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

Sjögren syndrome/scleroderma autoantigen 1 is a direct Tankyrase binding partner in cancer cells

Sjögren syndrome/scleroderma autoantigen 1 (SSSCA1) was first described as an auto-antigen over-expressed in Sjögren’s syndrome and in scleroderma patients. SSSCA1 has been linked to mitosis and centromere association and as a potential marker candidate in diverse solid cancers. Here we characterize SSSCA1 for the first time, to our knowledge, at the molecular, structural and subcellular level. We have determined the crystal structure of a zinc finger fold, a zinc ribbon domain type 2 (ZNRD2), at 2.3 Å resolution. We show that the C-terminal domain serves a dual function as it both behaves as the interaction site to Tankyrase 1 (TNKS1) and as a nuclear export signal. We identify TNKS1 as a direct binding partner of SSSCA1, map the binding site to TNKS1 ankyrin repeat cluster 2 (ARC2) and thus define a new binding sequence. We experimentally verify and map a new nuclear export signal sequence in SSSCA1.

Perdreau-Dahl et al. systematically characterise Sjögren syndrome/scleroderma autoantigen 1 at the molecular, structural and subcellular level. They show that the C-terminal domain serves a dual function as it both behaves as the interaction site to Tankyrase 1 and as a nuclear export signal.

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Genetic risk, adherence to a healthy lifestyle, and type 2 diabetes risk among 550,000 Chinese adults: results from 2 independent Asian cohorts

BACKGROUND

Whether genetic susceptibility to type 2 diabetes is modified by a healthy lifestyle among Chinese remains unknown.

OBJECTIVES

The aim of the study was to determine whether genetic risk and adherence to a healthy lifestyle contribute independently to the risk of developing type 2 diabetes.

METHODS

We defined a lifestyle score using BMI, alcohol intake, smoking, physical activities, and diets in 461,030 participants from the China Kadoorie Biobank and 38,434 participants from the Singapore Chinese Health Study. A genetic risk score was constructed based on type 2 diabetes loci among 100,175 and 16,172 participants in each cohort, respectively. A Cox proportional-hazards model was used to estimate the interaction between genetic and lifestyle factors on the risk of type 2 diabetes.

RESULTS

In 2 independent Asian cohorts, we consistently found a healthy lifestyle (the bottom quintile of lifestyle score) was associated with a substantially lower risk of type 2 diabetes than an unhealthy lifestyle (the top quintile of lifestyle score) regardless of genetic risk. In those at a high genetic risk, the risk of type 2 diabetes was 57% lower among participants with a healthy lifestyle than among those with an unhealthy lifestyle in the pooled cohorts. Among participants at high genetic risk, the standardized 10-y incidence of type 2 diabetes was 7.11% in those with an unhealthy lifestyle vs. 2.45% in those with a healthy lifestyle.

CONCLUSIONS

In 2 independent cohorts involving 558,302 Chinese participants, we did not observe an interaction between genetics and lifestyle with type 2 diabetes risk, but our findings provide replicable evidence to show lifestyle factors and genetic factors were independently associated with the risk of type 2 diabetes. Within any genetic risk category, a healthy lifestyle was associated with a significantly lower risk of type 2 diabetes among the Chinese population.

Inadequate β-cell mass is essential for the pathogenesis of type 2 diabetes

For patients with type 1 diabetes, it is accepted among the scientific community that there is a marked reduction in β-cell mass; however, with type 2 diabetes, there is disagreement as to whether this reduction in mass occurs in every case. Some have argued that β-cell mass in some patients with type 2 diabetes is normal and that the cause of the hyperglycaemia in these patients is a functional abnormality of insulin secretion. In this Personal View, we argue that a deficient β-cell mass is essential for the development of type 2 diabetes. The main point is that there are enormous (≥10 fold) variations in insulin sensitivity and insulin secretion in the general population, with a very close correlation between these two factors for any individual. Although β-cell mass cannot be accurately measured in living patients, it is highly likely that it too is highly correlated with insulin sensitivity and secretion. Thus, our argument is that a person with type 2 diabetes can have a β-cell mass that is the same as a person without type 2 diabetes, but because they are insulin resistant, the mass is inadequate and responsible for their diabetes. Because the abnormal insulin secretion of diabetes is caused by dysglycaemia and can be largely reversed with glycaemic control, it is a less serious problem than the reduction in β-cell mass, which is far more difficult to restore.

