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

N-glycome signatures in human plasma: associations with physiology and major diseases

N-glycome analysis in total plasma or serum yields information about the levels and glycosylation patterns of major plasma glycoproteins, including immunoglobulins, acute-phase proteins, and apolipoproteins. Until recently, glycomic studies in disease settings largely suffered from small cohort sizes, poor analytical resolution, and poor comparability of results owing to the diversity of analytical techniques. Here, we report on recent advances in high-throughput mass spectrometry glycomics technology that enabled elucidation of N-glycome signatures in the plasma of patients with type 2 diabetes, inflammatory bowel disease, or colorectal cancer. Use of this technology revealed both commonalities and differences among disease fingerprints. Moreover, we summarize findings on glycomic signatures associated with age, sex, and body mass index. High-throughput, high-resolution glycomics technologies, together with robust data analysis workflows, will advance clinical translation.

A CRISPR/Cas9 genome editing pipeline in the EndoC-βH1 cell line to study genes implicated in beta cell function

Type 2 diabetes (T2D) is a global pandemic with a strong genetic component, but most causal genes influencing the disease risk remain unknown. It is clear, however, that the pancreatic beta cell is central to T2D pathogenesis. In vitro gene-knockout (KO) models to study T2D risk genes have so far focused on rodent beta cells. However, there are important structural and functional differences between rodent and human beta cell lines. With that in mind, we have developed a robust pipeline to create a stable CRISPR/Cas9 KO in an authentic human beta cell line (EndoC-βH1). The KO pipeline consists of a dual lentiviral sgRNA strategy and we targeted three genes ( INS, IDE, PAM) as a proof of concept. We achieved a significant reduction in mRNA levels and complete protein depletion of all target genes. Using this dual sgRNA strategy, up to 94 kb DNA were cut out of the target genes and the editing efficiency of each sgRNA exceeded >87.5%. Sequencing of off-targets showed no unspecific editing. Most importantly, the pipeline did not affect the glucose-responsive insulin secretion of the cells. Interestingly, comparison of KO cell lines for NEUROD1 and SLC30A8 with siRNA-mediated knockdown (KD) approaches demonstrate phenotypic differences. NEUROD1-KO cells were not viable and displayed elevated markers for ER stress and apoptosis. NEUROD1-KD, however, only had a modest elevation, by 34%, in the pro-apoptotic transcription factor CHOP and a gene expression profile indicative of chronic ER stress without evidence of elevated cell death. On the other hand, SLC30A8-KO cells demonstrated no reduction in K ATP channel gene expression in contrast to siRNA silencing. Overall, this strategy to efficiently create stable KO in the human beta cell line EndoC-βH1 will allow for a better understanding of genes involved in beta cell dysfunction, their underlying functional mechanisms and T2D pathogenesis.

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.

Racial/ethnic differences in the burden of type 2 diabetes over the life course: a focus on the USA and India

Type 2 diabetes is a common disease worldwide, but its prevalence varies widely by geographical region and by race/ethnicity. This review summarises differences in the frequencies of type 2 diabetes according to race, ethnicity, socioeconomic position, area of residence and environmental toxins. Type 2 diabetes susceptibility often begins early in life, starting with genetic susceptibility at conception and continuing in later life, via in utero, childhood and adult exposures. Early-life factors may lead to overt type 2 diabetes in childhood or in later life, supporting the concept of developmental origins of health and disease. The causes of the racial/ethnic differences in incidence of type 2 diabetes are not well understood. Specifically, the relative contributions of genetic and environmental factors to such differences are largely unknown. With a few exceptions in isolated populations, there is little evidence that differences in frequencies of known type 2 diabetes susceptibility genetic alleles account for racial/ethnic differences, although the search for genetic susceptibility has not been uniform among the world's racial/ethnic groups. In the USA, race/ethnicity is associated with many other risk factors for type 2 diabetes, including being overweight/obese, diet and socioeconomic status. Some studies suggest that some of these factors may account for the race/ethnic differences in prevalence of type 2 diabetes, although there is inadequate research in this area. A better understanding of the impact of these factors on type 2 diabetes risk should lead to more effective prevention and treatment of this disease. This has not yet been achieved but should be a goal for future research.

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Translational genomics and precision medicine: Moving from the lab to the clinic

Translational genomics aims to improve human health by building on discoveries made through genetics research and applying them in the clinical setting. This progress has been made possible by technological advances in genomics and analytics and by the digital revolution. Such advances should enable the development of prognostic markers, tailored interventions, and the design of prophylactic preventive approaches. We are at the cusp of predicting disease risk for some disorders by means of polygenic risk scores integrated with classical epidemiological risk factors. This should lead to better risk stratification and clinical decision-making. A deeper understanding of the link between genome-wide sequence and association with well-characterized phenotypes will empower the development of biomarkers to aid diagnosis, inform disease progression trajectories, and allow better targeting of treatments to those patients most likely to respond.

