehend such findings, we resort to simulation, which confirms the validity of the findings with the TCGA data and further informs the dependence of replicability on signal level (or equivalently sample size). Overall, this study can advance our understanding of replicability for cancer omics and other studies that have identification as a key goal.

Assessing the causal associations of obstructive sleep apnea with serum uric acid levels and gout: a bidirectional two-sample Mendelian randomization study

OBJECTIVE

Multiple observational studies have reported the close associations of obstructive sleep apnea (OSA) with serum uric acid (SUA) levels and gout. However, the causal nature and direction remains unclear.

METHODS

A bidirectional two-sample Mendelian randomization (MR) study was performed, based on publicly available genome-wide association studies (GWAS) summary statistics, to investigate whether OSA is causally related to SUA levels, gout and vice versa. The inverse-variance weighted (IVW) was used as the primary analysis approach, supplemented with four sensitive analysis methods applied to assess the robustness of the results. Moreover, multivariable MR (MVMR) was utilized to evaluate the independent causal effect of OSA on SUA and gout after adjusting for body mass index (BMI), hypertension, type 2 diabetes (T2D), coronary artery disease (CAD), and chronic kidney disease (CKD).

RESULTS

Genetically predicted OSA liability was significantly associated with increased levels of SUA (IVW method: β = 0.19, 95% CI = 0.11 - 0.26, P = 7.24 × 10^-7) and risk of gout [IVW method: odds ratio (OR) = 1.75 95% CI = 1.13 - 2.69, P = 0.01] in univariable MR. The MVMR results suggested that OSA retained its significant association with increased SUA levels, whereas the significant association between OSA and gout was attenuated to null after adjusting for BMI and T2D. No causal effect of OSA on SUA levels and gout was found in the reverse direction.

CONCLUSIONS

Our findings suggest that OSA was causally associated with increased levels of SUA, but was not independently associated with gout risk.

Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression

The proximal tubule is a key regulator of kidney function and glucose metabolism. Diabetic kidney disease leads to proximal tubule injury and changes in chromatin accessibility that modify the activity of transcription factors involved in glucose metabolism and inflammation. Here we use single nucleus RNA and ATAC sequencing to show that diabetic kidney disease leads to reduced accessibility of glucocorticoid receptor binding sites and an injury-associated expression signature in the proximal tubule. We hypothesize that chromatin accessibility is regulated by genetic background and closely-intertwined with metabolic memory, which pre-programs the proximal tubule to respond differently to external stimuli. Glucocorticoid excess has long been known to increase risk for type 2 diabetes, which raises the possibility that glucocorticoid receptor inhibition may mitigate the adverse metabolic effects of diabetic kidney disease.

L-carnitine, a friend or foe for cardiovascular disease? A Mendelian randomization study

BACKGROUND

L-carnitine is emerging as an item of interest for cardiovascular disease (CVD) prevention and treatment, but controversy exists. To examine the effectiveness and safety of L-carnitine, we assessed how genetically different levels of L-carnitine are associated with CVD risk and its risk factors. Given higher CVD incidence and L-carnitine in men, we also examined sex-specific associations.

METHODS

We used Mendelian randomization to obtain unconfounded estimates. Specifically, we used genetic variants to predict L-carnitine, and obtained their associations with coronary artery disease (CAD), ischemic stroke, heart failure, and atrial fibrillation, as well as CVD risk factors (type 2 diabetes, glucose, HbA1c, insulin, lipid profile, blood pressure and body mass index) in large consortia and established cohorts, as well as sex-specific association in the UK Biobank. We obtained the Wald estimates (genetic association with CVD and its risk factors divided by the genetic association with L-carnitine) and combined them using inverse variance weighting. In sensitivity analysis, we used different analysis methods robust to pleiotropy and replicated using an L-carnitine isoform, acetyl-carnitine.

RESULTS

Genetically predicted L-carnitine was nominally associated with higher risk of CAD overall (OR 1.07 per standard deviation (SD) increase in L-carnitine, 95% CI 1.02 to 1.11) and in men (OR 1.09, 95% CI 1.02 to 1.16) but had a null association in women (OR 1.00, 95% CI 0.92 to 1.09). These associations were also robust to different methods and evident for acetyl-carnitine.

CONCLUSIONS

Our findings do not support a beneficial association of L-carnitine with CVD and its risk factors but suggest potential harm. L-carnitine may also exert a sex-specific role in CAD. Consideration of the possible sex disparity and exploration of the underlying pathways would be worthwhile.

