Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

Genetic Landscape of the ACE2 Coronavirus Receptor.

BACKGROUND: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases.

BACKGROUND: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. METHODS: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. RESULTS: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). CONCLUSION: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development.

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. The pathogenesis of AF remains poorly understood, which contributes to the current lack of highly effective treatments. To understand the genetic variation and biology underlying AF, we undertook a genome-wide association study (GWAS) of 6,337 AF individuals and 61,607 AF-free individuals from Norway, including replication in an additional 30,679 AF individuals and 278,895 AF-free individuals. Through genotyping and dense imputation mapping from whole-genome sequencing, we tested almost nine million genetic variants across the genome and identified seven risk loci, including two novel loci. One novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 × 10-18) comprised intronic and several highly correlated missense variants situated in the I-, A-, and M-bands of titin, which is the largest protein in humans and responsible for the passive elasticity of heart and skeletal muscle. The other novel locus (lead SNV rs56202902; p = 1.54 × 10-11) covered a large, gene-dense chromosome 1 region that has previously been linked to cardiac conduction. Pathway and functional enrichment analyses suggested that many AF-associated genetic variants act through a mechanism of impaired muscle cell differentiation and tissue formation during fetal heart development.

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Genetic studies of body mass index yield new insights for obesity biology.

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts.

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based data sets in quantifying the specific risk of individual variants for disease-related phenotypes.

A genome-wide association study of IgM antibody against phosphorylcholine: shared genetics and phenotypic relationship to chronic lymphocytic leukemia.

Phosphorylcholine (PC) is an epitope on oxidized low-density lipoprotein (oxLDL), apoptotic cells and several pathogens like Streptococcus pneumoniae. Immunoglobulin M against PC (IgM anti-PC) has the ability to inhibit uptake of oxLDL by macrophages and increase clearance of apoptotic cells. From our genome-wide association studies (GWASs) in four European-ancestry cohorts, six single nucleotide polymorphisms (SNPs) in 11q24.1 were discovered (in 3002 individuals) and replicated (in 646 individuals) to be associated with serum level of IgM anti-PC (the leading SNP rs35923643-G, combined ß = 0.19, 95% confidence interval 0.13-0.24, P = 4.3 × 10-11). The haplotype tagged by rs35923643-G (or its proxy SNP rs735665-A) is also known as the top risk allele for chronic lymphocytic leukemia (CLL), and a main increasing allele for general IgM. By using summary GWAS results of IgM anti-PC and CLL in the polygenic risk score (PRS) analysis, PRS on the basis of IgM anti-PC risk alleles positively associated with CLL risk (explained 0.6% of CLL variance, P = 1.2 × 10-15). Functional prediction suggested that rs35923643-G might impede the binding of Runt-related transcription factor 3, a tumor suppressor playing a central role in the immune regulation of cancers. Contrary to the expectations from the shared genetics between IgM anti-PC and CLL, an inverse relationship at the phenotypic level was found in a nested case-control study (30 CLL cases with 90 age- and sex-matched controls), potentially reflecting reverse causation. The suggested function of the top variant as well as the phenotypic association between IgM anti-PC and CLL risk needs replication and motivates further studies.

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.

Body composition and atrial fibrillation: a Mendelian randomization study.

AIMS: Increases in fat-free mass and fat mass have been associated with higher risk of atrial fibrillation (AF) in observational studies. It is not known whether these associations reflect independent causal processes. Our aim was to evaluate independent causal roles of fat-free mass and fat mass on AF. METHODS AND RESULTS: We conducted a large observational study to estimate the associations between fat-free mass and fat mass on incident AF in the UK Biobank (N = 487 404, N events = 10 365). Genome-wide association analysis was performed to obtain genetic instruments for Mendelian randomization (MR). We evaluated the causal effects of fat-free mass and fat mass on AF with two-sample method by using genetic associations from AFGen consortium as outcome. Finally, we evaluated independent causal effects of fat-free mass and fat mass with multivariate MR. Both fat-free mass and fat mass had observational associations with incident AF [hazard ratio (HR) = 1.77, 95% confidence interval (CI) 1.72-1.83; HR = 1.40, 95% CI 1.37-1.43 per standard deviation increase in fat-free and fat mass, respectively]. The causal effects using the inverse-variance weighted method were 1.55 (95% CI 1.38-1.75) for fat-free mass and 1.30 (95% CI 1.17-1.45) for fat mass. Weighted median, Egger regression, and penalized methods showed similar estimates. The multivariate MR analysis suggested that the causal effects of fat-free and fat mass were independent of each other (causal risk ratios: 1.37, 95% CI 1.06-1.75; 1.28, 95% CI 1.03-1.58). CONCLUSION: Genetically programmed increases in fat-free mass and fat mass independently cause an increased risk of AF.