The Pdx1-Bound Swi/Snf Chromatin Remodeling Complex Regulates Pancreatic Progenitor Cell Proliferation and Mature Islet β-Cell Function

Transcription factors positively and/or negatively impact gene expression by recruiting coregulatory factors, which interact through protein-protein binding. Here we demonstrate that mouse pancreas size and islet β-cell function are controlled by the ATP-dependent Swi/Snf chromatin remodeling coregulatory complex that physically associates with Pdx1, a diabetes-linked transcription factor essential to pancreatic morphogenesis and adult islet cell function and maintenance. Early embryonic deletion of just the Swi/Snf Brg1 ATPase subunit reduced multipotent pancreatic progenitor cell proliferation and resulted in pancreas hypoplasia. In contrast, removal of both Swi/Snf ATPase subunits, Brg1 and Brm, was necessary to compromise adult islet β-cell activity, which included whole-animal glucose intolerance, hyperglycemia, and impaired insulin secretion. Notably, lineage-tracing analysis revealed Swi/Snf-deficient β-cells lost the ability to produce the mRNAs for Ins and other key metabolic genes without effecting the expression of many essential islet-enriched transcription factors. Swi/Snf was necessary for Pdx1 to bind to the Ins gene enhancer, demonstrating the importance of this association in mediating chromatin accessibility. These results illustrate how fundamental the Pdx1:Swi/Snf coregulator complex is in the pancreas, and we discuss how disrupting their association could influence type 1 and type 2 diabetes susceptibility.

The impact of GDF-15, a biomarker for metformin, on the risk of coronary artery disease, breast and colorectal cancer, and type 2 diabetes and metabolic traits: a Mendelian randomisation study

AIMS/HYPOTHESIS

Growth differentiation factor 15 (GDF-15), a suggested biomarker for metformin use, may explain the potential cardioprotective and anti-cancer properties of metformin. We conducted a Mendelian randomisation study to examine the role of GDF-15 in risk of coronary artery disease (CAD) and breast and colorectal cancer. Secondary analyses included examination of the association of GDF-15 with type 2 diabetes, glycaemic traits, BP, lipids and BMI.

METHODS

We obtained SNPs strongly (p value <5 × 10^-8) predicting GDF-15 from a genome-wide association study (GWAS) (n = 5440) and applied them to genetic studies of CAD (CARDIoGRAMplusC4D 1000 Genomes-based GWAS [n = 184,305]), type 2 diabetes (DIAGRAM [DIAbetes Genetics Replication And Meta-analysis; n = 898,130]), glycaemic traits (MAGIC [the Meta-Analyses of Glucose and Insulin-related traits Consortium; HbA_1c: n = 123,665; fasting glucose: n = 46,186]), BP, breast cancer and colorectal cancer (UK Biobank [n ≤ 401,447]), lipids (GLGC [Global Lipids Genetic Consortium; n ≤ 92,820]) and adiposity (GIANT [Genetic Investigation of ANthropometric Traits Consortium; n = 681,275]). Causal estimates were obtained using inverse variance weighting, taking into account correlations between SNPs. Sensitivity analyses included focusing on the lead SNP (rs888663) and validation for CAD in the UK Biobank and for breast cancer in the Breast Cancer Association Consortium.

RESULTS

Using 5 SNPs, increased GDF-15 was associated with lower CAD (OR 0.93 per SD increase, 95% CI 0.87, 0.99) and breast cancer (OR 0.89 per SD increase, 95% CI 0.82, 0.96), with similar results from lead SNP analysis. However, the associations with CAD (OR 0.99 per SD increase, 95% CI 0.93, 1.04) and breast cancer (OR 0.97 per SD increase, 95% CI 0.94, 1.01) in the validation studies were not as apparent. GDF-15 was not associated with type 2 diabetes, glycaemic traits, CAD risk factors or colorectal cancer.

CONCLUSIONS/INTERPRETATION

There is no convincing evidence that GDF-15 reduces risk of CAD or breast or colorectal cancer. Whether the observed inverse association of metformin use with cancer risk is via other unexplored mechanistic pathways warrants further investigation.

The TCF7L2 Locus: A Genetic Window Into the Pathogenesis of Type 1 and Type 2 Diabetes

Over the past ∼15 years there has been great progress in our understanding of the genetics of both type 1 diabetes and type 2 diabetes. This has been driven principally by genome-wide association studies (GWAS) in increasingly larger sample sizes, where many distinct loci have now been reported for both traits. One of the loci that dominates these studies is the TCF7L2 locus for type 2 diabetes. This genetic signal has been leveraged to explore multiple aspects of disease risk, including developments in genetic risk scores, genetic commonalities with cancer, and for gaining insights into diabetes-related molecular pathways. Furthermore, the TCF7L2 locus has aided in providing insights into the genetics of both latent autoimmune diabetes in adults and various presentations of type 1 diabetes. This review outlines the knowledge gained to date and highlights how work with this locus leads the way in guiding how many other genetic loci could be similarly used to gain insights into the pathogenesis of diabetes.

Epigenetic disturbances in obesity and diabetes: Epidemiological and functional insights

BACKGROUND

Obesity and type 2 diabetes (T2D) are major public health issues worldwide, and put a significant burden on the healthcare system. Genetic variants, along with traditional risk factors such as diet and physical activity, could account for up to approximately a quarter of disease risk. Epigenetic factors have demonstrated potential in accounting for additional phenotypic variation, along with providing insights into the causal relationship linking genetic variants to phenotypes.