Genome-Wide Selection Scan in an Arabian Peninsula Population Identifies a TNKS Haplotype Linked to Metabolic Traits and Hypertension

Despite the extreme and varying environmental conditions prevalent in the Arabian Peninsula, it has experienced several waves of human migrations following the out-of-Africa diaspora. Eventually, the inhabitants of the peninsula region adapted to the hot and dry environment. The adaptation and natural selection that shaped the extant human populations of the Arabian Peninsula region have been scarcely studied. In an attempt to explore natural selection in the region, we analyzed 662,750 variants in 583 Kuwaiti individuals. We searched for regions in the genome that display signatures of positive selection in the Kuwaiti population using an integrative approach in a conservative manner. We highlight a haplotype overlapping TNKS that showed strong signals of positive selection based on the results of the multiple selection tests conducted (integrated Haplotype Score, Cross Population Extended Haplotype Homozygosity, Population Branch Statistics, and log-likelihood ratio scores). Notably, the TNKS haplotype under selection potentially conferred a fitness advantage to the Kuwaiti ancestors for surviving in the harsh environment while posing a major health risk to present-day Kuwaitis.

Hierarchical structural component model for pathway analysis of common variants

BACKGROUND

Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways.

METHODS

We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests.

RESULTS

Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance.

CONCLUSIONS

Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

Pharmacological enrichment of polygenic risk for precision medicine in complex disorders

Individuals with complex disorders typically have a heritable burden of common variation that can be expressed as a polygenic risk score (PRS). While PRS has some predictive utility, it lacks the molecular specificity to be directly informative for clinical interventions. We therefore sought to develop a framework to quantify an individual’s common variant enrichment in clinically actionable systems responsive to existing drugs. This was achieved with a metric designated the pharmagenic enrichment score (PES), which we demonstrate for individual SNP profiles in a cohort of cases with schizophrenia. A large proportion of these had elevated PES in one or more of eight clinically actionable gene-sets enriched with schizophrenia associated common variation. Notable candidates targeting these pathways included vitamins, antioxidants, insulin modulating agents, and cholinergic drugs. Interestingly, elevated PES was also observed in individuals with otherwise low common variant burden. The biological saliency of PES profiles were observed directly through their impact on gene expression in a subset of the cohort with matched transcriptomic data, supporting our assertion that this gene-set orientated approach could integrate an individual’s common variant risk to inform personalised interventions, including drug repositioning, for complex disorders such as schizophrenia.

Effects of Adiponectin on T2DM and Glucose Homeostasis: A Mendelian Randomization Study

PURPOSE

The associations of adiponectin with type 2 diabetes mellitus (T2DM), glucose homeostasis (including β-cell function index (HOMA-β), insulin resistance (HOMA-IR), fasting insulin (FI) and fasting glucose (FG)) have reported in epidemiological studies. However, the previous observational studies are prone to biases, such as reverse causation and residual confounding factors. Herein, a Mendelian Randomization (MR) study was conducted to determine whether causal effects exist among them.

MATERIALS AND AND METHODS

Two-sample MR analyses and multiple sensitivity analyses were performed using the summary data from the ADIPOGen consortium, MAGIC Consortium, and a meta-analysis of GWAS with a considerable sample of T2DM (62,892 cases and 596,424 controls of European ancestry). We got eight valid genetic variants to predict the causal effect among adiponectin and T2DM and glucose homeostasis after excluding the probable invalid or pleiotropic variants.

RESULTS

Adiponectin was not associated with T2DM (odds ratio (OR) = 1.004; 95% confidence interval (CI): 0.740, 1.363) when using MR Egger after removing the invalid SNPs, and the results were consistent when using the other four methods. Similar results existed among adiponectin and HOMA-β, HOMA-IR, FI, FG.

CONCLUSION

Our MR study revealed that adiponectin had no causal effect on T2DM and glucose homeostasis and that the associations among them in observational studies may be due to confounding factors.

Glucose-Dependent Insulinotropic Polypeptide Receptor Therapies for the Treatment of Obesity, Do Agonists = Antagonists?

Glucose-dependent insulinotropic polypeptide receptor (GIPR) is associated with obesity in human genome-wide association studies. Similarly, mouse genetic studies indicate that loss of function alleles and glucose-dependent insulinotropic polypeptide overexpression both protect from high-fat diet-induced weight gain. Together, these data provide compelling evidence to develop therapies targeting GIPR for the treatment of obesity. Further, both antagonists and agonists alone prevent weight gain, but result in remarkable weight loss when codosed or molecularly combined with glucagon-like peptide-1 analogs preclinically. Here, we review the current literature on GIPR, including biology, human and mouse genetics, and pharmacology of both agonists and antagonists, discussing the similarities and differences between the 2 approaches. Despite opposite approaches being investigated preclinically and clinically, there may be viability of both agonists and antagonists for the treatment of obesity, and we expect this area to continue to evolve with new clinical data and molecular and pharmacological analyses of GIPR function.

