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

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.

Integrated Analysis of Summary Statistics to Identify Pleiotropic Genes and Pathways for the Comorbidity of Schizophrenia and Cardiometabolic Disease

Genome-wide association studies (GWAS) have identified abundant risk loci associated with schizophrenia (SCZ), cardiometabolic disease (CMD) including body mass index, coronary artery diseases, type 2 diabetes, low- and high-density lipoprotein, total cholesterol, and triglycerides. Although recent studies have suggested that genetic risk shared between these disorders, the pleiotropic genes and biological pathways shared between them are still vague. Here we integrated comprehensive multi-dimensional data from GWAS, expression quantitative trait loci (eQTL), and gene set database to systematically identify potential pleiotropic genes and biological pathways shared between SCZ and CMD. By integrating the results from different approaches including FUMA, Sherlock, SMR, UTMOST, FOCUS, and DEPICT, we revealed 21 pleiotropic genes that are likely to be shared between SCZ and CMD. These genes include VRK2, SLC39A8, NT5C2, AMBRA1, ARL6IP4, OGFOD2, PITPNM2, CDK2AP1, C12orf65, ABCB9, SETD8, MPHOSPH9, FES, FURIN, INO80E, YPEL3, MAPK3, SREBF1, TOM1L2, GATAD2A, and TM6SF2. In addition, we also performed the gene-set enrichment analysis using the software of GSA-SNP2 and MAGMA with GWAS summary statistics and identified three biological pathways (MAPK-TRK signaling, growth hormone signaling, and regulation of insulin secretion signaling) shared between them. Our study provides insights into the pleiotropic genes and biological pathways underlying mechanisms for the comorbidity of SCZ and CMD. However, further genetic and functional studies are required to validate the role of these potential pleiotropic genes and pathways in the etiology of the comorbidity of SCZ and CMD, which should provide potential targets for future diagnostics and therapeutics.

Diabetes: Is There a Future for Pharmacogenomics Guided Treatment?

Diabetes is a disease defined on the basis of hyperglycemia. There are monogenic forms of diabetes where defining the genetic cause has a dramatic impact on treatment—with patients being able to transition from insulin to sulfonylureas. However, the majority of diabetes is type 2 diabetes. This review outlines the robust evidence accrued to date for pharmacogenetics of metformin, sulfonylureas, thiazolidinediones, and dipeptidyl peptidase‐4 inhibitors but highlights that these variants will only be of clinical utility when the genotype is already known at the point of prescribing. The future of pharmacogenetics in diabetes and other common complex disease relies on a paradigm shift—that of preemptive panel genotyping and use of clinical decision support tools to assimilate this genetic information with other clinical phenotypic data and to present this information simply to the prescriber. Given the recent dramatic fall in genotyping costs, this future is not far off.

Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic β Cell Identity and Function

EndoC-βH1 is emerging as a critical human β cell model to study the genetic and environmental etiologies of β cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-βH1. Analyses of these maps define known (e.g., PDX1 and ISL1) and putative (e.g., PCSK1 and mir-375) β cell-specific transcriptional cis-regulatory networks and identify allelic effects on cis-regulatory element use. Importantly, comparison with maps generated in primary human islets and/or β cells indicates preservation of chromatin looping but also highlights chromosomal aberrations and fetal genomic signatures in EndoC-βH1. Together, these maps, and a web application we created for their exploration, provide important tools for the design of experiments to probe and manipulate the genetic programs governing β cell identity and (dys)function in diabetes.

EndoC-βH1 is becoming an important cellular model to study genes and pathways governing human β cell identity and function, but its (epi)genomic similarity to primary human islets is unknown. Lawlor et al. complete and compare extensive EndoC and primary human islet multiomic maps to identify shared and distinct genomic circuitry.

Diabetes-associated genetic variation in TCF7L2 alters pulsatile insulin secretion in humans

BACKGROUNDMetabolic disorders such as type 2 diabetes have been associated with a decrease in insulin pulse frequency and amplitude. We hypothesized that the T allele at rs7903146 in TCF7L2, previously associated with β cell dysfunction, would be associated with changes in these insulin pulse characteristics.METHODSTwenty-nine nondiabetic subjects (age 46 ± 2, BMI 28 ± 1 kg/m2) participated in this study. Of these, 16 were homozygous for the C allele at rs7903146 and 13 were homozygous for the T allele. Deconvolution of peripheral C-peptide concentrations allowed the reconstruction of portal insulin secretion over time. These data were used for subsequent analyses. Pulse orderliness was assessed by approximate entropy (ApEn), and the dispersion of insulin pulses was measured by a frequency dispersion index (FDI) after a Fast Fourier Transform (FFT) of individual insulin secretion rates.RESULTSDuring fasting conditions, the CC genotype group exhibited decreased pulse disorderliness compared with the TT genotype group (1.10 ± 0.03 vs. 1.19 ± 0.04, P = 0.03). FDI decreased in response to hyperglycemia in the CC genotype group, perhaps reflecting less entrainment of insulin secretion during fasting.CONCLUSIONDiabetes-associated variation in TCF7L2 is associated with decreased orderliness and pulse dispersion, unchanged by hyperglycemia. Quantification of ApEn and FDI could represent novel markers of β cell health.FUNDINGThis work was funded by US NIH (DK78646, DK116231), University of Padova research grant CPDA145405, and Mayo Clinic General Clinical Research Center (UL1 TR000135).