SCOPE OF REVIEW

In this review article, we discuss the epidemiological and functional insights into epigenetic disturbances in obesity and diabetes, along with future research directions and approaches, with a focus on DNA methylation.

MAJOR CONCLUSIONS

Epigenetic mechanisms have been shown to contribute to obesity and T2D disease development, as well as potential differences in disease risks between ethnic populations. Technology to investigate epigenetic profiles in diseased individuals and tissues has advanced significantly in the last years, and suggests potential in application of epigenetic factors in clinical monitoring and as therapeutic options.

Birthweight and early-onset type 2 diabetes in American Indians: differential effects in adolescents and young adults and additive effects of genotype, BMI and maternal diabetes

AIMS/HYPOTHESIS

The aim of this work was to estimate the impact of birthweight on early-onset (age <40 years) type 2 diabetes.

METHODS

A longitudinal study of American Indians, aged ≥5 years, was conducted from 1965 to 2007. Participants who had a recorded birthweight were followed until they developed diabetes or their last examination before the age of 40 years, whichever came first. Age- and sex-adjusted diabetes incidence rates were computed and Poisson regression was used to model the effect of birthweight on diabetes incidence, adjusted for sex, BMI, a type 2 diabetes susceptibility genetic risk score (GRS) and maternal covariates.

RESULTS

Among 3039 participants, there were 652 incident diabetes cases over a median follow-up of 14.3 years. Diabetes incidence increased with age and was greater in the lowest and highest quintiles of birthweight. Adjusted for covariates, the effect of birthweight on diabetes varied over time, with a non-linear effect at 10-19 years (p < 0.001) and a negative linear effect at older age intervals (20-29 years, p < 0.001; 30-39 years, p = 0.003). Higher GRS, greater BMI and maternal diabetes had additive but not interactive effects on the association between birthweight and diabetes incidence.

CONCLUSIONS/INTERPRETATION

In this high-risk population, both low and high birthweights were associated with increased type 2 diabetes risk in adolescence (age 10-19 years) but only low birthweight was associated with increased risk in young adulthood (20-39 years). Higher type 2 diabetes GRS, greater BMI and maternal diabetes added to the risk of early-onset diabetes.

A global overview of pleiotropy and genetic architecture in complex traits

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

Sequencing reveals protective and pathogenic effects on development of diabetes of rare GLIS3 variants

Background

Based on the association of common GLIS3 variants with various forms of diabetes and the biological role of GLIS3 in beta-cells, we sequenced GLIS3 in non-diabetic and diabetic Danes to investigate the effect of rare missense variants on glucose metabolism.

Methods

We sequenced 53 patients with maturity-onset diabetes of the young (MODY), 5,726 non-diabetic participants, 2,930 patients with newly diagnosed type 2 diabetes and 206 patients with glutamic acid decarboxylase antibody (GADA) -positive diabetes.

Results

In total we identified 86 rare (minor allele frequency < 0.1%) missense variants. None was considered causal for the presence of MODY. Among patients with type 2 diabetes, we observed a higher prevalence of rare GLIS3 missense variants (2.5%) compared to non-diabetic individuals (1.8%) (odds ratio of 1.37 (interquartile range:1.01–1.88, p = 0.04)). A significantly increased HbA1c was found among patients with type 2 diabetes and with GADA-positive diabetes carrying rare GLIS3 variants compared to non-carriers of rare GLIS3 variants with diabetes (p = 0.02 and p = 0.004, respectively). One variant (p.I28V) was found to have a minor allele frequency of only 0.03% among patients with type 2 diabetes compared to 0.2% among non-diabetic individuals suggesting a protective function (odds ratio of 0.20 (interquartile range: 0.005–1.4, p = 0.1)), an effect which was supported by publically available data. This variant was also associated with a lower level of fasting plasma glucose among non-diabetic individuals (p = 0.046).

Conclusion

Rare missense variants in GLIS3 associates nominally with increased level of HbA1c and increased risk of developing type 2 diabetes. In contrast, the rare p.I28V variant associate with reduced level of fasting plasma glucose and may be protective against type 2 diabetes.

How Recent Advances in Genomics Improve Precision Diagnosis and Personalized Care of Maturity-Onset Diabetes of the Young

PURPOSE OF REVIEW

Non-autoimmune monogenic diabetes (MD) in young people shows a broad spectrum of clinical presentations, which is largely explained by multiple genetic etiologies. This review discusses how the application of state-of-the-art genomics research to precision diagnosis of MD, particularly the various subtypes of maturity-onset diabetes of the young (MODY), has increasingly informed diabetes precision medicine and patient care throughout life.

