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Multiple germline and somatic genomic factors are associated with risk of coronary artery disease, but there is no single measure of risk that integrates all information from a DNA sample. To address this gap, we develop an integrated genomic model that includes six germline and somatic genetic drivers for coronary artery disease, including polygenic risk score, genetically-proxied proteomic/metabolomic risk scores, and clonal hematopoiesis of indeterminate potential. We evaluated its predictive power in the UK Biobank (N = 391,536), and validate it using data from the TOPMed program (N = 34,177). The 10-year coronary artery disease risk based on the integrated genomic model profile ranges from 1.1% to 15.5% in the UK Biobank and from 3.8% to 33.0% in TOPMed, with a more pronounced gradient in males than females. The integrated genomic model captures the cumulative effect of multiple genetic drivers, identifying individuals at high risk for coronary artery disease despite lacking any single high-risk genetic factor, as well as individuals at low risk despite carrying known high-risk factors. In middle age, the integrated genomic model augments the performance of the Pooled Cohort Equations, a clinical risk calculator for coronary artery disease. While the integrated genomic model yields only modest incremental predictive value over polygenic risk score at the population level, it identifies approximately 13% of high-risk individuals not detected by polygenic risk score alone. Coronary artery disease has several genetic risk factors. Here, the authors develop a model that combines germline and somatic genetic drivers to predict coronary artery disease risk, identifying high-risk individuals not detected by polygenic risk scores alone.

Relative to European Americans, African Americans have lower 25-hydroxyvitamin D (25OHD) and vitamin D binding protein (VDBP) concentrations, higher 1,25-dihydroxyvitamin D (1,25(OH) 2 D 3 ) concentrations and bone mineral density (BMD), and paradoxically reduced burdens of calcified atherosclerotic plaque (subclinical atherosclerosis). To identify genetic factors contributing to vitamin D and BMD measures, association analysis of >14M variants was conducted in a maximum of 697 African American-Diabetes Heart Study participants with type 2 diabetes (T2D). The most significant association signals were detected for VDBP on chromosome 4; variants rs7041 (β = 0.44, SE = 0.019, P = 9.4x10 -86 ) and rs4588 (β = 0.17, SE = 0.021, P = 3.5x10 -08 ) in the group-specific component (vitamin D binding protein) gene ( GC ). These variants were found to be independently associated. In addition, rs7041 was also associated with bioavailable vitamin D (BAVD; β = 0.16, SE = 0.02, P = 3.3x10 -19 ). Six rare variants were significantly associated with 25OHD, including a non-synonymous variant in HSPG2 (rs116788687; β = -1.07, SE = 0.17, P = 2.2x10 -10 ) and an intronic variant in TNIK (rs143555701; β = -1.01, SE = 0.18, P = 9.0x10 -10 ), both biologically related to bone development. Variants associated with 25OHD failed to replicate in African Americans from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Evaluation of vitamin D metabolism and bone mineral density phenotypes in an African American population enriched for T2D could provide insight into ethnic specific differences in vitamin D metabolism and bone mineral density.

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases ( n  = 16,677) and controls ( n  = 33,856), followed by combined analyses with Europeans (n eff  = 231,420). We identify 21 novel genetic loci for significant association with T2D ( P  = 4.7 × 10 −8 to 5.2 × 10 −12 ), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B , PDHB , LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations. Marie Loh, Weihua Zhang et al. use a genome-wide association study meta-analysis to examine variants associated with Type 2 diabetes (T2D) in South Asian and European ancestry cohorts. Their results provide further insights into the genetic mechanisms underlying T2D across ancestral populations.

Background End-stage kidney disease (ESKD) is a significant public health concern disproportionately affecting African Americans (AAs). Type 2 diabetes (T2D) is the leading cause of ESKD in the USA, and efforts to uncover genetic susceptibility to diabetic kidney disease (DKD) have had limited success. A prior genome-wide association study (GWAS) in AAs with T2D-ESKD was expanded with additional AA cases and controls and genotypes imputed to the higher density 1000 Genomes reference panel. The discovery analysis included 3432 T2D-ESKD cases and 6977 non-diabetic non-nephropathy controls ( N  = 10,409), followed by a discrimination analysis in 2756 T2D non-nephropathy controls to exclude T2D-associated variants. Results Six independent variants located in or near RND3/RBM43 , SLITRK3 , ENPP7 , GNG7 , and APOL1 achieved genome-wide significant association ( P  < 5 × 10 −8 ) with T2D-ESKD. Following extension analyses in 1910 non-diabetic ESKD cases and 908 non-diabetic non-nephropathy controls, a meta-analysis of 5342 AA all-cause ESKD cases and 6977 AA non-diabetic non-nephropathy controls revealed an additional novel all-cause ESKD locus at EFNB2 (rs77113398; P  = 9.84 × 10 –9 ; OR = 1.94). Exclusion of APOL1 renal-risk genotype carriers identified two additional genome-wide significant T2D-ESKD-associated loci at GRAMD3 and MGAT4C . A second variant at GNG7 (rs373971520; P  = 2.17 × 10 –8 , OR = 1.46) remained associated with all-cause ESKD in the APOL1 -negative analysis. Conclusions Findings provide further evidence for genetic factors associated with advanced kidney disease in AAs with T2D.

To explore mechanisms whereby hepatic steatosis may be associated with cardiovascular risk, we investigated cross-sectional relationships between hepatic steatosis, regional fat accumulation, inflammatory biomarkers, and subclinical measures of atherosclerosis in the Diabetes Heart Study. The Diabetes Heart Study is a family study of sibling pairs concordant for type 2 diabetes. A subset of 623 randomly selected participants was evaluated for hepatic steatosis, defined as a liver:spleen attenuation ratio of <1.0 by computed tomography. We quantified visceral fat, subcutaneous fat, coronary, aortic, and carotid artery calcium by computed tomography; and carotid atherosclerosis by ultrasound. Associations between the liver:spleen attenuation ratio and these factors were expressed as Spearman correlations. After adjustment for age, race, gender, body mass index, and diabetes status, the liver:spleen attenuation ratio correlated with visceral fat (r =-0.22, P < 0.0001) and subcutaneous fat (r =-0.13, P= 0.031). Hepatic steatosis was associated with lower high-density lipoprotein (r = 0.21, P < 0.0001), higher triglycerides (r =-0.25, P < 0.0001), higher C-reactive protein (r =-0.095, P= 0.004), and lower serum adiponectin (r = 0.34, P < 0.0001). There were no significant associations between the liver:spleen attenuation ratio and coronary, aortic, or carotid calcium, or carotid intimal thickness. This suggests that hepatic steatosis is less likely a direct mediator of cardiovascular disease and may best be described as an epiphenomenon. The strong correlations between pro-atherogenic biomarkers, visceral fat, and elements of the metabolic syndrome suggest that hepatic steatosis reflects more than general adiposity, but represents a systemic, inflammatory, pro-atherogenic adipose state.

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.