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

Associations between potentially functional CORIN SNPs and serum corin levels in the Chinese Han population

Background

Corin is an important convertase involved in the natriuretic peptide system and may indirectly regulate blood pressure. Genetic factors relate to corin remain unclear. The purpose of the current study was to comprehensively examine the associations among CORIN SNPs, methylations, serum corin levels and hypertension.

Results

We genotyped 9 tag-SNPs in the CORIN gene and measured serum corin levels in 731 new-onset hypertensive cases and 731 age- and sex-matched controls. DNA methylations were tested in 43 individuals. Mendelian randomization was used to investigate the causal associations. Under additive models, we observed associations of rs2289433 (p.Cys13Tyr), rs6823184, rs10517195, rs2271037 and rs12509275 with serum corin levels after adjustment for covariates (P = 0.0399, 0.0238, 0.0016, 0.0148 and 0.0038, respectively). The tag-SNP rs6823184 and SNPs that are in strong linkage disequilibrium with it, i.e., rs10049713, rs6823698 and rs1866689, were associated with CORIN gene expression (P = 2.38 × 10^− 24, 5.94 × 10^− 27, 6.31 × 10^− 27 and 6.30 × 10^− 27, respectively). Neither SNPs nor corin levels was found to be associated with hypertension. SNP rs6823184, which is located in a DNase hypersensitivity cluster, a CpG island and transcription factor binding sites, was significantly associated with cg02955940 methylation levels (P = 1.54 × 10^− 7). A putative causal association between cg02955940 methylation and corin levels was detected (P = 0.0011).

Conclusion

This study identified potentially functional CORIN SNPs that were associated with serum corin level in the Chinese Han population. The effect of CORIN SNPs on corin level may be mediated by DNA methylation.

Damaging coding variants within kainate receptor channel genes are enriched in individuals with schizophrenia, autism and intellectual disabilities

Schizophrenia (Scz), autism spectrum disorder (ASD) and intellectual disability are common complex neurodevelopmental disorders. Kainate receptors (KARs) are ionotropic glutamate ion channels involved in synaptic plasticity which are modulated by auxiliary NETO proteins. Using UK10K exome sequencing data, we interrogated the coding regions of KAR and NETO genes in individuals with Scz, ASD or intellectual disability and population controls; performed follow-up genetic replication studies; and, conducted in silico and in vitro functional studies. We found an excess of Loss-of-Function and missense variants in individuals with Scz compared with control individuals (p = 1.8 × 10⁻¹⁰), and identified a significant burden of functional variants for Scz (p < 1.6 × 10⁻¹¹) and ASD (p = 6.9 × 10⁻¹⁸). Single allele associations for 6 damaging missense variants were significantly replicated (p < 5.0 × 10⁻¹⁵) and confirmed GRIK3 S310A as a protective genetic factor. Functional studies demonstrated that three missense variants located within GluK2 and GluK4, GluK2 (K525E) and GluK4 (Y555N, L825W), affect agonist sensitivity and current decay rates. These findings establish that genetic variation in KAR receptor ion channels confers risk for schizophrenia, autism and intellectual disability and provide new genetic and pharmacogenetic biomarkers for neurodevelopmental disease.

Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics

BACKGROUND

Associations between type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) were discovered in observational studies in both European and East Asian populations. However, whether such associations are causal remains largely unknown.

METHODS

We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of T2D with the risk of ALS in both European and East Asian populations. Our analysis was implemented using summary statistics obtained from large-scale genome-wide association studies with ~660,000 individuals for T2D and ~81,000 individuals for ALS in the European population, and ~191,000 individuals for T2D and ~4100 individuals for ALS in the East Asian population. The causal relationship between T2D and ALS in both populations was estimated using the inverse-variance-weighted methods and was further validated through extensive complementary and sensitivity analyses.

RESULTS

Using multiple instruments that were strongly associated with T2D, a negative association between T2D and ALS was identified in the European population with the odds ratio (OR) estimated to be 0.93 (95% CI 0.88-0.99, p = 0.023), while a positive association between T2D and ALS was observed in the East Asian population with OR = 1.28 (95% CI 0.99-1.62, p = 0.058). These results were robust against instrument selection, various modeling misspecifications, and estimation biases, with the Egger regression and MR-PRESSO ruling out the possibility of horizontal pleiotropic effects of instruments. However, no causal association was found between T2D-related exposures (including glycemic traits) and ALS in the European population.