Identifying potential causal effects of age at menopause: a Mendelian randomization phenome-wide association study

Age at natural menopause (ANM) is associated with a range of health-related traits, including bone health, female reproductive cancers, and cardiometabolic health. Our objective was to conduct a Mendelian randomization phenome-wide association study (MR-pheWAS) of ANM. We conducted a hypothesis-free analysis of the genetic risk score (GRS) for ANM with 18,961 health-related traits among 181,279 women in UK Biobank. We also stratified the GRS according to the involvement of SNPs in DNA damage response. We sought to replicate our findings in independent cohorts. We conducted a negative control MR-pheWAS among men. Among women, we identified potential effects of ANM on 221 traits (1.17% of all traits) at a false discovery rate (P value ≤ 5.83 × 10^-4), and 91 (0.48%) potential effects when using Bonferroni threshold (P value ≤ 2.64 × 10^-6). Our findings included 55 traits directly related to ANM (e.g. hormone replacement therapy, gynaecological conditions and menstrual conditions), and liver function, kidney function, lung function, blood-cell composition, breast cancer and bone and cardiometabolic health. Replication analyses confirmed that younger ANM was associated with HbA1c (adjusted mean difference 0.003 mmol/mol; 95% CI 0.001, 0.006 per year decrease in ANM), breast cancer (adjusted OR 0.96; 95% CI 0.95, 0.98), and bone-mineral density (adjusted mean difference - 0.05; 95% CI - 0.07, - 0.03 for lumbar spine). In men, 30 traits were associated with the GRS at a false discovery rate (P value ≤ 5.49 × 10^-6), and 11 potential effects when using Bonferroni threshold (P value ≤ 2.75 × 10^-6). In conclusion, our results suggest that younger ANM has potential causal effects on a range of health-related traits.

A suggested shared aetiology of dementia - a colocalization study

Identification of shared causal genes between dementia and its related clinical outcomes can help understand shared aetiology and multimorbidity surrounding dementia. We performed the HyPrColoc colocalization analysis to detect possible shared causal genes between dementia or Alzheimer's disease (AD) and 5 selected traits: stroke, diabetes, atherosclerosis, cholesterol level, and alcohol consumption within 601 dementia or AD associated genetic regions using summary results of the UK Biobank genome-wide association studies. Functional analysis was performed on the candidate causal genes to explore potential biological pathways. Rs150562240 in the LPIN3 gene was identified as a candidate shared causal variant across dementia, AD and atherosclerosis. Evidence for pairwise colocalization between dementia and stroke, dementia (or AD) and atherosclerosis, and dementia (or AD) and diabetes was found in 2, 6 and 2 genetic regions respectively. Colocalization signals between diabetes and the other 3 non-dementia/AD traits were detected in 5 regions. The colocalization evidence shown in our study suggested shared aetiology between dementia and related diseases such as stroke, atherosclerosis, and diabetes.

Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians

BACKGROUND

Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups.

METHODS

By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: N_case = 21,926, N_control = 342,747; East Asian-based: N_case = 36,614, N_control = 155,150) and PAD (European-based: N_case = 5673, N_control = 359,551; East Asian-based: N_case = 3593, N_control = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD.

RESULTS

We observed a strong genetic correlation (r_g) between T2D and PAD in both Europeans (r_g = 0.51; p-value = 9.34 × 10^-15) and East Asians (r_g = 0.46; p-value = 1.67 × 10^-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians.

CONCLUSIONS

Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.

Temporally ordered associations between type 2 diabetes and brain disorders - a Danish register-based cohort study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is linked with several neurodegenerative and psychiatric disorders, either as a comorbid condition or as a risk factor. We aimed to expand the evidence by examining associations with a broad range of brain disorders (psychiatric and neurological disorders, excluding late-onset neurodegenerative disorders), while also accounting for the temporal order of T2DM and these brain disorders.

METHODS

In a population-based cohort-study of 1,883,198 Danish citizens, born 1955-1984 and followed until end of 2016, we estimated associations between T2DM and 16 brain disorders first diagnosed between childhood and mid-adulthood. We calculated odds ratios (OR) and hazard ratios (HR) with 95% confidence intervals (CI) in temporally ordered analyses (brain disorder diagnosis after T2DM and vice versa), adjusted for sex, age, follow-up, birth year, and parental factors.

RESULTS

A total of 67,660 (3.6%) of the study population were identified as T2DM cases after age 30 and by a mean age of 45 years (SD of 8 years). T2DM was associated with most psychiatric disorders. Strongest associations were seen with other (i.e. non-anorectic) eating disorders (OR [95% CI]: 2.64 [2.36-2.94]) and schizophrenia spectrum disorder (2.73 [2.63-2.84]). Among neurological disorders especially inflammatory brain diseases (1.73 [1.57-1.91]) and epilepsy (1.67 [1.60-1.75]) were associated with T2DM. Most associations remained in both directions in the temporally ordered analyses. For most psychiatric disorders, associations were strongest in females.

CONCLUSIONS

T2DM was associated with several psychiatric and neurological disorders, and most associations were consistently found for both temporal order of disorders. This suggests a shared etiology of T2DM and those brain disorders. This study can form the starting point for studies directed at further elucidating potential causal links between disorders and shared biological mechanisms.

PanCircBase: An online resource for the exploration of circular RNAs in pancreatic islets

Circular RNAs (circRNAs) are a novel class of covalently closed RNA molecules that recently emerged as a critical regulator of gene expression in development and diseases. Recent research has highlighted the importance of novel circRNAs in the biosynthesis and secretion of insulin from β-cells of pancreatic islets. However, all circRNAs expressed in pancreatic islets or β-cells are not readily available in the database. In this study, we analyzed publicly available RNA-sequencing datasets of the pancreatic islets to catalog all circRNAs expressed in pancreatic islets to construct the PanCircBase (https://www.pancircbase.net/) database that provides the following resources: 1) pancreatic islet circRNA annotation details (genomic position, host gene, exon information, splice length, sequence, other database IDs, cross-species conservation), 2) divergent primers for PCR analysis of circRNAs, 3) siRNAs for silencing of target circRNAs, 4) miRNAs associated with circRNAs, 5) possible protein-coding circRNAs and their polypeptides. In summary, this is a comprehensive online resource for exploring circRNA expression and its possible function in pancreatic β-cells.

