The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR–Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Rapid progress of the discovery of genetic loci associated with common, complex diseases has outpaced the elucidation of mechanisms pertinent to disease pathogenesis. To address relevant barriers for coronary artery disease (CAD), we combined genetic discovery analyses with downstream characterization of likely causal variants, genes, and biological pathways. Specifically, we conducted a genome-wide association study (GWAS) comprising 181,522 cases of CAD among 1,165,690 participants. We detected 241 associations, including 54 associations and 30 loci not previously linked to CAD. Next, we prioritized likely causal variants using functionally-informed fine-mapping, yielding 42 associations with fewer than five variants in the 95% credible set. Combining eight complementary predictors, we prioritized 185 candidate causal genes, including 94 genes supported by three or more predictors. Similarity-based clustering underscored a role for early developmental processes, cell cycle signaling, and vascular proliferation in the pathogenesis of CAD. Our analysis identifies and systematically characterizes risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Investigators require intuitive tools to rationalize complex datasets generated by transcriptional profiling experiments. Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways to facilitate assessment of relative enrichment, lead investigators more effectively to biologically testable hypotheses. However, once a set of differentially expressed genes is isolated, pathway analysis approaches tend to ignore rich gene expression information and, moreover, do not exploit relationships between transcripts. In this article, we report the development of a new method in which both pathway topology and the magnitude of gene expression changes inform the scoring system, thereby providing a powerful filter in the enrichment of biologically relevant information. When four sample datasets were evaluated with this method, literature mining confirmed that those pathways germane to the physiological process under investigation were highlighted by our method relative to z-score overrepresentation calculations. Moreover, non-relevant processes were downgraded using the method described herein. The inclusion of expression and topological data in the calculation of a pathway regulation score (PRS) facilitated discrimination of key processes in real biological datasets. Specifically, by combining fold-change data for those transcripts exceeding a significance threshold, and by taking into account the potential for altered gene expression to impact upon downstream transcription, one may readily identify those pathways most relevant to pathophysiological processes.

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Background: It is well established that decreased kidney function can increase blood pressure (BP), but it is unproven whether moderately elevated BP causes chronic kidney disease (CKD) or glomerular hyperfiltration. Methods: Three hundred eleven thousand one hundred nineteen White British UK Biobank participants were included in logistic regression analyses to estimate the odds of CKD (defined as long-term kidney replacement therapy, estimated glomerular filtration rate [eGFR]<60 mL (min·1.73m 2 ), or urinary albumin:creatinine ratio ≥3 mg/mmol) associated with higher genetically predicted BP using genetic risk scores comprising 219 systolic and 223 diastolic BP loci. Analyses estimating associations with clinical categories of eGFR and urinary albumin:creatinine ratio were also conducted, with an eGFR ≥120 mL (min·1.73m 2 ) considered evidence of glomerular hyperfiltration. Results: Twenty-one thousand six hundred twenty-three participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. One thousand eight hundred twenty-eight participants had an eGFR ≥120 mL (min·1.73m 2 ). Each genetically predicted 10 mm Hg higher systolic BP and 5 mm Hg higher diastolic BP were associated with a 37% (95% CI, 1.29–1.45) and 19% (1.14–1.25) higher odds of CKD, respectively. Associations were evident for both the reduced eGFR and albuminuria components of the CKD outcome. The odds of hyperfiltration (versus an eGFR ≥60 and <90 mL[min·1.73m2]) were 49% higher (95% CI, 1.21–1.84) for each genetically predicted 10 mm Hg higher systolic BP. Associations with CKD and hyperfiltration were similar irrespective of preexisting diabetes, vascular disease, or different levels of adiposity. Conclusions: In this general population, genetic epidemiological evidence supports a causal role of life-long differences in BP for decreased kidney function, glomerular hyperfiltration, and albuminuria. Physiological autoregulation may not afford complete renal protection against the moderate BP elevations.