RECENT FINDINGS

Due to extended genetic and clinical heterogeneity of MODY, diagnosis approaches based on next-generation sequencing have been worthwhile to better ascribe a specific subtype to each patient with young-onset diabetes. This guides the best appropriate treatment and clinical follow-up. Early etiological diagnosis of MD and individualized treatment are essential for achieving metabolic targets and avoiding long-term diabetes complications, as well as for drastically decreasing the financial and societal burden of diabetes-related healthcare. Genomic medicine-based practices help to optimize long-term clinical follow-up and patient care management.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Genome editing of human pancreatic beta cell models: problems, possibilities and outlook

Understanding the molecular mechanisms behind beta cell dysfunction is essential for the development of effective and specific approaches for diabetes care and prevention. Physiological human beta cell models are needed for this work. We review the possibilities and limitations of currently available human beta cell models and how they can be dramatically enhanced using genome-editing technologies. In addition to the gold standard, primary isolated islets, other models now include immortalised human beta cell lines and pluripotent stem cell-derived islet-like cells. The scarcity of human primary islet samples limits their use, but valuable gene expression and functional data from large collections of human islets have been made available to the scientific community. The possibilities for studying beta cell physiology using immortalised human beta cell lines and stem cell-derived islets are rapidly evolving. However, the functional immaturity of these cells is still a significant limitation. CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) has enabled precise engineering of specific genetic variants, targeted transcriptional modulation and genome-wide genetic screening. These approaches can now be exploited to gain understanding of the mechanisms behind coding and non-coding diabetes-associated genetic variants, allowing more precise evaluation of their contribution to diabetes pathogenesis. Despite all the progress, genome editing in primary pancreatic islets remains difficult to achieve, an important limitation requiring further technological development.

New insights on the genetics of type 1 diabetes

PURPOSE OF REVIEW

The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.

RECENT FINDINGS

We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.

SUMMARY

The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.

Network medicine-travelling with the insulin receptor: Encounter of the second type

Important progress has been made in understanding many aspects of insulin action in the last 10 years. Attention will be focused here on the physical protein interaction network of the internalized insulin receptor (IR) and its relationships with the genetic architecture of type 2 diabetes mellitus (T2D). The IR recognizes signals from the outside (circulating insulin) and engages the insulin signaling response. Within seconds, the IR is also involved in insulin internalization and its subsequent degradation in endosomes (physiological clearance of insulin). A T2D disease module sharing functional similarities with insulin secretion in pancreatic islets was recently identified in the close neighborhood of the internalized IR in liver. This module brought a new light on the apparent functional heterogeneity of numerous genes at risk to T2D by linking them to a few noncanonical layers of signaling feedback loops. These findings should be translated into a better understanding of the primary mechanisms of the disease and consequently a more precise sub-classification of T2D, ultimately leading to precision medicine and the development of new therapeutical drugs.

Gene loci associated with insulin secretion in islets from non-diabetic mice

Genetic susceptibility to type 2 diabetes is primarily due to β-cell dysfunction. However, a genetic study to directly interrogate β-cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged >1,000-fold even though none of the mice were diabetic. The insulin secretory response to each secretagogue had a unique genetic architecture; some of the loci were specific for one condition, whereas others overlapped. Human loci that are syntenic to many of the insulin secretion QTL from mouse are associated with diabetes-related SNPs in human genome-wide association studies. We report on three genes, Ptpn18, Hunk and Zfp148, where the phenotype predictions from the genetic screen were fulfilled in our studies of transgenic mouse models. These three genes encode a non-receptor type protein tyrosine phosphatase, a serine/threonine protein kinase, and a Krϋppel-type zinc-finger transcription factor, respectively. Our results demonstrate that genetic variation in insulin secretion that can lead to type 2 diabetes is discoverable in non-diabetic individuals.

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis

OBJECTIVE

To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes.

DESIGN

Individual participant data meta-analysis.

DATA SOURCES

Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators.

REVIEW METHODS

Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score.

RESULTS

Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I²=7.1%, τ²=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I²=18.0%, τ²=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I²=58.8%, τ²=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I²=25.9%, τ²=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed.

CONCLUSIONS

These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.

The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation

PURPOSE OF REVIEW

Genome-wide association studies have delineated the genetic architecture of type 2 diabetes. While functional studies to identify target transcripts are ongoing, new genetic knowledge can be translated directly to health applications. The review covers several translation directions but focuses on genomic polygenic scores for screening and prevention.

RECENT FINDINGS

Over 400 genomic variants associated with T2D and its related quantitative traits are now known. Genetic scores comprising dozens to millions of associated variants can predict incident T2D. However, measurement of body mass index is more efficient than genetic scores to detect T2D risk groups, and knowledge of T2D genetic risk alone seems insufficient to improve health. Genetically determined metabolic sub-phenotypes can be identified by clustering variants associated with physiological axes like insulin resistance. Genetic sub-phenotyping may be a way forward to identify specific individual phenotypes for prevention and treatment. Genomic polygenic scores for T2D can predict incident diabetes but may not be useful to improve health overall. Genetic detection of T2D sub-phenotypes could be useful to personalize screening and care.

Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies

Background

The principal line of investigation in Genome Wide Association Studies (GWAS) is the identification of main effects, that is individual Single Nucleotide Polymorphisms (SNPs) which are associated with the trait of interest, independent of other factors. A variety of methods have been proposed to this end, mostly statistical in nature and differing in assumptions and type of model employed. Moreover, for a given model, there may be multiple choices for the SNP genotype encoding. As an alternative to statistical methods, machine learning methods are often applicable. Typically, for a given GWAS, a single approach is selected and utilized to identify potential SNPs of interest. Even when multiple GWAS are combined through meta-analyses within a consortium, each GWAS is typically analyzed with a single approach and the resulting summary statistics are then utilized in meta-analyses.