Genetic control of RNA splicing and its distinct role in complex trait variation

Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method, testing for heterogeneity between isoform-eQTLeffects (THISTLE), with improved power over competing methods. Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic (n = 2,865) and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10⁻⁸, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.

A powerful method for splicing quantitative trait loci (sQTL) mapping, THISTLE, is presented and applied to a collection of 2,865 brain samples. Integration with GWAS identifies 244 genes associated via cis-sQTLs, of which 61% were not identified using expression QTLs.

eQTLs as causal instruments for the reconstruction of hormone linked gene networks

Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.

Association of cardiovascular risk factors and lifestyle behaviors with aortic aneurysm: A Mendelian randomization study

Objective: To examine the causality between hypertension, diabetes, other cardiovascular risk factors, lifestyle behaviors, and the aortic aneurysm among patients of European ancestry.

Methods: We performed two-sample Mendelian randomization (MR) analysis to investigate the causality of 12 modifiable risk factors with aortic aneurysm, including hypertension, body mass index (BMI), waist–hip ratio (WHR), diabetes, tobacco smoking, alcohol and coffee consumption, physical activity, and sleep duration. Genome-wide significant genetic instruments (p < 5 × 10^–8) for risk factors were extracted from European-descent genome-wide association studies, whereas aortic aneurysm genetic instruments were selected from the UK Biobank and FinnGen cohort. The inverse-variance weighted MR was used as the main analysis, and MR-Egger (MRE), weighted median MR, MR pleiotropy residual sum and outlier, and Phenoscanner searching were performed as sensitivity analyses. Furthermore, we calculated MRE intercept to detect pleiotropy and Cochran’s Q statistics to assess heterogeneity and conducted bidirectional MR and MR Steiger tests to exclude the possibility of reverse causality.

Results: We observed significantly higher risks for the aortic aneurysm in hypertension [pooled OR: 4.30 (95% CI 2.84–6.52)], BMI [OR: 1.58 (95% CI 1.37–1.81)], WHR [OR: 1.51 (95% CI 1.21–1.88)], WHR adjusted for BMI (WHRadjBMI) [OR: 1.35 (95% CI 1.12–1.63)], age of smoking initiation [OR: 1.63 (95% CI 1.18–2.26)], and tobacco use (initiation, cessation, and heaviness) [OR: 2.88 (95% CI 1.85–2.26)]. In sensitivity analysis, the causal effects of hypertension, BMI, WHRadjBMI, and tobacco use (initiation, cessation, and heaviness) remained robust.

Conclusion: There was a positive causal relationship between hypertension, BMI, WHR, and WHRadjBMI and aortic aneurysm.

Association of Polygenic Variants with Type 2 Diabetes Risk and Their Interaction with Lifestyles in Asians

Over the last several decades, there has been a considerable growth in type 2 diabetes (T2DM) in Asians. A pathophysiological mechanism in Asian T2DM is closely linked to low insulin secretion, β-cell mass, and inability to compensate for insulin resistance. We hypothesized that genetic variants associated with lower β-cell mass and function and their combination with unhealthy lifestyle factors significantly raise T2DM risk among Asians. This hypothesis was explored with participants aged over 40. Participants were categorized into T2DM (case; n = 5383) and control (n = 53,318) groups. The genetic variants associated with a higher risk of T2DM were selected from a genome-wide association study in a city hospital-based cohort, and they were confirmed with a replicate study in Ansan/Ansung plus rural cohorts. The interacted genetic variants were identified with generalized multifactor dimensionality reduction analysis, and the polygenic risk score (PRS)-nutrient interactions were examined. The 8-SNP model was positively associated with T2DM risk by about 10 times, exhibiting a higher association than the 20-SNP model, including all T2DM-linked SNPs with p < 5 × 10−6. The SNPs in the models were primarily involved in pancreatic β-cell growth and survival. The PRS of the 8-SNP model interacted with three lifestyle factors: energy intake based on the estimated energy requirement (EER), Western-style diet (WSD), and smoking status. Fasting serum glucose concentrations were much higher in the participants with High-PRS in rather low EER intake and high-WSD compared to the High-EER and Low-WSD, respectively. They were shown to be higher in the participants with High-PRS in smokers than in non-smokers. In conclusion, the genetic impact of T2DM risk was mainly involved with regulating pancreatic β-cell mass and function, and the PRS interacted with lifestyles. These results highlight the interaction between genetic impacts and lifestyles in precision nutrition.