CONCLUSION

Our results provide new evidence supporting the causal neuroprotective role of T2D on ALS in the European population and provide empirically suggestive evidence of increasing risk of T2D on ALS in the East Asian population. Our results have an important implication on ALS pathology, paving ways for developing therapeutic strategies across multiple populations.

Type 2 Diabetes Mellitus is Associated with the Immunoglobulin G N-Glycome through Putative Proinflammatory Mechanisms in an Australian Population

Type 2 diabetes mellitus (T2DM) is a common complex trait arising from interactions among multiple environmental, genomic, and postgenomic factors. We report here the first attempt to investigate the association between immunoglobulin G (IgG) N-glycan patterns, T2DM, and their clinical risk factors in an Australian population. N-glycosylation of proteins is one of the most frequently observed co- and post-translational modifications, reflecting, importantly, the real-time status of the interplay between the genomic and postgenomic factors. In a community-based case-control study, 849 participants (217 cases and 632 controls) were recruited from an urban community in Busselton, Western Australia. We applied the ultraperformance liquid chromatography method to analyze the composition of IgG N-glycans. We then conducted Spearman's correlation analyses to explore the association between glycan biomarker candidates and clinical risk factors. We performed area under the curve (AUC) analysis of the receiver operating characteristic curves by fivefold cross-validation for clinical risk factors, IgG glycans, and their combination. Two directly measured and four derived glycan peaks were significantly associated with T2DM, after correction for extensive clinical confounders and false discovery rate, thus suggesting that IgG N-glycan traits are highly correlated with T2DM clinical risk factors. Moreover, adding the IgG glycan profiles to fasting blood glucose in the logistic regression model increased the AUC from 0.799 to 0.859. The AUC for IgG glycans alone was 0.623 with a 95% confidence interval 0.580-0.666. In addition, our study provided new evidence of diversity in T2DM complex trait by IgG N-glycan stratification. Six IgG glycan traits were firmly associated with T2DM, which reflects an increased proinflammatory and biological aging status. In summary, our study reports novel associations between the IgG N-glycome and T2DM in an Australian population and the putative role of proinflammatory mechanisms. Furthermore, IgG N-glycomic alterations offer future prospects as inflammatory biomarker candidates for T2DM diagnosis, and monitoring of T2DM progression to cardiovascular disease or renal failure.

Potential Impact of MicroRNA Gene Polymorphisms in the Pathogenesis of Diabetes and Atherosclerotic Cardiovascular Disease

MicroRNAs (miRNAs) are endogenous, small (18–23 nucleotides), non-coding RNA molecules. They regulate the posttranscriptional expression of their target genes. MiRNAs control vital physiological processes such as metabolism, development, differentiation, cell cycle and apoptosis. The control of the gene expression by miRNAs requires efficient binding between the miRNA and their target mRNAs. Genome-wide association studies (GWASs) have suggested the association of single-nucleotide polymorphisms (SNPs) with certain diseases in various populations. Gene polymorphisms of miRNA target sites have been implicated in diseases such as cancers, diabetes, cardiovascular and Parkinson’s disease. Likewise, gene polymorphisms of miRNAs have been reported to be associated with diseases. In this review, we discuss the SNPs in miRNA genes that have been associated with diabetes and atherosclerotic cardiovascular disease in different populations. We also discuss briefly the potential underlining mechanisms through which these SNPs increase the risk of developing these diseases.

Molecular docking of polyoxometalates as potential α-glucosidase inhibitors

α-Glucosidase is an important target enzyme for the treatment of type 2 diabetes in humans. In our previous studies, it was found that polyoxometalates exhibited an effective inhibitory effect on the activity of α-glucosidase, while polyoxometalates have the characteristics of structural diversity and unique properties. Herein, we investigated the inhibition of two different series of polyoxometalates on α-glucosidases by enzyme kinetics and molecular docking. The results demonstrated that all of the studied compounds had a significant inhibitory ability on α-glucosidase as compared with the positive control acarbose. H₈[P₂Mo₁₇Cr(OH₂)O₆₁] reversibly inhibited α-glucosidase in a competitive manner with IC₅₀ of 115.50 ± 1.64 μM and K_I value of 44.31 μM. All other compounds reversibly inhibited enzymatic activity in a mixed manner. H₆PMo₉V₃O₄₀ and H₈[P₂Mo₁₇Cu(OH₂)O₆₁] were the best inhibitors in the Keggin and Dawson series, respectively, with IC₅₀ of 9.63 ± 0.43 and 40.13 ± 0.61 μM, respectively. We conducted molecular docking study and found that the compound and α-glucosidase were mainly non-covalently interacting with hydrogen bonds and van der Waals forces. This result further confirmed the inhibition mechanism of enzyme kinetic experiments.