Results

In this work we use as case studies a Type 2 Diabetes (T2D) and a breast cancer GWAS to explore a diversity of applicable approaches spanning different methods and encoding choices. We assess similarity of these approaches based on the derived ranked lists of SNPs and, for each GWAS, we identify a subset of representative approaches that we use as an ensemble to derive a union list of top SNPs. Among these are SNPs which are identified by multiple approaches as well as several SNPs identified by only one or a few of the less frequently used approaches. The latter include SNPs from established loci and SNPs which have other supporting lines of evidence in terms of their potential relevance to the traits.

Conclusions

Not every main effect analysis method is suitable for every GWAS, but for each GWAS there are typically multiple applicable methods and encoding options. We suggest a workflow for a single GWAS, extensible to multiple GWAS from consortia, where representative approaches are selected among a pool of suitable options, to yield a more comprehensive set of SNPs, potentially including SNPs that would typically be missed with the most popular analyses, but that could provide additional valuable insights for follow-up.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0201-4) contains supplementary material, which is available to authorized users.

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.

Type 2 Diabetes: Multiple Genes, Multiple Diseases

PURPOSE OF REVIEW

Type 2 diabetes (T2D), which accounts for the vast majority of diabetes cases, is essentially a diagnosis of exclusion in current clinical practice. Therefore, it is not surprising that T2D is heterogenous in terms of patients' clinical presentation, disease course, and response to treatment. This review summarizes published attempts to improve diabetes subclassification, with a particular focus on the role of genetics.

RECENT FINDINGS

A handful of diabetes subclassification schemas have been proposed using clinical data (patient characteristics and laboratory values), with some subgroups associated with distinct management trends or complication risks. However, phenotypically driven classifications suffer from dependencies on time of variable measurement and are not readily linked to disease mechanism. Germline genetic data, in contrast, are essentially unchanged over a person's lifetime and rooted in mechanism. Clustering of T2D genetic loci has identified at least five groupings of loci representing mechanisms of disease that may aid in deconstructing heterogeneity of T2D, but further work is needed to determine clinical utility. Exciting progress in subclassification of diabetes has demonstrated initial steps in deconstructing disease heterogeneity. Incorporation of genetics into classification schemas will require additional research but has the potential to improve our understanding and management of T2D, both as a single disease and as a part of an integrated metabolic disease network.

Type 2 diabetes: a multifaceted disease

Type 2 diabetes is a complex disease usually diagnosed with little regard to aetiology. In the broader sense, it is a mix of different clearly defined aetiologies, such as monogenic diabetes, that we need to be better at identifying as this has major implications for treatment and patient management. Beyond this, however, type 2 diabetes is a highly heterogeneous polygenic disease. This review outlines the recent developments that recognise this heterogeneity by deconvoluting the aetiology of type 2 diabetes into pathophysiological processes, either by measuring physiological variables (such as beta cell function or insulin resistance) or using partitioned polygenic scores, and addresses recent work that clusters type 2 diabetes into distinct subgroups. Increasing evidence suggests that considering the aetiological components of type 2 diabetes matters, in terms of progression rates, treatment response and complications. In other words, clinicians need to recognise that type 2 diabetes is multifaceted and that its characteristics are important for how patients are managed.

JAZF1, a relevant metabolic regulator in type 2 diabetes

Excessive adiposity and metabolic inflammation are the key risk factors of type 2 diabetes mellitus (T2DM). Juxtaposed with another zinc finger gene 1 (JAZF1) has been identified as a novel transcriptional cofactor, with function of regulating glucose and lipid homeostasis and inflammation. JAZF1 is involved in metabolic process of T2DM via interaction with several nuclear receptors and protein kinases. Additionally, increasing evidence from genome-wide association studies (GWAS) has shown that JAZF1 polymorphisms are closely associated with T2DM. In this review, we have updated the latest research advances on JAZF1 and discussed its regulatory network in T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy.

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.

Altered N-glycosylation profiles as potential biomarkers and drug targets in diabetes

N-glycosylation is a ubiquitous protein modification, and N-glycosylation profiles are emerging as both biomarkers and functional effectors in various types of diabetes. Genome-wide association studies identified glycosyltransferase genes as candidate causal genes for type 1 and type 2 diabetes. Studies focused on N-glycosylation changes in type 2 diabetes demonstrated that patients can be distinguished from healthy controls based on N-glycome composition. In addition, individuals at an increased risk of future disease development could be identified based on N-glycome profiles. Moreover, accumulating evidence indicates that N-glycans have a major role in preventing the impairment of glucose-stimulated insulin secretion by maintaining the glucose transporter in proper orientation, indicating that interindividual variation in protein N-glycosylation might be a novel risk factor contributing to diabetes development. Defective N-glycosylation of T cells has been implicated in type 1 diabetes pathogenesis. Furthermore, studies of N-glycan alterations have successfully been used to identify individuals with rare types of diabetes (such as the HNF1A-MODY), and also to evaluate functional significance of novel diabetes-associated mutations. In conclusion, both N-glycans and glycosyltransferases emerge as potential therapeutic targets in diabetes.