Mendelian randomization analyses of 23 known and suspected risk factors and biomarkers for breast cancer overall and by molecular subtypes

Many risk factors have been identified for breast cancer. The potential causality for some of them remains uncertain, and few studies have comprehensively investigated these associations by molecular subtypes. We performed a two-sample Mendelian randomization (MR) study to evaluate potential causal associations of 23 known and suspected risk factors and biomarkers with breast cancer risk overall and by molecular subtypes using data from the Breast Cancer Association Consortium. The inverse-variance weighted method was used to estimate odds ratios (OR) and 95% confidence interval (CI) for association of each trait with breast cancer risk. Significant associations with breast cancer risk were found for 15 traits, including age at menarche, age at menopause, body mass index, waist-to-hip ratio, height, physical activity, cigarette smoking, sleep duration, and morning-preference chronotype, and six blood biomarkers (estrogens, insulin-like growth factor-1, sex hormone-binding globulin [SHBG], telomere length, HDL-cholesterol and fasting insulin). Noticeably, an increased circulating SHBG was associated with a reduced risk of estrogen receptor (ER)-positive cancer (OR = 0.83, 95% CI: 0.73-0.94), but an elevated risk of ER-negative (OR = 1.12, 95% CI: 0.93-1.36) and triple negative cancer (OR = 1.19, 95% CI: 0.92-1.54) (P_{heterogeneity}  = 0.01). Fasting insulin was most strongly associated with an increased risk of HER2-negative cancer (OR = 1.94, 95% CI: 1.18-3.20), but a reduced risk of HER2-enriched cancer (OR = 0.46, 95% CI: 0.26-0.81) (P_{heterogeneity}  = 0.006). Results from sensitivity analyses using MR-Egger and MR-PRESSO were generally consistent. Our study provides strong evidence supporting potential causal associations of several risk factors for breast cancer and suggests potential heterogeneous associations of SHBG and fasting insulin levels with subtypes of breast cancer.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Analysis of circRNA-mRNA expression profiles and functional enrichment in diabetes mellitus based on high throughput sequencing

To study the pathogenesis of diabetes mellitus (DM) and identify new biomarkers, high-throughput RNA sequencing provides a technical means to explore the regulatory network of MD gene expression. To better elucidate the genetic basis of DM, we analysed the circRNA and mRNA expression profiles in serum samples from diabetic patients. The circRNAs and mRNAs with abnormal expression in the DM group and non-diabetic group (NDM) were classified by RNA sequencing and differential expression analysis. The circRNA-miRNA-mRNA regulatory network reveals the mechanism by which competitive endogenous RNAs (ceRNAs) regulate gene expression. The biological functions and interactions of circRNA and mRNA were analysed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential expression analysis showed that 441 circRNAs (366 up-regulated, 75 down-regulated) and 683 mRNAs (354 up-regulated, 329 down-regulated) were significantly differentially expressed in the DM group compared with the NDM group. Screening of the differential genes at the nodes of the interaction network showed that a single circRNA could interact with multiple miRNAs and then jointly regulate more mRNAs. In addition, the expressions of circRNA CNOT6 and AXIN1 as well as mRNA STAT3, MYD88, and B2M were associated with the progression of diabetes. Enrichment pathway analysis indicated that differentially expressed circRNA and mRNA may participate in Nod-like receptor signalling pathway, insulin signalling pathway, sphinolipid metabolism pathway, and ribosome pathway, and play a role in the pathogenesis of diabetes. This study provides a theoretical basis for elucidating the molecular mechanism of DM occurrence and development at circRNA and mRNA levels.

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

Integrative analysis of the high-resolution baboon diurnal transcriptome, provides insights into the effect of circadian rhythm on the whole-body primate gene network.

Uncovering signals of positive selection in Peruvian populations from three ecological regions

Perú hosts extremely diverse ecosystems which can be broadly classified into three major ecoregions: the Pacific desert coast, the Andean highlands, and the Amazon rainforest. Since its initial peopling approximately 12,000 years ago, the populations inhabiting such ecoregions might have differentially adapted to their contrasting environmental pressures. Previous studies have described several candidate genes underlying adaptation to hypobaric hypoxia among Andean highlanders. However, the adaptive genetic diversity of coastal and rainforest populations has been less studied. Here, we gathered genome-wide SNP-array data from 286 Peruvians living across the three ecoregions and analysed signals of recent positive selection through population differentiation and haplotype-based selection scans. Among highland populations, we identify candidate genes related to cardiovascular function (TLL1, DUSP27, TBX5, PLXNA4, SGCD), to the Hypoxia-Inducible Factor pathway (TGFA, APIP), to skin pigmentation (MITF), as well as to glucose (GLIS3) and glycogen metabolism (PPP1R3C, GANC). In contrast, most signatures of adaptation in coastal and rainforest populations comprise candidate genes related to the immune system (including SIGLEC8, TRIM21, CD44 and ICAM1 in the coast; CBLB and PRDM1 in rainforest and the BRD2- HLA-DOA- HLA-DPA1 region in both), possibly as a result of strong pathogen-driven selection. This study identifies candidate genes related to human adaptation to the diverse environments of South America.