Sibling history is associated with heart failure after a first myocardial infarction

Objective

Morbidity and mortality due to heart failure (HF) as a complication of myocardial infarction (MI) is high, and remains among the leading causes of death and hospitalisation. This study investigated the association between family history of MI with or without HF, and the risk of developing HF after first MI.

Methods

Through nationwide registries, we identified all individuals aged 18-50 years hospitalised with first MI from 1997 to 2016 in Denmark. We identified 13 810 patients with MI, and the cohort was followed until HF diagnosis, second MI, 3 years after index MI, emigration, death or the end of 2016, whichever occurred first. HRs were estimated by Cox hazard regression models adjusted for sex, age, calendar year and comorbidities (reference: patients with no family history of MI).

Results

After adjustment, we observed an increased risk of MI-induced HF for those having a sibling with MI with HF (HR 2.05, 95% CI 1.02 to 4.12). Those having a sibling with MI without HF also had a significant, but lower increased risk of HF (HR 1.39, 95% CI 1.05 to 1.84). Parental history of MI with or without HF was not associated with HF.

Conclusion

In this nationwide cohort, sibling history of MI with or without HF was associated with increased risk of HF after first MI, while a parental family history was not, suggesting that shared environmental factors may predominate in the determination of risk for developing HF.

Germline burden of rare damaging variants negatively affects human healthspan and lifespan

Heritability of human lifespan is 23-33% as evident from twin studies. Genome-wide association studies explored this question by linking particular alleles to lifespan traits. However, genetic variants identified so far can explain only a small fraction of lifespan heritability in humans. Here, we report that the burden of rarest protein-truncating variants (PTVs) in two large cohorts is negatively associated with human healthspan and lifespan, accounting for 0.4 and 1.3 years of their variability, respectively. In addition, longer-living individuals possess both fewer rarest PTVs and less damaging PTVs. We further estimated that somatic accumulation of PTVs accounts for only a small fraction of mortality and morbidity acceleration and hence is unlikely to be causal in aging. We conclude that rare damaging mutations, both inherited and accumulated throughout life, contribute to the aging process, and that burden of ultra-rare variants in combination with common alleles better explain apparent heritability of human lifespan.

ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study

OBJECTIVE

To determine whether ACE inhibitors reduce the risk of type 2 diabetes using a Mendelian randomization (MR) approach.

RESEARCH DESIGN AND METHODS

A two-sample MR analysis included 17 independent genetic variants associated with ACE serum concentration in 4,147 participants from the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (clinical trial reg. no. NCT00069784) trial, and their effects on type 2 diabetes risk were estimated from 18 studies of the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. A genetic risk score (GRS) underpinning lower ACE concentration was then tested for association with type 2 diabetes prevalence in 341,872 participants, including 16,320 with type 2 diabetes, from the UK Biobank. MR estimates were compared after standardization for blood pressure change, with the estimate obtained from a randomized controlled trial (RCT) meta-analysis of ACE inhibitors versus placebo (n = 31,200).

RESULTS

Genetically lower ACE concentrations were associated with a lower risk of type 2 diabetes (odds ratio [OR] per SD 0.92 [95% CI 0.89-0.95]; P = 1.79 × 10^-7). This result was replicated in the UK Biobank (OR per SD 0.97 [0.96-0.99]; P = 8.73 × 10^-4). After standardization, the ACE GRS was associated with a larger decrease in type 2 diabetes risk per 2.4-mmHg lower mean arterial pressure (MAP) compared with that obtained from an RCT meta-analysis (OR per 2.4-mmHg lower MAP 0.19 [0.07-0.51] vs. 0.76 [0.60-0.97], respectively; P = 0.007 for difference).

CONCLUSIONS

These results support the causal protective effect of ACE inhibitors on type 2 diabetes risk and may guide therapeutic decision making in clinical practice.

Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

BACKGROUND

There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother-offspring pairs.

METHODS

We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown.

RESULTS

We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes.

CONCLUSIONS

We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.

Classification of Type 2 Diabetes Genetic Variants and a Novel Genetic Risk Score Association With Insulin Clearance

CONTEXT

Genome-wide association studies have identified more than 450 single nucleotide polymorphisms (SNPs) for type 2 diabetes (T2D).

OBJECTIVE

To facilitate use of these SNPs in future genetic risk score (GRS)-based analyses, we aimed to classify the SNPs based on physiology. We also sought to validate GRS associations with insulin-related traits in deeply phenotyped Mexican Americans.