No evidence of a causal association of type 2 diabetes and glucose metabolism with atrial fibrillation.

AIMS/HYPOTHESIS: Several epidemiological studies have shown an increased risk of atrial fibrillation in individuals with type 2 diabetes or milder forms of dysglycaemia. We aimed to assess whether this relation is causal using a Mendelian randomisation approach. METHODS: Two-sample Mendelian randomisation was used to obtain estimates of the influence of type 2 diabetes, fasting blood glucose (FBG), and HbA1c on the risk of atrial fibrillation. Instrumental variables were constructed using available summary statistics from meta-analyses of genome-wide association studies (GWAS) for type 2 diabetes and associated phenotypes. Pleiotropic SNPs were excluded from the analyses. The most recent GWAS meta-analysis summary statistics for atrial fibrillation, which included over 1 million individuals (approximately 60,000 individuals with atrial fibrillation) was used for outcome analysis. RESULTS: Neither type 2 diabetes (OR 1.01 [95% CI 0.98, 1.03]; p = 0.37), nor FBG (OR 0.95 [95% CI 0.82, 1.09] per mmol/l; p = 0.49) or HbA1c (OR 1.01 [95% CI, 0.85, 1.17] per mmol/mol [%]; p = 0.88) were associated with atrial fibrillation in Mendelian randomisation analyses. We had >80% statistical power to detect ORs of 1.08, 1.06 and 1.09 or larger for type 2 diabetes, FBG and HbA1c, respectively, for associations with atrial fibrillation. CONCLUSIONS/INTERPRETATION: This Mendelian randomisation analysis does not support a causal role of clinical significance between genetically programmed type 2 diabetes, FBG or HbA1c and development of atrial fibrillation. These data suggest that drug treatment to reduce dysglycaemia is unlikely to be an effective strategy for atrial fibrillation prevention. DATA AVAILABILITY: The datasets analysed during the current study are available from the following repository: Nielsen JB, Thorolfsdottir RB, Fritsche LG, et al (2018) GWAS summary statistics for AF (N=60,620 AF cases and 970,216 controls). Center for Statistical Genetics: http://csg.sph.umich.edu/willer/public/afib2018/nielsen-thorolfsdottir-willer-NG2018-AFib-gwas-summary-statistics.tbl.gz.

Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study.

BACKGROUND: Observational studies have shown inverse associations among fitness, physical activity, and cardiovascular disease. However, little is known about these associations in individuals with elevated genetic susceptibility for these diseases. METHODS: We estimated associations of grip strength, objective and subjective physical activity, and cardiorespiratory fitness with cardiovascular events and all-cause death in a large cohort of 502 635 individuals from the UK Biobank (median follow-up, 6.1 years; interquartile range, 5.4-6.8 years). Then we further examined these associations in individuals with different genetic burden by stratifying individuals based on their genetic risk scores for coronary heart disease and atrial fibrillation. We compared disease risk among individuals in different tertiles of fitness, physical activity, and genetic risk using lowest tertiles as reference. RESULTS: Grip strength, physical activity, and cardiorespiratory fitness showed inverse associations with incident cardiovascular events (coronary heart disease: hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.77-0.81; HR, 0.95; 95% CI, 0.93-0.97; and HR, 0.68; 95% CI, 0.63-0.74, per SD change, respectively; atrial fibrillation: HR, 0.75; 95% CI, 0.73-0.76; HR, 0.93; 95% CI, 0.91-0.95; and HR, 0.60; 95% CI, 0.56-0.65, per SD change, respectively). Higher grip strength and cardiorespiratory fitness were associated with lower risk of incident coronary heart disease and atrial fibrillation in each genetic risk score group (Ptrend <0.001 in each genetic risk category). In particular, high levels of cardiorespiratory fitness were associated with 49% lower risk for coronary heart disease (HR, 0.51; 95% CI, 0.38-0.69) and 60% lower risk for atrial fibrillation (HR, 0.40; 95%, CI 0.30-0.55) among individuals at high genetic risk for these diseases. CONCLUSIONS: Fitness and physical activity demonstrated inverse associations with incident cardiovascular disease in the general population, as well as in individuals with elevated genetic risk for these diseases.

Clinical and Genetic Determinants of Varicose Veins.