In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study

AIMS/HYPOTHESIS

The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome 'diabetes' and whether these associations were causal.

METHODS

In 2467 individuals of the population-based, cross-sectional EpiHealth study (45-75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of ≥7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study.

RESULTS

Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, α-L-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes.

CONCLUSIONS/INTERPRETATION

The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways.

Genetic predisposition to mosaic Y chromosome loss in blood

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.

Environmental and genetic contributions to diabetes

Diabetes mellitus (DM) is a heterogeneous group of disorders characterized by persistent hyperglycemia. Its two most common forms are type 1 diabetes (T1D) and type 2 diabetes (T2D), for which genetic and environmental risk factors act in synergy. Because it occurs in children and involves infectious, autoimmune or toxic destruction of the insulin-secreting pancreatic beta-cells, type 1 diabetes has been called juvenile or insulin-deficient diabetes. In type 2, patients can still secrete some insulin but its effectiveness may be attenuated by 'insulin resistance.' There is also a group of rare forms of diabetes in the young which are inherited as monogenetic diseases. Whether one calls the underlying process 'genes vs. environment' or 'nature vs nurture', diabetes occurs at the interface of the two domains. Together with our genetic background we are born tabula rasa-a blank slate upon which the story of life, with all its environmental inputs will be written. There is one proviso: the influence of epigenetic inheritance must also be considered. Thus, in the creation of databases that include "big data" originating from genomic as well as exposome (defined as: the totality of environmental exposure from conception to death), a broad perspective is crucial as these factors act in concert in such chronic illnesses as diabetes that, for example, are likely to require adoption of an appropriate lifestyle change. Also, it is becoming increasingly evident that epigenetic factors can modulate the interplay between genes and environment. Consequently, throughout the life of an individual nature and nurture interact in a complex manner in the development of diabetes. This review addresses the question of the contribution of gene and environment and their interactions in the development of diabetes.

Optimal Designs of Two-Phase Studies

The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Serotonin is elevated in risk-genotype carriers of TCF7L2 - rs7903146

The transcription factor 7-like 2 (TCF7L2) polymorphism rs7903146 is known to be tightly associated with an elevated risk for type 2 diabetes, whereas the molecular mechanisms remain elusive. We evaluated the metabolic profile of a total of 394 patients' serum samples with respect to their rs7903146 genotype using targeted metabolomics in a discovery (n = 154) and a validation (n = 240) study. We have identified serotonin as the top metabolite being increased in carriers of the risk allele. Serotonin was significantly associated with the rs7903146 genotype after full adjustment including type 2 diabetes and further top ranked metabolites. Given the role of peripheral serotonin in metabolic homeostasis and type 2 diabetes, this finding provides a first hint that the well-known impact of the TCF7L2 polymorphism on type 2 diabetes risk may involve a serotonin-dependent pathway.

The Challenges of Chromosome Y Analysis and the Implications for Chronic Kidney Disease

The role of chromosome Y in chronic kidney disease (CKD) remains unknown, as chromosome Y is typically excluded from genetic analysis in CKD. The complex, sex-specific presentation of CKD could be influenced by chromosome Y genetic variation, but there is limited published research available to confirm or reject this hypothesis. Although traditionally thought to be associated with male-specific disease, evidence linking chromosome Y genetic variation to common complex disorders highlights a potential gap in CKD research. Chromosome Y variation has been associated with cardiovascular disease, a condition closely linked to CKD and one with a very similar sexual dimorphism. Relatively few sources of genetic variation in chromosome Y have been examined in CKD. The association between chromosome Y aneuploidy and CKD has never been explored comprehensively, while analyses of microdeletions, copy number variation, and single-nucleotide polymorphisms in CKD have been largely limited to the autosomes or chromosome X. In many studies, it is unclear whether the analyses excluded chromosome Y or simply did not report negative results. Lack of imputation, poor cross-study comparability, and requirement for separate or additional analyses in comparison with autosomal chromosomes means that chromosome Y is under-investigated in the context of CKD. Limitations in genotyping arrays could be overcome through use of whole-chromosome sequencing of chromosome Y that may allow analysis of many different types of genetic variation across the chromosome to determine if chromosome Y genetic variation is associated with CKD.