DESIGN, SETTING, AND PARTICIPANTS

A total of 457 T2D SNPs from the literature were assigned physiologic function based on association studies and cluster analyses. All SNPs (All-GRS), beta-cell (BC-GRS), insulin resistance (IR-GRS), lipodystrophy (Lipo-GRS), and body mass index plus lipids (B + L-GRS) were evaluated for association with diabetes and indices of insulin secretion (from oral glucose tolerance test), insulin sensitivity and insulin clearance (from euglycemic clamp), and adiposity and lipid markers in 1587 Mexican Americans.

RESULTS

Of the 457 SNPs, 52 were classified as BC, 30 as IR, 12 as Lipo, 12 as B + L, whereas physiologic function of 351 was undefined. All-GRS was strongly associated with T2D. Among nondiabetic Mexican Americans, BC-GRS was associated with reduced insulinogenic index, IR-GRS was associated with reduced insulin sensitivity, and Lipo-GRS was associated with reduced adiposity. B + L-GRS was associated with increased insulin clearance. The latter did not replicate in an independent cohort wherein insulin clearance was assessed by a different method.

CONCLUSIONS

Supporting their utility, BC-GRS, IR-GRS, and Lipo-GRS, based on SNPs discovered largely in Europeans, exhibited expected associations in Mexican Americans. The novel association of B + L-GRS with insulin clearance suggests that impaired ability to reduce insulin clearance in compensation for IR may play a role in the pathogenesis of T2D. Whether this applies to other ethnic groups remains to be determined.

Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics

Type 2 diabetes mellitus (T2DM) is an increasingly prevalent multifactorial disease that has both genetic and environmental risk factors, resulting in impaired glucose homeostasis. Genome-wide association studies (GWAS) have identified over 400 genetic signals that are associated with altered risk of T2DM. Human physiology and epigenomic data support a central role for the pancreatic islet in the pathogenesis of T2DM. This Review focuses on the promises and challenges of moving from genetic associations to molecular mechanisms and highlights efforts to identify the causal variant and effector transcripts at T2DM GWAS susceptibility loci. In addition, we examine current human models that are used to study both β-cell development and function, including EndoC-β cell lines and human induced pluripotent stem cell-derived β-like cells. We use examples of four T2DM susceptibility loci (CDKAL1, MTNR1B, SLC30A8 and PAM) to emphasize how a holistic approach involving genetics, physiology, and cellular and developmental biology can disentangle disease mechanisms at T2DM GWAS signals.

The impact of phenotype, ethnicity and genotype on progression of type 2 diabetes mellitus

AIM

To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.

METHODS

We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.

RESULTS

Our search yielded 2785 articles; based on title, abstract and full-text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: 'Initiation of insulin', 'Initiation of oral antidiabetic drug', 'treatment intensification', 'antidiabetic therapy failure', 'glycaemic deterioration', 'decline in beta-cell function' and 'change in insulin dose'. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi-ethnic studies.

CONCLUSION

We have identified the major determinants of diabetes progression-younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large-scale studies of diabetes progression in different ethnic groups.

Fine-mapping within eQTL credible intervals by expression CROP-seq

The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

Multiparametric Assays for Accelerating Early Drug Discovery

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Evaluation of the Host Genetic Effects of Tuberculosis-Associated Variants Among Patients With Type 1 and Type 2 Diabetes Mellitus

Background

Understanding the link between tuberculosis (TB) and diabetes is increasingly important as public health responds to the growing global burden of noncommunicable diseases. Genetic association studies have identified numerous host genetic variants linked to TB; however, potential host genetic mechanisms linking TB and diabetes remain unexplored.

Methods

We used genetic and phenotypic data from the UK Biobank to evaluate the association of 6 previously reported TB-related host genetic variants (genome-wide significant associations from published studies) with diabetes. The study included 409692 adults of European ancestry including 2177 with type 1 diabetes mellitus (T1DM) and 13976 with type 2 diabetes mellitus (T2DM), defined by ICD-10 diagnosis codes.

Results

Of the 6 TB-associated single nucleotide polymorphisms (SNPs), 2 were associated with T1DM and 3 with T2DM, after adjusting for age, sex, body mass index, smoking, alcohol use, and population structure. After correction for multiple testing, SNPs rs2894257 and rs3135359 (HLA-DRA-DQA1) were associated with T1DM (rs2894257: odds ratio [OR], 1.32; 95% confidence interval [CI], 1.21–1.45; rs3135359: OR, 1.72; 95% CI, 1.57–1.88) and T2DM (rs2894257: OR, 1.11; 95% CI, 1.08–1.15; rs3135359: OR, 1.06; 95% CI, 1.025–1.096). The associations with T2DM weakened for rs2894257 and rs3135359 after further exclusion of probable T1DM cases defined by International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes. SNP rs4733781 on chromosome 8 (ASAP1 gene) was associated with T2DM after exclusion of T1DM cases.

Conclusions

Our findings suggest that common host genetic effects may play a role in the molecular mechanism linking TB and diabetes. Future large genetic studies of TB and diabetes should focus on developing countries with high burdens of infectious and chronic diseases.

Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks¹, a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted². To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (n_total = 675,898; BioBank Japan (n = 179,066), UK Biobank (n = 361,194) and FinnGen (n = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02-1.04], P_meta = 3.9 × 10^-13) and parental lifespan (hazard ratio = 1.06[1.06-1.07], P = 2.0 × 10^-86). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals (P_{heterogeneity} = 9.5 × 10^-8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype-phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health.

The evolving role of TonEBP as an immunometabolic stress protein

Tonicity-responsive enhancer-binding protein (TonEBP), which is also known as nuclear factor of activated T cells 5 (NFAT5), was discovered 20 years ago as a transcriptional regulator of the cellular response to hypertonic (hyperosmotic salinity) stress in the renal medulla. Numerous studies since then have revealed that TonEBP is a pleiotropic stress protein that is involved in a range of immunometabolic diseases. Some of the single-nucleotide polymorphisms (SNPs) in TONEBP introns are cis-expression quantitative trait loci that affect TONEBP transcription. These SNPs are associated with increased risk of type 2 diabetes mellitus, diabetic nephropathy, inflammation, high blood pressure and abnormal plasma osmolality, indicating that variation in TONEBP expression might contribute to these phenotypes. In addition, functional studies have shown that TonEBP is involved in the pathogenesis of rheumatoid arthritis, atherosclerosis, diabetic nephropathy, acute kidney injury, hyperlipidaemia and insulin resistance, autoimmune diseases (including type 1 diabetes mellitus and multiple sclerosis), salt-sensitive hypertension and hepatocellular carcinoma. These pathological activities of TonEBP are in contrast to the protective actions of TonEBP in response to hypertonicity, bacterial infection and DNA damage induced by genotoxins. An emerging theme is that TonEBP is a stress protein that mediates the cellular response to a range of pathological insults, including excess caloric intake, inflammation and oxidative stress.

Effects of tryptophan, serotonin, and kynurenine on ischemic heart diseases and its risk factors: a Mendelian Randomization study

BACKGROUND/OBJECTIVES

Tryptophan is an essential amino acid that must be obtained from dietary items, such as dairy products, eggs, nuts, legumes, and grains, which are rich in tryptophan. It has also been suggested as a dietary supplement to improve mental health. Observationally plasma tryptophan is inversely associated with ischemic heart disease (IHD), however, its main metabolites, serotonin, and kynurenine are positively associated with IHD, which makes the effects of tryptophan difficult to infer. This study aimed to obtain less-confounded estimates of the associations of tryptophan and physiologically related factors (serotonin and kynurenine) with IHD, its risk factors and depression.

SUBJECTS/METHODS

We used a two-sample Mendelian Randomization study design. We used genetic instruments independently associated with tryptophan, serotonin, and kynurenine metabolites applied to a meta-analysis of the UK Biobank SOFT CAD study with the CARDIoGRAMplusC4D consortium (cases n ≤ 76,014 and controls n ≤ 264,785), and other consortia for risk factors including diabetes, lipids, and blood pressure, as well as for depression. We combined genetic variant-specific estimates using inverse variance weighting, with MR-Egger, the weighted median and MR-PRESSO as sensitivity analyses.

RESULTS

Tryptophan and serotonin were not associated with IHD. Kynurenine was nominally and positively associated with IHD (odds ratio 1.57 per standard deviation, 95% confidence interval 1.05-2.33) but not after correction for multiple comparisons. Associations with IHD risk factors and depression were null.

CONCLUSIONS

We cannot exclude the possibility that one of the main metabolites of tryptophan, kynurenine, might be positively associated with IHD. Further studies are needed to confirm any association and underlying mechanism.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Plasma N-Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study

OBJECTIVE

Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke).

RESEARCH DESIGN AND METHODS

Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women.

RESULTS

The N-glycan-based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78-0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD.

CONCLUSIONS

Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

Melatonin Effects on Glucose Metabolism: Time To Unlock the Controversy

The past decade has witnessed a revival of interest in the hormone melatonin, partly attributable to the discovery that genetic variation in MTNR1B - the melatonin receptor gene - is a risk factor for impaired fasting glucose and type 2 diabetes (T2D). Despite intensive investigation, there is considerable confusion and seemingly conflicting data on the metabolic effects of melatonin and MTNR1B variation, and disagreement on whether melatonin is metabolically beneficial or deleterious, a crucial issue for melatonin agonist/antagonist drug development and dosing time. We provide a conceptual framework - anchored in the dimension of 'time' - to reconcile paradoxical findings in the literature. We propose that the relative timing between elevated melatonin concentrations and glycemic challenge should be considered to better understand the mechanisms and therapeutic opportunities of melatonin signaling in glycemic health and disease.

Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility.

The authors perform a meta-analysis of cutaneous squamous cell carcinoma, identifying causal variants within skin-specific regulatory elements.

