Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants.

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.

Determining the Relationship Between Blood Pressure, Kidney Function, and Chronic Kidney Disease: Insights From Genetic Epidemiology.

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: 311 119 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]< 60mL/min/1.73m2, 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·1.73m2) considered evidence of glomerular hyperfiltration. RESULTS: 21 623 participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. 1828 participants had an eGFR ≥120 mL/min/1.73m2. Each genetically predicted 10 mmHg higher systolic BP and 5 mmHg 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 mmHg 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.

Assessment of the causal relevance of ECG parameters for risk of atrial fibrillation: A mendelian randomisation study.

BACKGROUND: Atrial electrical and structural remodelling in older individuals with cardiovascular risk factors has been associated with changes in surface electrocardiographic (ECG) parameters (e.g., prolongation of the PR interval) and higher risks of atrial fibrillation (AF). However, it has been difficult to establish whether altered ECG parameters are the cause or a consequence of the myocardial substrate leading to AF. This study aimed to examine the potential causal relevance of ECG parameters on risk of AF using mendelian randomisation (MR). METHODS AND FINDINGS: Weighted genetic scores explaining lifelong differences in P-wave duration, PR interval, and QT interval were constructed, and associations between these ECG scores and risk of AF were estimated among 278,792 UK Biobank participants (mean age: 57 years at recruitment; 19,132 AF cases). The independent genetic variants contributing to each of the separate ECG scores, and their corresponding weights, were based on published genome-wide association studies. In UK Biobank, genetic scores representing a 5 ms longer P-wave duration or PR interval were significantly associated with lower risks of AF (odds ratio [OR] 0.91; 95% confidence interval [CI]: 0.87-0.96, P = 2 × 10-4 and OR 0.94; 95% CI: 0.93-0.96, P = 2 × 10-19, respectively), while longer QT interval was not significantly associated with AF. These effects were independently replicated among a further 17,931 AF cases from the AFGen Consortium. Investigation of potential mechanistic pathways showed that differences in ECG parameters associated with specific ion channel genes had effects on risk of AF consistent with the overall scores, while the overall scores were not associated with changes in left atrial size. Limitations of the study included the inherent assumptions of MR, restriction to individuals of European ancestry, and possible restriction of results to the normal ECG ranges represented in UK Biobank. CONCLUSIONS: In UK Biobank, we observed evidence suggesting a causal relationship between lifelong differences in ECG parameters (particularly PR interval) that reflect longer atrial conduction times and a lower risk of AF. These findings, which appear to be independent of atrial size and concomitant cardiovascular comorbidity, support the relevance of varying mechanisms underpinning AF and indicate that more individualised treatment strategies warrant consideration.

Pathways Enrichment Analysis of Gene Expression Data in Type 2 Diabetes.

Profiling genome-wide transcriptional changes with advanced high-throughput transcriptional profiling techniques has led to a revolution in biomedical science. It has been challenging to handle the massive data generated by these techniques and draw meaningful conclusions from it. Therefore, computational biologists have developed a number of innovative methods of varying complexity and effectiveness to analyze such complex data. Over the past decade, rich information in pathway repositories has attracted and motivated researchers to incorporate such existing biological knowledge into computational analysis tools to develop what is known as pathway enrichment analysis tools. This chapter describes a new sophisticated pathway enrichment tool that exploits topology of pathway as well as expression of significantly changed genes to identify biologically significant pathways for high-dimensional gene expression datasets. Also, we demonstrate the use of this tool to analyze gene expression data from a type 2 diabetes dataset to identify a list of significantly enriched metabolic pathways.

Impact of ADCY9 Genotype on Response to Anacetrapib.

BACKGROUND: Exploratory analyses of previous randomized trials generated a hypothesis that the clinical response to cholesteryl ester transfer protein (CETP) inhibitor therapy differs by ADCY9 genotype, prompting the ongoing dal-GenE trial in individuals with a particular genetic profile. The randomized placebo-controlled REVEAL trial (Randomized Evaluation of the Effects of Anacetrapib through Lipid-Modification) demonstrated the clinical efficacy of the CETP inhibitor anacetrapib among patients with preexisting atherosclerotic vascular disease. In the present study, we examined the impact of ADCY9 genotype on response to anacetrapib in the REVEAL trial. METHODS: Individuals with stable atherosclerotic vascular disease who were treated with intensive atorvastatin therapy received either anacetrapib 100 mg daily or matching placebo. Cox proportional hazards models, adjusted for the first 5 principal components of ancestry, were used to estimate the effects of allocation to anacetrapib on major vascular events (a composite of coronary death, myocardial infarction, coronary revascularization, or presumed ischemic stroke) and the interaction with ADCY9 rs1967309 genotype. RESULTS: Among 19 210 genotyped individuals of European ancestry, 2504 (13.0%) had a first major vascular event during 4 years median follow-up: 1216 (12.6%) among anacetrapib-allocated participants and 1288 (13.4%) among placebo-allocated participants. Proportional reductions in the risk of major vascular events with anacetrapib did not differ significantly by ADCY9 genotype: hazard ratio (HR) = 0.92 (95% CI, 0.81-1.05) for GG; HR = 0.94 (95% CI, 0.84-1.06) for AG; and HR = 0.93 (95% CI, 0.76-1.13) for AA genotype carriers, respectively; genotypic P for interaction = 0.96. Furthermore, there were no associations between ADCY9 genotype and the proportional reductions in the separate components of major vascular events or meaningful differences in lipid response to anacetrapib. CONCLUSIONS: The REVEAL trial is the single largest study to date evaluating the ADCY9 pharmacogenetic interaction. It provides no support for the hypothesis that ADCY9 genotype is materially relevant to the clinical effects of the CETP inhibitor anacetrapib. The ongoing dal-GenE study will provide direct evidence as to whether there is any specific pharmacogenetic interaction with dalcetrapib. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov. Unique identifier: NCT01252953. URL: http://www.isrctn.com. Unique identifier: ISRCTN48678192. URL: https://www.clinicaltrialsregister.eu. Unique identifier: 2010-023467-18.

A MATLAB tool for pathway enrichment using a topology-based pathway regulation score.

BACKGROUND: Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes (J Comput Biol 19:563-573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI). RESULTS: Using input from a variety of microarray platforms and species, users are able to calculate PRS scores, along with a corresponding z-score for comparison. Further pathway significance assessment may be performed to increase confidence in the pathways obtained, and users can view Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams marked-up to highlight impacted genes. CONCLUSIONS: The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.

A topology-based score for pathway enrichment.

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.