The Niemann-Pick C1-like 1 rs2073547 polymorphism is associated with type 2 diabetes mellitus in a Chinese population

Objective

To explore the association between Niemann–Pick C1-like 1 gene ( NPC1L1) single nucleotide polymorphisms and type 2 diabetes mellitus (T2DM) in a Chinese population.

Methods

Using propensity score matching, 490 T2DM patients and 490 matched controls were recruited from 13 communities in Guangxi, China. NPC1L1 rs217386 and rs2073547 genotyping was performed using a MassARRAY system.

Results

The rs2073547 genotype distribution differed significantly among patient groups. Low-density lipoprotein cholesterol levels were similar among different rs2073547 genotypes and alleles. The rs2073547 AG genotype was significantly more prevalent in patients with T2DM. After adjusting for risk or protective factors for diabetes, AG and GG+AG genotypes of rs2073547 were associated with significantly increased risks of T2DM. Compared with the AA genotype, the AG genotype was associated with a significantly higher risk of T2DM in participants with gamma-glutamyl transpeptidase (GGT) <45 U/L, systolic blood pressure (SBP) ≥140 mmHg, or triglyceride <1.70 mmol/L. In participants with GGT <45 U/L or SBP ≥140 mmHg, the GG+AG genotype was associated with a significantly higher T2DM risk versus the AA genotype.

Conclusions

The rs2073547 polymorphism of NPC1L1 may be related to T2DM susceptibility in the Chinese population.

Genetics, adaptation to environmental changes and archaic admixture in the pathogenesis of diabetes mellitus in Indigenous Australians

Indigenous Australians are particularly affected by type 2 diabetes mellitus (T2D) due to both their genetic susceptibility and a range of environmental and lifestyle risk factors. Recent genetic studies link predisposition to some diseases, including T2D, to alleles acquired from archaic hominins, such as Neanderthals and Denisovans, which persist in the genomes of modern humans today. Indo-Pacific human populations, including Indigenous Australians, remain extremely underrepresented in genomic research with a paucity of data examining the impact of Denisovan or Neanderthal lineages on human phenotypes in Oceania. The few genetic studies undertaken emphasize the uniqueness and antiquity of Indigenous Australian genomes, with possibly the largest proportion of Denisovan ancestry of any population in the world. In this review, we focus on the potential contributions of ancient genes/pathways to modern human phenotypes, while also highlighting the evolutionary roles of genetic adaptation to dietary and environmental changes associated with an adopted Western lifestyle. We discuss the role of genetic and epigenetic factors in the pathogenesis of T2D in understudied Indigenous Australians, including the potential impact of archaic gene lineages on this disease. Finally, we propose that greater understanding of the underlying genetic predisposition may contribute to the clinical efficacy of diabetes management in Indigenous Australians. We suggest that improved identification of T2D risk variants in Oceania is needed. Such studies promise to clarify how genetic and phenotypic differences vary between populations and, crucially, provide novel targets for personalised medical therapies in currently marginalized groups.

Comprehensive transcriptomic analysis indicates brain regional specific alterations in type 2 diabetes

Type 2 diabetes (T2D) can result in a number of comorbidities involving various organs including heart, eye, kidney and even the brain. However, little is known about the molecular basis of T2D associated brain disorders. In this study, we performed a comprehensive transcriptomic analysis in a total of 304 T2D samples and 608 matched control samples from thirteen distinct brain regions. We observed prominent difference among transcriptomic profiles of diverse brain regions in T2D. The most striking change was found in caudate with thousands of T2D-associated genes identified, followed by hippocampus, while nearly no transcriptomic change was observed in other brain regions. Functional analysis of T2D-associated genes revealed impaired synaptic functions and an association with neurodegenerative diseases. Co-expression analysis of caudate transcriptomic profiles unveiled a core regional specific module that was disorganized in T2D. Sub-modules consisting of regional markers were enriched in T2D risk single nucleotide polymorphisms (SNPs) and implied a causal link with T2D. Hub genes of this module include NSF and ADD2, the former of which has been associated with T2D and neurogenerative diseases. Thus, our work provides useful information for further studies in T2D associated brain disorders.

Sex differences in the association between spousal metabolic risk factors with incidence of type 2 diabetes: a longitudinal study of the Iranian population

BACKGROUND

We investigated whether metabolic risk factors in one spouse were associated with an excessive risk of type 2 diabetes in the other.