Identification of a LIF-Responsive, Replication-Competent Subpopulation of Human β Cells

The beta (β)-cell mass formed during embryogenesis is amplified by cell replication during fetal and early postnatal development. Thereafter, β cells become functionally mature, and their mass is maintained by a low rate of replication. For those few β cells that replicate in adult life, it is not known how replication is initiated nor whether this occurs in a specialized subset of β cells. We capitalized on a YAP overexpression system to induce replication of stem-cell-derived β cells and, by single-cell RNA sequencing, identified an upregulation of the leukemia inhibitory factor (LIF) pathway. Activation of the LIF pathway induces replication of human β cells in vitro and in vivo. The expression of the LIF receptor is restricted to a subset of transcriptionally distinct human β cells with increased proliferative capacity. This study delineates novel genetic networks that control the replication of LIF-responsive, replication-competent human β cells.

Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

OBJECTIVE

Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity.

METHODS

We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations.

RESULTS

We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals.

CONCLUSIONS

Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.

Using human genetics to understand the disease impacts of testosterone in men and women

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22-1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33-1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76-0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.

Association of IGF1 and VEGFA polymorphisms with diabetic retinopathy in Pakistani population

AIMS

The incidence of microvascular complications, including diabetic retinopathy (DR), increases with duration of type 2 diabetes (T2D). Meta-GWAS have reported numerous single-nucleotide polymorphisms (SNPs) associated with T2D; however, no loci, achieving genome-wide significance has been reported for DR. Vascular endothelial growth factor A (VEGFA) and insulin-like growth factor 1 (IGF1) are considered as potential genetic candidates involved in T2D and DR progression. Moreover, the association of serum levels of these proteins with diabetes-related traits is controversial. Therefore, the current study was designed to evaluate the possible genetic predisposition and role of these circulating growth factors in serum in the pathophysiology of T2D and DR.

METHODS

A cohort of 1126 individuals with T2D was collected including those without retinopathy (DNR = 573), non-progressive diabetic retinopathy (NPDR = 301) and progressive diabetic retinopathy (PDR = 252), and 348 healthy controls. Genomic DNA was isolated, and six SNPs: rs833061, rs13207351, rs1570360, rs2010963, rs5742632 and rs6214, were genotyped and results statistically analyzed. ELISA was performed on a subset of the samples to measure serum levels of IGF1 and VEGFA.

RESULTS

The minor allele of rs6214 was associated with T2D [OR = 1.67 (95% CI  1.39-2.01, p = 4.9E-8)], rs13207351 was associated with NPDR [OR = 1.97 (95% CI  1.28-3.03, p = 9.0E-3)]when compared with DNR, and rs5742632 showed positive association with PDR [OR = 1.66 (95% CI  1.33-2.05, p = 1.0E-4)] compared to DNR. Lowered IGF1 serum levels were found to be associated with T2D, NPDR and PDR.

CONCLUSIONS

IGF1 was found to increase the T2DM susceptibility as well as advanced DR, i.e., PDR, while VEGFA was found to be associated with early DR stage, i.e., NPDR.

The genetics of human ageing

The past two centuries have witnessed an unprecedented rise in human life expectancy. Sustaining longer lives with reduced periods of disability will require an understanding of the underlying mechanisms of ageing, and genetics is a powerful tool for identifying these mechanisms. Large-scale genome-wide association studies have recently identified many loci that influence key human ageing traits, including lifespan. Multi-trait loci have been linked with several age-related diseases, suggesting shared ageing influences. Mutations that drive accelerated ageing in prototypical progeria syndromes in humans point to an important role for genome maintenance and stability. Together, these different strands of genetic research are highlighting pathways for the discovery of anti-ageing interventions that may be applicable in humans.

Influence of Genetic Variation in PDE3A on Endothelial Function and Stroke

We aimed to characterize the genetics of endothelial function and how this influences risk for cardiovascular diseases such as ischemic stroke. We integrated genetic data from a study of ultrasound flow-mediated dilatation of brachial artery in adolescents from ALSPAC (Avon Longitudinal Study of Parents and Children; n=5214) with a study of ischemic stroke (MEGASTROKE: n=60 341 cases and 452 969 controls) to identify variants that confer risk of ischemic stroke through altered endothelial function. We identified a variant in PDE3A (Phosphodiesterase 3A), encoding phosphodiesterase 3A, which was associated with flow-mediated dilatation in adolescents (9-12 years of age; β[SE], 0.38 [0.070]; P=3.8×10-8) and confers risk of ischemic stroke (odds ratio, 1.04 [95% CI, 1.02-1.06]; P=5.2×10-6). Bayesian colocalization analyses showed the same underlying variation is likely to lead to both associations (posterior probability, 97%). The same variant was associated with flow-mediated dilatation in a second study in young adults (age, 24-27 years; β[SE], 0.47 [0.23]; P=0.047) but not in older adults (β[SE], -0.012 [0.13]; P=0.89). We conclude that a genetic variant in PDE3A influences endothelial function in early life and leads to increased risk of ischemic stroke. Subtle, measurable changes to the vasculature that are influenced by genetics also influence risk of ischemic stroke.

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.

Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits

Over the recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank (UKB) has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we used GWAS summary statistics derived from the UKB cohort to investigate functional mechanisms of pleiotropic effects across the human phenome. We find that highly pleiotropic variants often correspond to broadly expressed genes with ubiquitous functions, such as matrisome components and cell growth regulators; and tend to colocalize with tissue-shared eQTLs. At the same time, signaling pathway components are more prevalent among highly pleiotropic genes compared to regulatory proteins such as transcription factors. Our results suggest that protein-level pleiotropy mediated by ubiquitously expressed genes is the most prevalent mechanism of pleiotropic genetic effects across the human phenome.

Contribution of Known Genetic Risk Variants to Dyslipidemias and Type 2 Diabetes in Mexico: A Population-Based Nationwide Study

Dyslipidemias are common risk factors for the development of chronic disorders including type 2 diabetes (T2D). Over 100 associated loci have been identified but few reports have evaluated the population attributable fraction captured by them in population-based nationwide surveys. Therefore, we determined the population contribution of a set of known genetic risk variants to the development of dyslipidemias and T2D in Mexico. This study included 1665 participants from a Mexican National Health Survey carried out in the year 2000. It is a probabilistic complex sample survey of households, which comprises representative data at a national level. 103 previously reported SNPs associated with different dyslipidemias or T2D were genotyped and used to compute polygenic risk scores. We found that the previously known variants associated with dyslipidemias explain at most 7% of the total risk variance of lipid levels. In contrast, the known genetic risk component for T2D explained a negligible amount of variance (0.1%). Notably, variants derived from the Native-American ancestry have the strongest effect and contribute with a high proportion of the variance. These results support the need for additional studies aimed to identify specific genetic risk variants for Mexican population.

Novel Approaches for Identifying the Molecular Background of Schizophrenia

Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported.

Single-cell transcriptomics from human pancreatic islets: sample preparation matters

Single-cell RNA sequencing (scRNA-seq) of human primary tissues is a rapidly emerging tool for investigating human health and disease at the molecular level. However, optimal processing of solid tissues presents a number of technical and logistical challenges, especially for tissues that are only available at autopsy, which includes pancreatic islets, a tissue that is highly relevant to diabetes. To assess the possible effects of different sample preparation protocols on fresh islet samples, we performed a detailed comparison of scRNA-seq data generated with islets isolated from a human donor but processed according to four treatment strategies, including fixation and cryopreservation. We found significant and reproducible differences in the proportion of cell types identified, and more minor effects on cell-specific patterns of gene expression. Fresh islets from a second donor confirmed gene expression signatures of alpha and beta subclusters. These findings may well apply to other tissues, emphasizing the need for careful consideration when choosing processing methods, comparing results between different studies, and/or interpreting data in the context of multiple cell types from preserved tissue.

Identifying causal variants and genes using functional genomics in specialized cell types and contexts

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

Potential Role of Gene Regulator NFAT5 in the Pathogenesis of Diabetes Mellitus

Nuclear factor of activated T cells 5 (NFAT5), a Rel/nuclear factor- (NF-) κB family member, is the only known gene regulator of the mammalian adaptive response to osmotic stress. Exposure to elevated glucose increases the expression and nuclear translocation of NFAT5, as well as NFAT5-driven transcriptional activity in vivo and in vitro. Increased expression of NFAT5 is closely correlated with the progression of diabetes in patients. The distinct structure of NFAT5 governs its physiological and pathogenic roles, indicating its opposing functions. The ability of NFAT5 to maintain cell homeostasis and proliferation is impaired in patients with diabetes. NFAT5 promotes the formation of aldose reductase, pathogenesis of diabetic vascular complications, and insulin resistance. Additionally, NFAT5 activates inflammation at a very early stage of diabetes and induces persistent inflammation. Recent studies revealed that NFAT5 is an effective therapeutic target for diabetes. Here, we describe the current knowledge about NFAT5 and its relationship with diabetes, focusing on its diverse regulatory functions, and highlight the importance of this protein as a potential therapeutic target in patients with diabetes.

Ten Years of the International Parkinson Disease Genomics Consortium: Progress and Next Steps

In June 2009 a small group of investigators met at the annual Movement Disorders Society meeting in Paris. The explicit goal of this meeting was to discuss a potential research alliance focused on the genetics of Parkinson disease (PD). The outcome of this informal meeting was the creation of the International Parkinson Disease Genomics Consortium (IPDGC), a group focused on collaborative genetics research, enabled by trust, sharing, and as little paperwork as possible. The IPDGC has grown considerably since its inception, including over 100 scientists from around the World. The focus has also grown, to include clinical and functional investigation of PD at scale. Most recently, the IPDGC has expanded to initiate major research efforts in East Asia and Africa, and has prioritized collaborations with ongoing major efforts in India and South America. Here we summarize the efforts of the IPDGC thus far and place these in the context of a decade of progress in PD genomics. We also discuss the future direction of IPDGC and our stated research priorities for the next decade.