Genetic Association between Different Metabolic Variants in APOA5 and PLIN1 in Type 2 Diabetes Mellitus among the Western Saudi Population: Case-Control Study

Type 2 diabetes mellitus (T2DM) is a multifactorial disease with a high global incidence. Hypertriglyceridemia is a major risk factor for both cardiovascular disease and T2DM. In this study, we determined the allele and genotype frequencies of apolipoprotein A5 (APOA5) single nucleotide polymorphism (SNP) rs662799 and perilipin 1 (PLIN1) SNPs rs894160, rs6496589, and rs1052700 and evaluated their association with T2DM risk in western Saudis. Only rs6496589 was found to be significantly associated with T2DM risk. We determined the risk allele for each SNP based on relative risk, and found that the G allele of rs662799, T allele of rs894160, G allele of r6496589, and T allele of rs1052700 correlated with T2DM risk. The effect of each SNP on T2DM risk and five of its clinical phenotypes was explored using multiple logistic regression. We found significant correlations between the C/G and G/G genotypes of rs6496589 and T2DM risk in the unadjusted model, whereas G/G was the only genotype that correlated with the risk of T2DM in the adjusted model. There was no significant correlation between rs662799, rs894160, and rs1052700 genotypes and T2DM risk. In conclusion, we have identified novel risk alleles and genotypes that contribute to genetic risk for T2DM in the western Saudi population.

Unravelling the Distinct Effects of Systolic and Diastolic Blood Pressure Using Mendelian Randomisation

A true discrepancy between the effect of systolic blood pressure (SBP) and diastolic blood pressure (DBP) on cardiovascular (CV) outcomes remains unclear. This study performed two-sample Mendelian randomization (MR) using genetic instruments that exclusively predict SBP, DBP or both to dissect the independent effect of SBP and DBP on a range of CV outcomes. Genetic predisposition to higher SBP and DBP was associated with increased risk of coronary artery disease (CAD), myocardial infarction (MI), stroke, heart failure (HF), atrial fibrillation (AF), chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM). Genetically proxied SBP exclusively was associated with CAD (OR 1.18, 95% CI: 1.03–1.36, per 10 mmHg), stroke (1.44[1.28–1.62]), ischemic stroke (1.49[1.30–1.69]), HF (1.41[1.20–1.65]), AF (1.28[1.15–1.43]), and T2DM (1.2[1.13–1.46]). Genetically proxied DBP exclusively was associated with stroke (1.21[1.06–1.37], per 5 mmHg), ischemic stroke (1.24[1.09–1.41]), stroke small-vessel (1.35[1.10–1.65]) and CAD (1.19[1.00–1.41]). Multivariable MR using exclusive SBP and DBP instruments showed the predominant effect of SBP on CAD (1.23[1.05–1.44], per 10 mmHg), stroke (1.39[1.20–1.60]), ischemic stroke (1.44[1.25–1.67]), HF (1.42[1.18–1.71]), AF (1.26[1.10–1.43]) and T2DM (1.31[1.14–1.52]). The discrepancy between effects of SBP and DBP on outcomes warrants further studies on underpinning mechanisms which may be amenable to therapeutic targeting.

Oxidative Stress in Type 2 Diabetes: The Case for Future Pediatric Redoxomics Studies

Considerable evidence supports the role of oxidative stress in adult type 2 diabetes (T2D). Due to increasing rates of pediatric obesity, lack of physical activity, and consumption of excess food calories, it is projected that the number of children living with insulin resistance, prediabetes, and T2D will markedly increase with enormous worldwide economic costs. Understanding the factors contributing to oxidative stress and T2D risk may help develop optimal early intervention strategies. Evidence suggests that oxidative stress, triggered by excess dietary fat consumption, causes excess mitochondrial hydrogen peroxide emission in skeletal muscle, alters redox status, and promotes insulin resistance leading to T2D. The pathophysiological events arising from excess calorie-induced mitochondrial reactive oxygen species production are complex and not yet investigated in children. Systems medicine is an integrative approach leveraging conventional medical information and environmental factors with data obtained from “omics” technologies such as genomics, proteomics, and metabolomics. In adults with T2D, systems medicine shows promise in risk assessment and predicting drug response. Redoxomics is a branch of systems medicine focusing on “omics” data related to redox status. Systems medicine with a complementary emphasis on redoxomics can potentially optimize future healthcare strategies for adults and children with T2D.

Trends in insulin resistance: insights into mechanisms and therapeutic strategy

The centenary of insulin discovery represents an important opportunity to transform diabetes from a fatal diagnosis into a medically manageable chronic condition. Insulin is a key peptide hormone and mediates the systemic glucose metabolism in different tissues. Insulin resistance (IR) is a disordered biological response for insulin stimulation through the disruption of different molecular pathways in target tissues. Acquired conditions and genetic factors have been implicated in IR. Recent genetic and biochemical studies suggest that the dysregulated metabolic mediators released by adipose tissue including adipokines, cytokines, chemokines, excess lipids and toxic lipid metabolites promote IR in other tissues. IR is associated with several groups of abnormal syndromes that include obesity, diabetes, metabolic dysfunction-associated fatty liver disease (MAFLD), cardiovascular disease, polycystic ovary syndrome (PCOS), and other abnormalities. Although no medication is specifically approved to treat IR, we summarized the lifestyle changes and pharmacological medications that have been used as efficient intervention to improve insulin sensitivity. Ultimately, the systematic discussion of complex mechanism will help to identify potential new targets and treat the closely associated metabolic syndrome of IR.

