Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets.

Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including MYH6, CKM, NKX2-5 and ATP2A2, were among the top 50 DEGs. Our meta-analysis also identified 39 new DEGs that were not detected using those individual RNA-seq datasets. Some of those genes, including PTH1R, ADAM15 and S100A4, confirmed previous reports of associations with cardiovascular functions. Using DEGs from this meta-analysis, the Ingenuity Pathway Analysis (IPA) identified five activated toxicity pathways, including failure of heart as the most significant pathway. Among the upstream regulators, SMARCA4 was downregulated and prioritized by IPA as the top affected upstream regulator for several DCM-related genes. To our knowledge, this study is the first to perform a transcriptomic meta-analysis for clinical DCM using RNA-seq datasets. Overall, our meta-analysis successfully identified a core set of genes associated with DCM

Polygenic risk scores for schizophrenia and major depression are associated with socio-economic indicators of adversity in two British community samples

Schizophrenia (SCZ) and major depressive disorder (MDD) are complex psychiatric disorders which contribute substantially to the global burden of disease. Both psychopathologies are heritable with some genetic overlap between them. Importantly, SCZ and MDD have also been found to be associated with environmental risk factors. However, rather than being independent of genetic influences, exposure to environmental risk factors may be under genetic control, known as gene-environment correlation (rGE). In this study we investigated rGE in relation to polygenic risk scores for SCZ and MDD in adults, derived from large genome-wide association studies, across two different British community samples: Understanding Society (USoc) and the National Child Development Study (NCDS). We tested whether established environmental risk factors for SCZ and/or MDD are correlated with polygenic scores in adults and whether these associations differ between the two disorders and cohorts. Findings partially overlapped between disorders and cohorts. In NCDS, we identified a significant correlation between the genetic risk for MDD and an indicator of low socio-economic status, but no significant findings emerged for SCZ. In USoc, we replicated associations between indicators of low socio-economic status and the genetic propensity for MDD. In addition, we identified associations between the genetic susceptibility for SCZ and being single or divorced. Results across both studies provide further evidence that the genetic risk for SCZ and MDD were associated with common environmental risk factors, specifically MDD’s association with lower socio-economic status

Genetic variants in TRPM7 associated with unexplained stillbirth modify ion channel function

Stillbirth is the loss of a fetus after 22 weeks of gestation, of which almost half go completely unexplained despite post-mortem. We recently sequenced 35 arrhythmia-associated genes from 70 unexplained stillbirth cases. Our hypothesis was that deleterious mutations in channelopathy genes may have a functional effect in utero that may be pro-arrhythmic in the developing fetus. We observed four heterozygous, nonsynonymous variants in transient receptor potential melastatin 7 (TRPM7), a ubiquitously expressed ion channel known to regulate cardiac development and repolarization in mice. We used site-directed mutagenesis and single-cell patch-clamp to analyze the functional effect of the four stillbirth mutants on TRPM7 ion channel function in heterologous cells. We also used cardiomyocytes derived from human pluripotent stem cells to model the contribution of TRPM7 to action potential morphology. Our results show that two TRPM7 variants, p.G179V and p.T860M, lead to a marked reduction in ion channel conductance. This observation was underpinned by a lack of measurable TRPM7 protein expression, which in the case of p.T860M was due to rapid proteasomal degradation. We also report that human hiPSC-derived cardiomyocytes possess measurable TRPM7 currents; however, siRNA knockdown did not directly affect action potential morphology. TRPM7 variants found in the unexplained stillbirth population adversely affect ion channel function and this may precipitate fatal arrhythmia in utero

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact

First genome-wide association study investigating blood pressure and renal traits in domestic cats

Hypertension (HTN) and chronic kidney disease (CKD) are common in ageing cats. In humans, blood pressure (BP) and renal function are complex heritable traits. We performed the first feline genome-wide association study (GWAS) of quantitative traits systolic BP and creatinine and binary outcomes HTN and CKD, testing 1022 domestic cats with a discovery, replication and meta-analysis design. No variants reached experimental significance level in the discovery stage for any phenotype. Follow up of the top 9 variants for creatinine and 5 for systolic BP, one SNP reached experimental-wide significance for association with creatinine in the combined meta-analysis (chrD1.10258177; P = 1.34 × 10(–6)). Exploratory genetic risk score (GRS) analyses were performed. Within the discovery sample, GRS of top SNPs from the BP and creatinine GWAS show strong association with HTN and CKD but did not validate in independent replication samples. A GRS including SNPs corresponding to human CKD genes was not significant in an independent subset of cats. Gene-set enrichment and pathway-based analysis (GSEA) was performed for both quantitative phenotypes, with 30 enriched pathways with creatinine. Our results support the utility of GWASs and GSEA for genetic discovery of complex traits in cats, with the caveat of our findings requiring validation

Genetic Architecture of Quantitative Cardiovascular Traits: Blood Pressure, ECG and Imaging Phenotypes

Background: We provide an overview of the genetic architecture of quantitative cardiovascular phenotypes such as blood pressure (BP), electrocardiogram (ECG) and cardiac imaging measurements which play a critical and prognostic role in the management of numerous diseases. Methods: The genetics of BP, ECG and cardiac imaging traits have been studied in large-scale genome-wide association studies (GWASs). Results: To-date more than 1,400 BP loci have been discovered. These genetic loci harbouring known and novel BP-regulating genes, several of which are linked to existing drugs, that can be repurposed for BP treatment. Regarding the ECG indices, 437 independent signals have been reported for resting heart rate (n 400,000), mainly modulating the autonomic nervous system, as well as 202 loci for PR interval, 29 loci for QRS duration and 35 loci for QT interval. The LV GWASs (n 17,000) identified 14 loci harbouring genes regulating the cardiac developmental pathways. Conclusion: Large-scale genetic analyses of quantitative cardiovascular traits have yielded hundreds of susceptibility loci, candidate genes and key biological pathways, which significantly advance our understanding of their genetic architecture and shed lights on potential novel therapeutic targets

Sex Differences in the Morphology of RR-Matched T-waves

Evidence of sex-related differences in cardiac risk is emerging, but whether these reflect sex-related differences in ventricular electrophysiology remains unclear. Our aim was to quantify T-wave morphological differences between men and women across different leads and RR interval values. We analysed 12-lead ECG recordings from 23,962 participants in the UK Biobank without known cardiovascular disease, and subsequently clustered them into bins of RR interval. In each cluster, we derived a lead and sex-specific mean warped T-wave (MWT). Then, we quantified differences between MWT in men and women in time and amplitude using linear, d_{w} and d_{a}, and non-linear markers, d_{w}^{NL} and d_{a}^{NL}. Leads V3 and aVR showed the lowest differences between men and women (median d_{w}, d_{w}^{NL}, d_{a} and d_{a}^{NL} of 1.12 ms, 0.69 ms, 3.29 and 1.20, respectively), while V1 showed the largest (5.69 ms, 4.50 ms, 208.94 and 199.45, respectively). Sex-related differences in MWT increased with the RR interval (d_{w}, d_{w}^{NL}, d_{a} and d_{a}^{NL} ranging 1.44 - 5.89 ms, 1.23 - 3.97 ms, 8.58 - 28.38 and 1.53 - 4.41, respectively). These values compare to those found for morphological T-wave variations due to large changes in heart rate (5.66 ms, 2.35 ms, 57.61 and 9.51, respectively). These results indicate sex and lead should be considered when using T-wave morphologies for cardiovascular risk prediction