A brief history of human disease genetics

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

Genetic Variants Predisposing Most Strongly to Type 1 Diabetes Diagnosed Under Age 7 Years Lie Near Candidate Genes That Function in the Immune System and in Pancreatic β-Cells

OBJECTIVE

Immunohistological analyses of pancreata from patients with type 1 diabetes suggest distinct autoimmune islet β-cell pathology between those diagnosed at <7 years (<7 group) and those diagnosed at age ≥13 years (≥13 group), with both B- and T-lymphocyte islet inflammation common in children in the <7 group, whereas B cells are rare in the ≥13 group. Based on these observations, we sought to identify differences in genetic susceptibility between these prespecified age-at-diagnosis groups to inform on the etiology of the most aggressive form of type 1 diabetes that initiates in the first years of life.

RESEARCH DESIGN AND METHODS

Using multinomial logistic regression models, we tested if known type 1 diabetes loci (17 within the HLA and 55 non-HLA loci) had significantly stronger effect sizes in the <7 group compared with the ≥13 group, using genotype data from 27,071 individuals (18,485 control subjects and 3,121 case subjects diagnosed at <7 years, 3,757 at 7-13 years, and 1,708 at ≥13 years).

RESULTS

Six HLA haplotypes/classical alleles and six non-HLA regions, one of which functions specifically in β-cells (GLIS3) and the other five likely affecting key T-cell (IL2RA, IL10, IKZF3, and THEMIS), thymus (THEMIS), and B-cell development/functions (IKZF3 and IL10) or in both immune and β-cells (CTSH), showed evidence for stronger effects in the <7 group.

CONCLUSIONS

A subset of type 1 diabetes-associated variants are more prevalent in children diagnosed under the age of 7 years and are near candidate genes that act in both pancreatic β- and immune cells.

Pleiotropic Effects of a KCNQ1 Variant on Lipid Profiles and Type 2 Diabetes: A Family-Based Study in China

OBJECTIVE

The genetic variant rs2237895, located in the Potassium Voltage-Gated Channel Subfamily Q Member 1 (KCNQ1) gene, has been replicated to be associated with type 2 diabetes mellitus (T2DM) susceptibility, but the relationship with lipids is conflicting. Furthermore, the common genetic predisposition to T2DM and lipids was not fully detected.

METHODS

In total, 5839 individuals (2220 were T2DM patients) across 2885 families were included. The effect of rs2237895 on T2DM and lipids was estimated using linear regression and logistic regression models after adjustment for multiple covariates. Mediation analysis was then used to test whether KCNQ1 participated in T2DM pathogenesis via lipid-mediated pathways.

RESULTS

Per allele-C of rs2237895 was associated with 17% (11-23%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%.

CONCLUSION

KCNQ1 had pleiotropic effects on lipids and T2DM, and the unexpected genetic effect on association of HDL-C with T2DM was observed, indicating the different pathways to lipids and T2DM. Further research studies are needed to verify potential biological mechanisms.

Understanding the genetics of neuropsychiatric disorders: the potential role of genomic regulatory blocks

Recent genome-wide association studies have identified numerous loci associated with neuropsychiatric disorders. The majority of these are in non-coding regions, and are commonly assigned to the nearest gene along the genome. However, this approach neglects the three-dimensional organisation of the genome, and the fact that the genome contains arrays of extremely conserved non-coding elements termed genomic regulatory blocks (GRBs), which can be utilized to detect genes under long-range developmental regulation. Here we review a GRB-based approach to assign loci in non-coding regions to potential target genes, and apply it to reanalyse the results of one of the largest schizophrenia GWAS (SWG PGC, 2014). We further apply this approach to GWAS data from two related neuropsychiatric disorders-autism spectrum disorder and bipolar disorder-to show that it is applicable to developmental disorders in general. We find that disease-associated SNPs are overrepresented in GRBs and that the GRB model is a powerful tool for linking these SNPs to their correct target genes under long-range regulation. Our analysis identifies novel genes not previously implicated in schizophrenia and corroborates a number of predicted targets from the original study. The results are available as an online resource in which the genomic context and the strength of enhancer-promoter associations can be browsed for each schizophrenia-associated SNP.

Genetic factors and risk of type 2 diabetes among women with a history of gestational diabetes: findings from two independent populations

OBJECTIVE

Women with a history of gestational diabetes mellitus (GDM) have an exceptionally high risk for type 2 diabetes (T2D). Yet, little is known about genetic determinants for T2D in this population. We examined the association of a genetic risk score (GRS) with risk of T2D in two independent populations of women with a history of GDM and how this association might be modified by non-genetic determinants for T2D.

RESEARCH DESIGN AND METHODS

This cohort study included 2434 white women with a history of GDM from the Nurses' Health Study II (NHSII, n=1884) and the Danish National Birth Cohort (DNBC, n=550). A GRS for T2D was calculated using 59 candidate single nucleotide polymorphisms for T2D identified from genome-wide association studies in European populations. An alternate healthy eating index (AHEI) score was derived to reflect dietary quality after the pregnancy affected by GDM.