Cardiorespiratory fitness, genetic susceptibility, inflammation and risk of incident type 2 diabetes: A population-based longitudinal study

OBJECTIVE

To examine whether the association between cardiorespiratory fitness (CRF) and type 2 diabetes (T2D) is modified by genetic susceptibility and inflammation.

PARTICIPANTS

The prospective study included 57,185 participants (40-70 years) who were free from T2D and received the CRF assessment at enrollment (2006-2010) in the UK biobank. CRF was examined through a submaximal cycle ergometer test and expressed in metabolic equivalent of tasks (METs), genetic susceptibility was quantified using a genetic risk score, and inflammation was assessed according to the concentration of C-reactive protein. All these three factors were categorized into tertiles.

RESULTS

During a median follow-up of 10.4 years, 5477 (7.0%) cases of T2D were ascertained. CRF was inversely associated with the risk of T2D in a dose-response manner. The hazard ratio (HR) was 0.85 (95% confidence interval [CI]: 0.79-0.92) per 1 MET increment of CRF. There was a significant interaction between CRF and genetic susceptibility to T2D in relation to the risk of T2D (P for interaction = 0.03). Compared with participants with high CRF and low genetic susceptibility, the HR was 4.98 (95% CI: 3.17-7.82) for those with low CRF and high genetic susceptibility. A similar pattern was observed in participants with low CRF and high inflammation compared with those who had high CRF and low inflammation (HR = 2.53; 95% CI: 1.83-3.48), though the interaction between CRF and inflammation did not reach statistical significance. T2D risk declined progressively with increased CRF among different inflammation categories.

CONCLUSION

Our study reveals that genetic susceptibility may modify the association between CRF and T2D, highlighting that risk of T2D associated with genetics could benefit most from interventions on improving CRF.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Mendelian randomization analysis of 37 clinical factors and coronary artery disease in East Asian and European populations

BACKGROUND

Coronary artery disease (CAD) remains the leading cause of mortality worldwide despite enormous efforts devoted to its prevention and treatment. While many genetic loci have been identified to associate with CAD, the intermediate causal risk factors and etiology have not been fully understood. This study assesses the causal effects of 37 heritable clinical factors on CAD in East Asian and European populations.

METHODS

We collected genome-wide association summary statistics of 37 clinical factors from the Biobank Japan (42,793 to 191,764 participants) and the UK Biobank (314,658 to 442,817 participants), paired with summary statistics of CAD from East Asians (29,319 cases and 183,134 controls) and Europeans (91,753 cases and 311,344 controls). These clinical factors covered 12 cardiometabolic traits, 13 hematological indices, 7 hepatological and 3 renal function indices, and 2 serum electrolyte indices. We performed univariable and multivariable Mendelian randomization (MR) analyses in East Asians and Europeans separately, followed by meta-analysis.

RESULTS

Univariable MR analyses identified reliable causal evidence (P < 0.05/37) of 10 cardiometabolic traits (height, body mass index [BMI], blood pressure, glycemic and lipid traits) and 4 other clinical factors related to red blood cells (red blood cell count [RBC], hemoglobin, hematocrit) and uric acid (UA). Interestingly, while generally consistent, we identified population heterogeneity in the causal effects of BMI and UA, with higher effect sizes in East Asians than those in Europeans. After adjusting for cardiometabolic factors in multivariable MR analysis, red blood cell traits (RBC, meta-analysis odds ratio 1.07 per standard deviation increase, 95% confidence interval 1.02-1.13; hemoglobin, 1.10, 1.03-1.16; hematocrit, 1.10, 1.04-1.17) remained significant (P < 0.05), while UA showed an independent causal effect in East Asians only (1.12, 1.06-1.19, P = 3.26×10-5).

CONCLUSIONS

We confirmed the causal effects of 10 cardiometabolic traits on CAD and identified causal risk effects of RBC, hemoglobin, hematocrit, and UA independent of traditional cardiometabolic factors. We found no causal effects for 23 clinical factors, despite their reported epidemiological associations. Our findings suggest the physiology of red blood cells and the level of UA as potential intervention targets for the prevention of CAD.

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders

The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.

Genome-Wide Association Study Identifies Genetic Loci Associated With Fat Cell Number and Overlap With Genetic Risk Loci for Type 2 Diabetes

Interindividual differences in generation of new fat cells determine body fat and type 2 diabetes risk. In the GENetics of Adipocyte Lipolysis (GENiAL) cohort, which consists of participants who have undergone abdominal adipose biopsy, we performed a genome-wide association study (GWAS) of fat cell number (n = 896). Candidate genes from the genetic study were knocked down by siRNA in human adipose-derived stem cells. We report 318 single nucleotide polymorphisms (SNPs) and 17 genetic loci displaying suggestive (P < 1 × 10-5) association with fat cell number. Two loci pass threshold for GWAS significance, on chromosomes 2 (lead SNP rs149660479-G) and 7 (rs147389390-deletion). We filtered for fat cell number-associated SNPs (P < 1.00 × 10-5) using evidence of genotype-specific expression. Where this was observed we selected genes for follow-up investigation and hereby identified SPATS2L and KCTD18 as regulators of cell proliferation consistent with the genetic data. Furthermore, 30 reported type 2 diabetes-associated SNPs displayed nominal and consistent associations with fat cell number. In functional follow-up of candidate genes, RPL8, HSD17B12, and PEPD were identified as displaying effects on cell proliferation consistent with genetic association and gene expression findings. In conclusion, findings presented herein identify SPATS2L, KCTD18, RPL8, HSD17B12, and PEPD of potential importance in controlling fat cell numbers (plasticity), the size of body fat, and diabetes risk.