Effects of Calcium, Magnesium, and Potassium Concentrations on Ventricular Repolarization in Unselected Individuals

BACKGROUND

Subclinical changes on the electrocardiogram are risk factors for cardiovascular mortality. Recognition and knowledge of electrolyte associations in cardiac electrophysiology are based on only in vitro models and observations in patients with severe medical conditions.

OBJECTIVES

This study sought to investigate associations between serum electrolyte concentrations and changes in cardiac electrophysiology in the general population.

METHODS

Summary results collected from 153,014 individuals (54.4% women; mean age 55.1 ± 12.1 years) from 33 studies (of 5 ancestries) were meta-analyzed. Linear regression analyses examining associations between electrolyte concentrations (mmol/l of calcium, potassium, sodium, and magnesium), and electrocardiographic intervals (RR, QT, QRS, JT, and PR intervals) were performed. The study adjusted for potential confounders and also stratified by ancestry, sex, and use of antihypertensive drugs.

RESULTS

Lower calcium was associated with longer QT intervals (-11.5 ms; 99.75% confidence interval [CI]: -13.7 to -9.3) and JT duration, with sex-specific effects. In contrast, higher magnesium was associated with longer QT intervals (7.2 ms; 99.75% CI: 1.3 to 13.1) and JT. Lower potassium was associated with longer QT intervals (-2.8 ms; 99.75% CI: -3.5 to -2.0), JT, QRS, and PR durations, but all potassium associations were driven by use of antihypertensive drugs. No physiologically relevant associations were observed for sodium or RR intervals.

CONCLUSIONS

The study identified physiologically relevant associations between electrolytes and electrocardiographic intervals in a large-scale analysis combining cohorts from different settings. The results provide insights for further cardiac electrophysiology research and could potentially influence clinical practice, especially the association between calcium and QT duration, by which calcium levels at the bottom 2% of the population distribution led to clinically relevant QT prolongation by >5 ms.

Mitochondrial Dysfunctions: A Thread Sewing Together Alzheimer's Disease, Diabetes, and Obesity

Metabolic disorders are severe and chronic impairments of the health of many people and represent a challenge for the society as a whole that has to deal with an ever-increasing number of affected individuals. Among common metabolic disorders are Alzheimer's disease, obesity, and type 2 diabetes. These disorders do not have a univocal genetic cause but rather can result from the interaction of multiple genes, lifestyle, and environmental factors. Mitochondrial alterations have emerged as a feature common to all these disorders, underlining perhaps an impaired coordination between cellular needs and mitochondrial responses that could contribute to their development and/or progression.

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND

Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

METHODS

We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

RESULTS

Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

CONCLUSIONS

Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

A Peripheral Blood DNA Methylation Signature of Hepatic Fat Reveals a Potential Causal Pathway for Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a risk factor for type 2 diabetes (T2D). We aimed to identify the peripheral blood DNA methylation signature of hepatic fat. We conducted epigenome-wide association studies of hepatic fat in 3,400 European ancestry (EA) participants and in 401 Hispanic ancestry and 724 African ancestry participants from four population-based cohort studies. Hepatic fat was measured using computed tomography or ultrasound imaging and DNA methylation was assessed at >400,000 cytosine-guanine dinucleotides (CpGs) in whole blood or CD14+ monocytes using a commercial array. We identified 22 CpGs associated with hepatic fat in EA participants at a false discovery rate <0.05 (corresponding P = 6.9 × 10-6) with replication at Bonferroni-corrected P < 8.6 × 10-4 Mendelian randomization analyses supported the association of hypomethylation of cg08309687 (LINC00649) with NAFLD (P = 2.5 × 10-4). Hypomethylation of the same CpG was also associated with risk for new-onset T2D (P = 0.005). Our study demonstrates that a peripheral blood-derived DNA methylation signature is robustly associated with hepatic fat accumulation. The hepatic fat-associated CpGs may represent attractive biomarkers for T2D. Future studies are warranted to explore mechanisms and to examine DNA methylation signatures of NAFLD across racial/ethnic groups.

Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study

AIMS/HYPOTHESIS

Muscle mass and strength may protect against type 2 diabetes as a sink for glucose disposal. In randomised controlled trials, resistance training improves glucose metabolism in people with the metabolic syndrome. Whether increasing muscle mass and strength protects against diabetes in the general population is unknown. We assessed the effect of markers of muscle mass and strength on diabetes and glycaemic traits using bi-directional Mendelian randomisation.

METHODS

Inverse variance weighting estimates were obtained by applying genetic variants that predict male lean mass, female lean mass and grip strength, obtained from the UK Biobank GWAS, to the largest available case-control study of diabetes (DIAbetes Genetics Replication And Meta-analysis [DIAGRAM]; n = 74,124 cases and 824,006 controls) and to a study of glycaemic traits (Meta-Analyses of Glucose and Insulin-related traits Consortium [MAGIC]). Conversely, we also applied genetic variants that predict diabetes, HbA_1c, fasting glucose, fasting insulin and HOMA-B to UK Biobank summary statistics for genetic association with lean mass and grip strength. As sensitivity analyses we used weighted median, Mendelian randomisation (MR)-Egger and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and removed pleiotropic SNPs.