RESULTS

Women on average were followed for 21 years in NHSII and 13 years in DNBC, during which 446 (23.7%) and 155 (28.2%) developed T2D, respectively. The GRS was generally positively associated with T2D risk in both cohorts. In the pooled analysis, the relative risks (RRs) for increasing quartiles of GRS were 1.00, 0.97, 1.25 and 1.19 (p trend=0.02). In both cohorts, the association appeared to be stronger among women with poorer (AHEI <median) than better dietary quality (AHEI ≥median), although the interaction was not significant. For example, in NHSII, the RRs across increasing quartiles of GRS were 1.00, 0.99, 1.51 and 1.29 (p trend=0.06) among women with poorer dietary quality and 1.00, 0.83, 0.81 and 0.94 (p trend=0.79) among women with better dietary quality (p interaction=0.11).

CONCLUSIONS

Among white women with a history of GDM, higher GRS for T2D was associated with an increased risk of T2D.

Phenotypic and Molecular Characterization of Risk Loci Associated With Asthma and Lung Function

Purpose

Respiratory diseases have a highly multifactorial etiology where different mechanisms contribute to the individual's susceptibility. We conducted a deep characterization of loci associated with asthma and lung function by previous genome-wide association studies (GWAS).

Methods

Sixteen variants were selected from previous GWAS of childhood/adult asthma and pulmonary function tests. We conducted a phenome-wide association study of these loci in 4,083 traits assessed in the UK Biobank (n = 361,194 participants). Data from the Genotype-Tissue Expression (GTEx) project were used to conduct a transcriptomic analysis with respect to tissues relevant for asthma pathogenesis. A pediatric cohort assessed with the International Study of Asthma and Allergies in Children (ISAAC) Phase II tools was used to further explore the association of these variants with 116 traits related to asthma comorbidities.

Results

Our phenome-wide association studies (PheWAS) identified 206 phenotypic associations with respect to the 16 variants identified. In addition to the replication of the phenotypes tested in the discovery GWAS, we observed novel associations related to blood levels of immune cells (eosinophils, neutrophils, monocytes, and lymphocytes) for the asthma-related variants. Conversely, the lung-function variants were associated with phenotypes related to body fat mass. In the ISAAC-assessed cohort, we observed that risk alleles associated with increased fat mass can exacerbate allergic reactions in individuals affected by allergic respiratory diseases. The GTEx-based analysis showed that the variants tested affect the transcriptomic regulation of multiple surrounding genes across several tissues.

Conclusions

This study generated novel data regarding the genetics of respiratory diseases and their comorbidities, providing a deep characterization of loci associated with asthma and lung function.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

A causal relationship between cigarette smoking and type 2 diabetes mellitus: A Mendelian randomization study

The causality between smoking and type 2 diabetes is unclear. We conducted a two-sample Mendelian randomization study to explore the causal relationship between smoking initiation and type 2 diabetes. Summary-level data for type 2 diabetes were obtained from a meta-analysis of 32 genome-wide association studies (DIAbetes Genetics Replication And Meta-analysis consortium), which included 898 130 individuals of European ancestry. Totally, 377 single-nucleotide polymorphisms associated with smoking initiation at genome wide significance threshold (p < 5 × 10^-8) were identified from the hitherto largest genome-wide association study on smoking. The inverse-variance weighted, weighted median, MR-Egger regression, and MR-PRESSO approaches were used to analyze the data. Genetically predicted smoking initiation was associated with type 2 diabetes with an odds ratio of 1.28 (95% confidence interval, 1.20, 1.37; p = 2.35 × 10^-12). Results were consistent across sensitivity analyses and there was no evidence of horizontal pleiotropy. This study provides genetic evidence supporting a causal association between the smoking initiation and type 2 diabetes. Reducing cigarette smoking initiation can now be even more strongly recommended for type 2 diabetes prevention.

Insights into malaria susceptibility using genome-wide data on 17,000 individuals from Africa, Asia and Oceania

The human genetic factors that affect resistance to infectious disease are poorly understood. Here we report a genome-wide association study in 17,000 severe malaria cases and population controls from 11 countries, informed by sequencing of family trios and by direct typing of candidate loci in an additional 15,000 samples. We identify five replicable associations with genome-wide levels of evidence including a newly implicated variant on chromosome 6. Jointly, these variants account for around one-tenth of the heritability of severe malaria, which we estimate as ~23% using genome-wide genotypes. We interrogate available functional data and discover an erythroid-specific transcription start site underlying the known association in ATP2B4, but are unable to identify a likely causal mechanism at the chromosome 6 locus.  Previously reported HLA associations do not replicate in these samples. This large dataset will provide a foundation for further research on the genetic determinants of malaria resistance in diverse populations.

Four genome-wide associated loci are currently known for malaria susceptibility. Here, the authors expand on earlier work by combining data from 11 malaria-endemic countries and additional population sequencing informing an African-enriched imputation reference panel, with findings including a previously unreported association on chromosome 6.