METHODS

The study cohort (1999-2018) included 1833 men and 1952 women, aged ≥ 20 years with information on both their own and their spouse's diabetes status and metabolic risk factors including body mass index (BMI), waist circumference, systolic and diastolic blood pressure, triglyceride to high-density lipoprotein cholesterol ratio, and type 2 diabetes. The associations between spousal metabolic risk factors and type 2 diabetes were estimated using Cox regression models adjusted for the three nested sets of covariates.

RESULTS

We found 714 (360 men and 354 women) incident cases of type 2 diabetes, after more than 15 years of follow-up. Among women, having a husband with diabetes was associated with a 38% (hazard ratio (HR) 1.38; 95% confidence interval (CI) 1.03, 1. 84) increased risk of type 2 diabetes, adjusted for age, socioeconomic status, individual's own value of the respective spousal exposure variable, family history of diabetes, and physical activity level. After further adjustment for the woman's own BMI level, the husband's diabetes was associated with 23% (HR 1.23; 0.92, 1.64) higher risk of type 2 diabetes in wives, values which did not reach statistical significance. No significant associations were found between spousal metabolic risk factors and incidence of type 2 diabetes among index men.

CONCLUSION

We found a sex-specific effect of spousal diabetes on the risk of type 2 diabetes. Having a husband with diabetes increased an individual's risk of type 2 diabetes. Our results might contribute to the early detection of individuals at high risk of developing type 2 diabetes, particularly, in women adversely affected by their partner's diabetes.

Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study

Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ-glutamyl transferase [GGT]). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

The Burmese cat as a genetic model of type 2 diabetes in humans

The recent extension of genetic tools to the domestic cat, together with the serendipitous consequences of selective breeding, have been essential to the study of the genetic diseases that affect them. Cats are increasingly presented for veterinary surveillance and share many of human's heritable diseases, allowing them to serve as natural models of these conditions. Feline diabetes mellitus is a common condition in domestic cats that bears close pathological and clinical resemblance to type 2 diabetes in humans, including pancreatic β-cell dysfunction and peripheral insulin resistance. In Australia, New Zealand and Europe, diabetes mellitus is almost four times more common in cats of the Burmese breed than in other breeds. This geographically based breed predisposition parallels familial and population clustering of type 2 diabetes in humans. As a genetically isolated population, the Australian Burmese breed provides a spontaneous, naturally occurring genetic model of type 2 diabetes. Genetically isolated populations typically exhibit extended linkage disequilibrium and increased opportunity for deleterious variants to reach high frequencies over many generations due to genetic drift. Studying complex diseases in such populations allows for tighter control of confounding factors including environmental heterogeneity, allelic frequencies and population stratification. The homogeneous genetic background of Australian Burmese cats may provide a unique opportunity to either refine genetic signals previously associated with type 2 diabetes or identify new risk factors for this disease.

Novel roles of mechanistic target of rapamycin signaling in regulating fetal growth†

Mechanistic target of rapamycin (mTOR) signaling functions as a central regulator of cellular metabolism, growth, and survival in response to hormones, growth factors, nutrients, energy, and stress signals. Mechanistic TOR is therefore critical for the growth of most fetal organs, and global mTOR deletion is embryonic lethal. This review discusses emerging evidence suggesting that mTOR signaling also has a role as a critical hub in the overall homeostatic control of fetal growth, adjusting the fetal growth trajectory according to the ability of the maternal supply line to support fetal growth. In the fetus, liver mTOR governs the secretion and phosphorylation of insulin-like growth factor binding protein 1 (IGFBP-1) thereby controlling the bioavailability of insulin-like growth factors (IGF-I and IGF-II), which function as important growth hormones during fetal life. In the placenta, mTOR responds to a large number of growth-related signals, including amino acids, glucose, oxygen, folate, and growth factors, to regulate trophoblast mitochondrial respiration, nutrient transport, and protein synthesis, thereby influencing fetal growth. In the maternal compartment, mTOR is an integral part of a decidual nutrient sensor which links oxygen and nutrient availability to the phosphorylation of IGFBP-1 with preferential effects on the bioavailability of IGF-I in the maternal-fetal interface and in the maternal circulation. These new roles of mTOR signaling in the regulation fetal growth will help us better understand the molecular underpinnings of abnormal fetal growth, such as intrauterine growth restriction and fetal overgrowth, and may represent novel avenues for diagnostics and intervention in important pregnancy complications.

The Genetic Basis of Metabolic Disease

Recent developments in genetics and genomics are providing a detailed and systematic characterization of the genetic underpinnings of common metabolic diseases and traits, highlighting the inherent complexity within systems for homeostatic control and the many ways in which that control can fail. The genetic architecture underlying these common metabolic phenotypes is complex, with each trait influenced by hundreds of loci spanning a range of allele frequencies and effect sizes. Here, we review the growing appreciation of this complexity and how this has fostered the implementation of genome-scale approaches that deliver robust mechanistic inference and unveil new strategies for translational exploitation.