BACKGROUND: Varicose veins are a common problem with no approved medical therapies. Although it is believed that varicose vein pathogenesis is multifactorial, there is limited understanding of the genetic and environmental factors that contribute to their formation. Large-scale studies of risk factors for varicose veins may highlight important aspects of pathophysiology and identify groups at increased risk for disease. METHODS: We applied machine learning to agnostically search for risk factors of varicose veins in 493 519 individuals in the UK Biobank. Predictors were further studied with univariable and multivariable Cox regression analyses (2441 incident events). A genome-wide association study of varicose veins was also performed among 337 536 unrelated individuals (9577 cases) of white British descent, followed by expression quantitative loci and pathway analyses. Because height emerged as a new candidate risk factor, we performed mendelian randomization analyses to assess a potential causal role for height in varicose vein development. RESULTS: Machine learning confirmed several known (age, sex, obesity, pregnancy, history of deep vein thrombosis) and identified several new risk factors for varicose vein disease, including height. After adjustment for traditional risk factors in Cox regression, greater height remained independently associated with varicose veins (hazard ratio for upper versus lower quartile, 1.74; 95% CI, 1.51-2.01; P<0.0001). A genome-wide association study identified 30 new genome-wide significant loci, identifying pathways involved in vascular development and skeletal/limb biology. Mendelian randomization analysis provided evidence that increased height is causally related to varicose veins (inverse-variance weighted: odds ratio, 1.26; P=2.07×10-16). CONCLUSIONS: Using data from nearly a half-million individuals, we present a comprehensive genetic and epidemiological study of varicose veins. We identified novel clinical and genetic risk factors that provide pathophysiological insights and could help future improvements of treatment of varicose vein disease.

Identification of 22 novel loci associated with urinary biomarkers of albumin, sodium, and potassium excretion.

Urine biomarkers reflecting kidney function and handling of dietary sodium and potassium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease, and diabetes mellitus. Knowledge about the genetic determinants of these biomarkers may shed light on pathophysiological mechanisms underlying the development of these diseases. We performed genome-wide association studies of urinary albumin: creatinine ratio (UACR), urinary potassium: creatinine ratio (UK/UCr), urinary sodium: creatinine ratio (UNa/UCr) and urinary sodium: potassium ratio (UNa/UK) in up to 218,450 (discovery) and 109,166 (replication) unrelated individuals of European ancestry from the UK Biobank. Further, we explored genetic correlations, tissue-specific gene expression, and possible genes implicated in the regulation of these biomarkers. After replication, we identified 19 genome-wide significant independent loci associated with UACR, 6 each with UK/UCr and UNa/UCr, and 4 with UNa/UK. In addition to 22 novel associations, we confirmed several established associations, including between the CUBN locus and microalbuminuria. We detected high pairwise genetic correlation across the urinary biomarkers, and between their levels and several physiological measurements. We highlight GIPR, a potential diabetes drug target, as possibly implicated in the genetic control of urinary potassium excretion, and NRBP1, a locus associated with gout, as plausibly involved in sodium and albumin excretion. Overall, we identified 22 novel genome-wide significant associations with urinary biomarkers and confirmed several previously established associations, providing new insights into the genetic basis of these traits and their connection to chronic diseases.

Associations of Circulating Protein Levels With Lipid Fractions in the General Population.

Objective- Revealing patterns of associations between circulating protein and lipid levels could improve biological understanding of cardiovascular disease (CVD). In this study, we investigated the associations between proteins related to CVD and triglyceride (TG), total cholesterol, LDL (low-density lipoprotein), and HDL (high-density lipoprotein) cholesterol levels in individuals from the general population. Approach and Results- We measured plasma protein levels using the Olink ProSeek CVD I or II+III arrays and analyzed 57 proteins available in 3 population-based cohorts: EpiHealth (n=2029; 52% women; median age, 61 years), PIVUS (Prospective Study of the Vasculature in Uppsala Seniors; n=790; 51% women; all aged 70 years), and ULSAM (Uppsala Longitudinal Study of Adult Men; n=551; all men aged 77 years). A discovery analysis was performed in EpiHealth in a regression framework (adjusted for sex, age, body mass index, smoking, glucose levels, systolic blood pressure, blood pressure medication, diabetes mellitus medication, and CVD history), and associations with false discovery rate <0.05 were further tested in PIVUS and ULSAM, where a P value of 0.05 was considered a successful replication (validation false discovery rate of 0.1%). We used summary statistics from a genome-wide association study on each protein biomarker (meta-analysis of EpiHealth, PIVUS, ULSAM, and IMPROVE [Carotid Intima-Media Thickness and IMT-Progression as Predictors of Vascular Events in a High-Risk European Population]) and publicly available data from Global Lipids Genetics Consortium to perform Mendelian randomization analyses to address possible causality of protein levels. Of 57 tested proteins, 42 demonstrated an association with at least 1 lipid fraction; 35 were associated with TG, 15 with total cholesterol, 9 with LDL cholesterol, and 24 with HDL cholesterol. Among these associations, we found KIM-1 (kidney injury molecule-1), TNFR (TNF [tumor necrosis factor] receptor) 1 and 2, TRAIL-R2 (TRAIL [TNF-related apoptosis-inducing ligand] receptor 2), and RETN (resistin) to be associated with all 4 lipid fractions. Further, 15 proteins were related to both TG and HDL cholesterol in a consistent and biologically expected manner, that is, higher TG and lower HDL cholesterol or vice versa. Another common pattern of associations was concomitantly higher TG, total cholesterol, and LDL cholesterol, which is associated with higher CVD risk. We did not find evidence of causal links for protein levels. Conclusions- Our comprehensive analysis of plasma proteins and lipid fractions of 3370 individuals from the general population provides new information about lipid metabolism.