Genetic Evidence for a Causal Relationship between Hyperlipidemia and Type 2 Diabetes in Mice

Dyslipidemia is considered a risk factor for type 2 diabetes (T2D), yet studies with statins and candidate genes suggest that circulating lipids may protect against T2D development. Apoe-null (Apoe^-/-) mouse strains develop spontaneous dyslipidemia and exhibit a wide variation in susceptibility to diet-induced T2D. We thus used Apoe^-/- mice to elucidate phenotypic and genetic relationships of circulating lipids with T2D. A male F2 cohort was generated from an intercross between LP/J and BALB/cJ Apoe^-/- mice and fed 12 weeks of a Western diet. Fasting, non-fasting plasma glucose, and lipid levels were measured and genotyping was performed using miniMUGA arrays. We uncovered a major QTL near 60 Mb on chromosome 15, Nhdlq18, which affected non-HDL cholesterol and triglyceride levels under both fasting and non-fasting states. This QTL was coincident with Bglu20, a QTL that modulates fasting and non-fasting glucose levels. The plasma levels of non-HDL cholesterol and triglycerides were closely correlated with the plasma glucose levels in F2 mice. Bglu20 disappeared after adjustment for non-HDL cholesterol or triglycerides. These results demonstrate a causative role for dyslipidemia in T2D development in mice.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

Diabetes risk loci-associated pathways are shared across metabolic tissues

Aims/hypothesis

Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants.

Methods

In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms.

Results

One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated.

Conclusion

Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08587-5.

A Polygenic Score for Type 2 Diabetes Improves Risk Stratification Beyond Current Clinical Screening Factors in an Ancestrally Diverse Sample

A substantial proportion of the adult United States population with type 2 diabetes (T2D) are undiagnosed, calling into question the comprehensiveness of current screening practices, which primarily rely on age, family history, and body mass index (BMI). We hypothesized that a polygenic score (PGS) may serve as a complementary tool to identify high-risk individuals. The T2D polygenic score maintained predictive utility after adjusting for family history and combining genetics with family history led to even more improved disease risk prediction. We observed that the PGS was meaningfully related to age of onset with implications for screening practices: there was a linear and statistically significant relationship between the PGS and T2D onset (-1.3 years per standard deviation of the PGS). Evaluation of U.S. Preventive Task Force and a simplified version of American Diabetes Association screening guidelines showed that addition of a screening criterion for those above the 90th percentile of the PGS provided a small increase the sensitivity of the screening algorithm. Among T2D-negative individuals, the T2D PGS was associated with prediabetes, where each standard deviation increase of the PGS was associated with a 23% increase in the odds of prediabetes diagnosis. Additionally, each standard deviation increase in the PGS corresponded to a 43% increase in the odds of incident T2D at one-year follow-up. Using complications and forms of clinical intervention (i.e., lifestyle modification, metformin treatment, or insulin treatment) as proxies for advanced illness we also found statistically significant associations between the T2D PGS and insulin treatment and diabetic neuropathy. Importantly, we were able to replicate many findings in a Hispanic/Latino cohort from our database, highlighting the value of the T2D PGS as a clinical tool for individuals with ancestry other than European. In this group, the T2D PGS provided additional disease risk information beyond that offered by traditional screening methodologies. The T2D PGS also had predictive value for the age of onset and for prediabetes among T2D-negative Hispanic/Latino participants. These findings strengthen the notion that a T2D PGS could play a role in the clinical setting across multiple ancestries, potentially improving T2D screening practices, risk stratification, and disease management.

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.

Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: A population-based cohort study

BACKGROUND

Type 2 diabetes (T2D) is highly prevalent in British South Asians, yet they are underrepresented in research. Genes & Health (G&H) is a large, population study of British Pakistanis and Bangladeshis (BPB) comprising genomic and routine health data. We assessed the extent to which genetic risk for T2D is shared between BPB and European populations (EUR). We then investigated whether the integration of a polygenic risk score (PRS) for T2D with an existing risk tool (QDiabetes) could improve prediction of incident disease and the characterisation of disease subtypes.