Mineral Nutrition and the Risk of Chronic Diseases: A Mendelian Randomization Study

We applied Mendelian randomization analyses to investigate the potential causality between blood minerals (calcium, magnesium, iron, copper, and zinc) and osteoporosis (OP), gout, rheumatoid arthritis (RA), type 2 diabetes (T2D), Alzheimer’s disease (AD), bipolar disorder (BD), schizophrenia, Parkinson’s disease and major depressive disorder. Single nucleotide polymorphisms (SNPs) that are independent (r² < 0.01) and are strongly related to minerals (p < 5 × 10⁻⁸) are selected as instrumental variables. Each standard deviation increase in magnesium (0.16 mmol/L) is associated with an 8.94-fold increase in the risk of RA (p = 0.044) and an 8.78-fold increase in BD (p = 0.040) but a 0.10 g/cm² increase in bone density related to OP (p = 0.014). Each per-unit increase in copper is associated with a 0.87-fold increase in the risk of AD (p = 0.050) and BD (p = 0.010). In addition, there is suggestive evidence that calcium is positively correlated (OR = 1.36, p = 0.030) and iron is negatively correlated with T2D risk (OR = 0.89, p = 0.010); both magnesium (OR = 0.26, p = 0.013) and iron (OR = 0.71, p = 0.047) are negatively correlated with gout risk. In the sensitivity analysis, causal estimation is not affected by pleiotropy. This study supports the long-standing hypothesis that magnesium supplementation can increase RA and BD risks and decrease OP risk and that copper intake can reduce AD and BD risks. This study will be helpful to address some controversial debates on the relationships between minerals and chronic diseases.

Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population

To understand the genetics of type 2 diabetes in people of Japanese ancestry, we conducted A meta-analysis of four genome-wide association studies (GWAS; 36,614 cases and 155,150 controls of Japanese ancestry). We identified 88 type 2 diabetes-associated loci (P < 5.0 × 10^-8) with 115 independent signals (P < 5.0 × 10^-6), of which 28 loci with 30 signals were novel. Twenty-eight missense variants were in linkage disequilibrium (r² > 0.6) with the lead variants. Among the 28 missense variants, three previously unreported variants had distinct minor allele frequency (MAF) spectra between people of Japanese and European ancestry (MAF_JPN > 0.05 versus MAF_EUR < 0.01), including missense variants in genes related to pancreatic acinar cells (GP2) and insulin secretion (GLP1R). Transethnic comparisons of the molecular pathways identified from the GWAS results highlight both ethnically shared and heterogeneous effects of a series of pathways on type 2 diabetes (for example, monogenic diabetes and beta cells).

Glucose and Insulin-Related Traits, Type 2 Diabetes and Risk of Schizophrenia: A Mendelian Randomization Study

BACKGROUND

The link between schizophrenia and diabetes mellitus is well established by observational studies; however, the cause-effect relationship remains unclear.

METHODS

Here, we conducted Mendelian randomization analyses to assess a causal relationship of the genetic variants related to elevated fasting glucose levels, hemoglobin A1c (HbA1c), fasting insulin levels, and type 2 diabetes with the risk of schizophrenia. The analyses were performed using summary statistics obtained for the variants identified from the genome-wide association meta-analyses of fasting glucose levels (up to 133,010 individuals), HbA1c (up to 153,377 individuals), fasting insulin levels (up to 108,557 individuals), type 2 diabetes (up to 659,316 individuals), and schizophrenia (up to 108,341 individuals). The association between each variant and schizophrenia was weighted by its association with each studied condition, and estimates were combined using an inverse-variance weighted meta-analysis.

FINDINGS

Using information from thirteen variants related to fasting insulin levels, the causal effect of fasting insulin levels increases (per 1-SD) on the risk of schizophrenia was estimated at an odds ratio (OR) of 2·33 (p = 0·001), which is consistent with findings from the observational studies. The fasting glucose associated single nucleotide polymorphisms (SNPs) had no effect on the risk of schizophrenia in Europeans and East Asians (p > 0·05). Nonsignificant effects on the risk of schizophrenia was observed with raised HbA1c and type 2 diabetes, and consistent estimates were obtained across different populations.

INTERPRETATION

Our results suggest a causal role of elevated fasting insulin levels in schizophrenia pathogenesis.