Genome-wide association study of coronary artery disease among individuals with diabetes: the UK Biobank.

AIMS/HYPOTHESIS: Coronary artery disease (CAD) is a common complication among individuals with diabetes. A better understanding of the genetic background of CAD in this population has the potential to suggest novel molecular targets for screening, risk assessment and drug development. METHODS: We performed a genome-wide association study of CAD in 15,666 unrelated individuals (3,968 CAD cases and 11,698 controls) of white British ancestry with diabetes at inclusion in the UK Biobank study. Our results were compared with results from participants without diabetes. RESULTS: We found genome-wide significant evidence for association with CAD at the previously well-established LPA locus (lead variant: rs74617384; OR 1.38 [95% CI 1.26, 1.51], p = 3.2 × 10-12) and at 9p21 (lead variant: rs10811652; OR 1.19 [95% CI 1.13, 1.26], p = 6.0 × 10-11). Moreover, other variants previously associated with CAD showed similar effects in the participants with and without diabetes, indicating that the genetic architecture of CAD is largely the same. CONCLUSIONS/INTERPRETATION: Our results indicate large similarities between the genetic architecture of CAD in participants with and without diabetes. Larger studies are needed to establish whether there are important diabetes-specific CAD loci.

Genome-wide DNA methylation study identifies genes associated with the cardiovascular biomarker GDF-15.

Growth-differentiation factor 15 (GDF-15) is expressed in low to moderate levels in most healthy tissues and increases in response to inflammation. GDF-15 is associated with cardiovascular dysfunction and over-expressed in the myocardium of patients with myocardial infarction (MI). However, little is known about the function of GDF-15 in cardiovascular disease, and the underlying regulatory network of GDF-15 is not known. To investigate a possible association between GDF-15 levels and DNA methylation, we performed a genome-wide DNA methylation study of white blood cells in a population-based study (N = 717). Significant loci where replicated in an independent cohort (N = 963). We also performed a gene ontology (GO) enrichment analysis. We identified and replicated 16 CpG-sites (false discovery rate [FDR] < 0.05), at 11 independent loci including MIR21. MIR21 encodes a microRNA (miR-21) that has previously been shown to be associated with the development of heart disease. Interestingly, GDF15 mRNA contains a binding site for miR-21. Four sites were also differentially methylated in blood from participants previously diagnosed with MI and 14 enriched GO terms (FDR < 0.05, enrichment > 2) were identified, including 'cardiac muscle cell differentiation'. This study shows that GDF-15 levels are associated with differences in DNA methylation in blood cells, and a subset of the loci are also differentially methylated in participants with MI. However, there might be interactions between GDF-15 levels and methylation in other tissues not addressed in this study. These results provide novel links between GDF-15 and cardiovascular disease.

Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior.

Caffeine is the most widely consumed psychoactive substance in the world and presents with wide interindividual variation in metabolism. This variation may modify potential adverse or beneficial effects of caffeine on health. We conducted a genome-wide association study (GWAS) of plasma caffeine, paraxanthine, theophylline, theobromine and paraxanthine/caffeine ratio among up to 9,876 individuals of European ancestry from six population-based studies. A single SNP at 6p23 (near CD83) and several SNPs at 7p21 (near AHR), 15q24 (near CYP1A2) and 19q13.2 (near CYP2A6) met GW-significance (P < 5 × 10-8) and were associated with one or more metabolites. Variants at 7p21 and 15q24 associated with higher plasma caffeine and lower plasma paraxanthine/caffeine (slow caffeine metabolism) were previously associated with lower coffee and caffeine consumption behavior in GWAS. Variants at 19q13.2 associated with higher plasma paraxanthine/caffeine (slow paraxanthine metabolism) were also associated with lower coffee consumption in the UK Biobank (n = 94 343, P < 1.0 × 10-6). Variants at 2p24 (in GCKR), 4q22 (in ABCG2) and 7q11.23 (near POR) that were previously associated with coffee consumption in GWAS were nominally associated with plasma caffeine or its metabolites. Taken together, we have identified genetic factors contributing to variation in caffeine metabolism and confirm an important modulating role of systemic caffeine levels in dietary caffeine consumption behavior. Moreover, candidate genes identified encode proteins with important clinical functions that extend beyond caffeine metabolism.