METHODS AND FINDINGS

In this observational cohort study, we assessed whether common genetic loci associated with T2D in EUR individuals were replicated in 22,490 BPB individuals in G&H. We replicated fewer loci in G&H (n = 76/338, 22%) than would be expected given power if all EUR-ascertained loci were transferable (n = 101, 30%; p = 0.001). Of the 27 transferable loci that were powered to interrogate this, only 9 showed evidence of shared causal variants. We constructed a T2D PRS and combined it with a clinical risk instrument (QDiabetes) in a novel, integrated risk tool (IRT) to assess risk of incident diabetes. To assess model performance, we compared categorical net reclassification index (NRI) versus QDiabetes alone. In 13,648 patients free from T2D followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval (CI): 2.0% to 4.4%). IRT performed best in reclassification of individuals aged less than 40 years deemed low risk by QDiabetes alone (NRI 5.6%, 95% CI 3.6% to 7.6%), who tended to be free from comorbidities and slim. After adjustment for QDiabetes score, PRS was independently associated with progression to T2D after gestational diabetes (hazard ratio (HR) per SD of PRS 1.23, 95% CI 1.05 to 1.42, p = 0.028). Using cluster analysis of clinical features at diabetes diagnosis, we replicated previously reported disease subgroups, including Mild Age-Related, Mild Obesity-related, and Insulin-Resistant Diabetes, and showed that PRS distribution differs between subgroups (p = 0.002). Integrating PRS in this cluster analysis revealed a Probable Severe Insulin Deficient Diabetes (pSIDD) subgroup, despite the absence of clinical measures of insulin secretion or resistance. We also observed differences in rates of progression to micro- and macrovascular complications between subgroups after adjustment for confounders. Study limitations include the absence of an external replication cohort and the potential biases arising from missing or incorrect routine health data.

CONCLUSIONS

Our analysis of the transferability of T2D loci between EUR and BPB indicates the need for larger, multiancestry studies to better characterise the genetic contribution to disease and its varied aetiology. We show that a T2D PRS optimised for this high-risk BPB population has potential clinical application in BPB, improving the identification of T2D risk (especially in the young) on top of an established clinical risk algorithm and aiding identification of subgroups at diagnosis, which may help future efforts to stratify care and treatment of the disease.

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study

BACKGROUND AND PURPOSE

To explore the causal relationships of elements of the exposome with ischemic stroke and its subtypes at the omics level and to provide evidence for stroke prevention.

METHODS

We conducted a Mendelian randomization study between exposure and any ischemic stroke (AIS) and its subtypes (large-artery atherosclerotic disease [LAD], cardioembolic stroke [CE], and small vessel disease [SVD]). The exposure dataset was the UK Biobank involving 361,194 subjects, and the outcome dataset was the MEGASTROKE consortium including 52,000 participants.

RESULTS

We found that higher blood pressure (BP) (systolic BP: odds ratio [OR], 1.02; 95% confidence interval [CI], 1.01 to 1.04; diastolic BP: OR, 1.03; 95% CI, 1.01 to 1.05; pulse pressure: OR, 1.03; 95% CI, 1.00 to 1.06), atrial fibrillation (OR, 1.18; 95% CI, 1.13 to 1.25), and diabetes (OR, 1.13; 95% CI, 1.07 to 1.18) were significantly associated with ischemic stroke. Importantly, higher education (OR, 0.69; 95% CI, 0.60 to 0.79) decreased the risk of ischemic stroke. Higher systolic BP (OR, 1.06; 95% CI, 1.02 to 1.10), pulse pressure (OR, 1.08; 95% CI, 1.02 to 1.14), diabetes (OR, 1.28; 95% CI, 1.13 to 1.45), and coronary artery disease (OR, 1.58; 95% CI, 1.25 to 2.00) could cause LAD. Atrial fibrillation could cause CE (OR, 1.90; 95% CI, 1.71 to 2.11). For SVD, higher systolic BP (OR, 1.04; 95% CI, 1.00 to 1.07), diastolic BP (OR, 1.06; 95% CI, 1.01 to 1.12), and diabetes (OR, 1.22; 95% CI, 1.10 to 1.36) were causal factors.

CONCLUSIONS

The study revealed elements of the exposome causally linked to ischemic stroke and its subtypes, including conventional causal risk factors and novel protective factors such as higher education.

First genome-wide association study of 99 body measures derived from 3-dimensional body scans

Body height, body mass index, hip and waist circumference are important risk factors or outcome variables in clinical and epidemiological research with complex underlying genetics. However, these classical anthropometric traits represent only a very limited view on the human body and other traits with potentially higher functional specificity are not yet studied to a larger extent. Participants of LIFE-Adult were assessed by three-dimensional body scanner VITUS XXL determining 99 high-quality anthropometric traits in parallel. Genotyping was performed by Axiom Genome-Wide CEU 1 Array Plate microarray technology and imputation was done using 1000 Genomes phase 3 reference panel. Combined phenotype and genetic information are available for a total of 7,562 participants. Largest heritabilities were estimated for height traits (maximum heritability with h² = 44% for neck height) and 61 traits achieved values larger than 20%. By genome-wide analyses, we identified 16 loci associated with at least one of the 99 traits. Ten of these loci were not described for association with classical anthropometric traits so far. The strongest novel association was observed for 7p14.3 (rs11979006, P = 2.12 × 10⁻⁹) for the trait Back Width with ZNRF2 as the most plausible candidate gene. Loci established for association with classical anthropometric traits were subjected to anthropometric phenome-wide association analysis. From the reported 709 loci, 211 are co-associated with body scanner traits (enrichment: OR = 1.96, P = 1.08 × 10⁻⁶¹). We conclude that genetics of 3D laser-based anthropometry is promising to identify novel loci and to improve the functional understanding of established ones.