RESULTS

Grip strength was not significantly associated with diabetes using inverse variance weighting (OR 0.72 per SD increase in grip strength, 95% CI 0.51, 1.01, p = 0.06) and including pleiotropic SNPs but was significantly associated with diabetes using MR-PRESSO (OR 0.77, 95% CI 0.62, 0.95, p = 0.02) after removing pleiotropic SNPs. Female lean mass was significantly associated with diabetes (OR 0.91, 95% CI 0.84, 0.99, p = 0.02) while male lean mass was not significant but directionally similar (OR 0.94, 95% CI 0.88, 1.01, p = 0.09). Conversely, diabetes was inversely and significantly associated with male lean mass (β -0.02 SD change in lean mass, 95% CI -0.04, -0.00, p = 0.04) and grip strength (β -0.01, 95% CI -0.02, -0.00, p = 0.01).

CONCLUSIONS/INTERPRETATION

Increased muscle mass and strength may be related to lower diabetes risk. Diabetes may also be associated with grip strength and lean mass. Muscle strength could warrant further investigation as a possible target of intervention for diabetes prevention.

Genomic annotation of disease-associated variants reveals shared functional contexts

Variation in non-coding DNA, encompassing gene regulatory regions such as enhancers and promoters, contributes to risk for complex disorders, including type 2 diabetes. While genome-wide association studies have successfully identified hundreds of type 2 diabetes loci throughout the genome, the vast majority of these reside in non-coding DNA, which complicates the process of determining their functional significance and level of priority for further study. Here we review the methods used to experimentally annotate these non-coding variants, to nominate causal variants and to link them to diabetes pathophysiology. In recent years, chromatin profiling, massively parallel sequencing, high-throughput reporter assays and CRISPR gene editing technologies have rapidly become indispensable tools. Rather than treating individual variants in isolation, we discuss the importance of accounting for context, both genetic (such as flanking DNA sequence) and environmental (such as cellular state or environmental exposure). Incorporating these features shows promise in terms of revealing biologically convergent molecular signatures across distant and seemingly unrelated loci. Studying regulatory elements in the proper context will be crucial for interpreting the functional significance of disease-associated variants and applying the resulting knowledge to improve patient care.

Noncoding Variations in the Gene Encoding Ceramide Synthase 6 are Associated with Type 2 Diabetes in a Large Indigenous Australian Pedigree

Type 2 diabetes (T2D) is a chronic disease that disproportionately affects Indigenous Australians. We have previously reported the localization of a novel T2D locus by linkage analysis to chromosome 2q24 in a large admixed Indigenous Australian pedigree (Busfield et al. (2002). American Journal of Human Genetics, 70, 349-357). Here we describe fine mapping of this region in this pedigree, with the identification of SNPs showing strong association with T2D: rs3845724 (diabetes p = 7 × 10-4), rs4668106 (diabetes p = 9 × 10-4) and rs529002 (plasma glucose p = 3 × 10-4). These associations were successfully replicated in an independent collection of Indigenous Australian T2D cases and controls. These SNPs all lie within the gene encoding ceramide synthase 6 (CERS6) and thus may regulate ceramide synthesis.

On the differences between mega- and meta-imputation and analysis exemplified on the genetics of age-related macular degeneration

While current genome‐wide association analyses often rely on meta‐analysis of study‐specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega‐imputation and mega‐analysis) or study‐specifically (meta‐imputation and meta‐analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age‐related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis.

From 27,448,454 genetic variants after 1,000‐Genomes‐based imputation, mega‐imputation yielded ~400,000 more variants with high imputation quality (mostly rare variants) compared to meta‐imputation. For AMD signal detection (P < 5 × 10⁻⁸) in mega‐imputed data, most loci were detected with mega‐analysis without adjusting for study membership (40 loci, including 34 known); we considered these loci genuine, since genetic effects and P‐values were comparable across analyses. In meta‐imputed data, we found 31 additional signals, mostly near chromosome tails or reference panel gaps, which disappeared after accounting for interaction of whole‐genome amplification (WGA) with study membership or after excluding studies with WGA‐participants.

For signal detection with multistudy IPD, we recommend mega‐imputation and mega‐analysis, with meta‐imputation followed by meta‐analysis being a computationally appealing alternative.

Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC

We recently conducted a large association analysis to compare the genetic profiles between patients with psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC). Despite including over 7,000 genotyped patients, only the MHC achieved genome-wide significance. In this study, we hypothesized that appropriate epigenomic elements (H3K27ac marks for active enhancers) can guide us to reveal valuable information about the loci with suggestive evidence of association. Our aim is to investigate these loci and explore how they may lead to the development of PsA. We evaluated this potential by investigating the genes connected with these loci from the perspective of pharmacogenomics and gene expression. We illustrated that markers with suggestive evidence of association outside the MHC region are enriched in H3K27ac marks for osteoblast and chondrogenic differentiated cells; using pharmacogenomics resources, we showed that genes near these markers are targeted by existing drugs used to treat psoriatic arthritis. Significantly, six of the ten suggestive significant loci overlapping the regulatory elements encompass genes differentially expressed (FDR < 5%) in differentiated osteoblasts, including genes participating in the Wnt signaling such as RUNX1, FUT8, and CTNNAL1. Our approach shows that epigenomic information can be used as cost-effective approach to enhance the inferences for GWAS results, especially in situations when few genome-wide significant loci are available. Our results also point the way to more directed investigations comparing the genetics of PsA and PsC.

The Contribution of Low-Frequency and Rare Coding Variation to Susceptibility to Type 2 Diabetes

PURPOSE OF REVIEW

Soon after the first genome-wide association study (GWAS) for type 2 diabetes (T2D) was published, it was hypothesized that rare and low-frequency variants might explain a substantial proportion of disease risk. Rare coding variants in particular were emphasized given their large expected role in disease. This review summarizes the extent to which recent T2D genetic studies provide evidence for or against this hypothesis.

RECENT FINDINGS

Following a comprehensive study of T2D genetic architecture using three sequencing and genotyping technologies, four even larger studies have provided a yet higher resolution view of the role of rare and low-frequency coding variation in T2D susceptibility. Empirical evidence strongly suggests that common regulatory variants are the dominant contributor to T2D heritability. However, rare coding variants may nonetheless be pervasive across T2D-relevant genes. A strategy using common variants to map disease genes, and rare coding variants to link molecular gene perturbations to cellular and phenotypic effects, may be an effective means to investigate T2D pathogenesis and potential new therapies.

Heterogeneous impact of type 2 diabetes mellitus-related genetic variants on gestational glycemic traits: review and future research needs

Gestational glucose homeostasis influences mother's metabolic health, pregnancy outcomes, fetal development and offspring growth. To understand the genetic roles in pregnant glucose metabolism and genetic predisposition for gestational diabetes (GDM), we reviewed the recent literature up to Jan, 2018 and evaluated the influence of T2DM-related genetic variants on gestational glycemic traits and glucose tolerance. A total of 140 variants of 89 genes were integrated. Their associations with glycemic traits in and outside pregnancy were compared. The genetic circumstances underlying glucose metabolism exhibit a similarity between pregnant and non-pregnant populations. While, not all of the T2DM-associated genetic variants are related to pregnant glucose tolerance, such as genes involved in fasting insulin/C-peptide regulation. Some genetic variants may have distinct effects on gestational glucose homeostasis. And certain genes may be particularly involved in this process via specific mechanisms, such as HKDC1, MTNR1B, BACE2, genes encoding cell cycle regulators, adipocyte regulators, inflammatory factors and hepatic factors related to gestational glucose sensing and insulin signaling. However, it is currently difficult to evaluate these associations with quantitative synthesis due to inadequate data, different analytical methods, varied measurements for glycemic traits, controversies in diagnosis of GDM, and unknown ethnicity- and/or sex-related influences on pregnant maternal metabolism. In conclusion, different genetic associations with glycemic traits may exist between pregnant and non-pregnant conditions. Comprehensive research on specific genetic regulation in gestation is necessary.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

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.

Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment

Pain is a subjective sensory experience that can, mostly, be reported but cannot be directly measured or quantified. Nevertheless, a suite of biomarkers related to mechanisms, neural activity, and susceptibility offer the possibility-especially when used in combination-to produce objective pain-related indicators with the specificity and sensitivity required for diagnosis and for evaluation of risk of developing pain and of analgesic efficacy. Such composite biomarkers will also provide improved understanding of pain pathophysiology.

Assessing the causal association of glycine with risk of cardio-metabolic diseases

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance.

Genetics and genomics studies in type 2 diabetes: A brief review of the current scenario in the Arab region

BACKGROUND

Type 2 diabetes (T2D) is a polygenic and multi-factorial complex disease, the challenge to find genetic markers that could explain the risk of development of this disease still remains unresolved. The Arab region is one among the populations with a high prevalence of T2D and a large number of studies have been carried out in exploring the genetic factors associated with T2D risk.

AIM

To summarize the recent developments in the Arab world based on the recent studies that had looked into genetic factors associated with the development of T2D in the Arab populations.

METHODS

A systematic literature search was conducted to identify studies published between 2015 and 2018 reporting genetic factors or polymorphisms associated with the risk of T2D in the Arab world. The online databases PubMed and Web of Science were used to perform the literature search.

CONCLUSION

The present study has evaluated 14 studies published during the year 2015-2018. Studies from Egypt, Iraq, Jordan, Oman, Qatar, Saudi Arabia, Tunisia, and United Arab Emirates had been explored studying the associations of GIPR, ADIPOQ, FTO, (GRCh38.p12), MLXIP, AKNAD1, KCNJ11 CDKAL1, CDKN2A/2B, TCF7L2, ACE, SNAP25, ELMO1, VDR, KCTD8, GABRA4 and PRKD1 genes with T2D development.