Evaluating diabetes and hypertension disease causality using mouse phenotypes

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used.</p> <p>Results</p> <p>Here we tapped the rich resource of mouse phenotype data and developed a method to quantify the probability that a gene perturbation causes the phenotypes of a disease. Using type II diabetes (T2D) and hypertension (HT) as study cases, we found that the genes, when perturbed, having high probability to cause T2D and HT phenotypes tend to be hubs in the interactome networks and are enriched for signaling pathways regulating metabolism but not metabolic pathways, even though the genes in these metabolic pathways are often the most significantly changed in expression levels in these diseases.</p> <p>Conclusions</p> <p>Compared to human genetic disease-based predictions, our mouse phenotype based predictors greatly increased the coverage while keeping a similarly high specificity. The disease phenotype probabilities given by our approach can be used to evaluate the likelihood of disease causality of disease-associated genes and genes surrounding disease-associated SNPs.</p

Programming of cardiovascular disease across the life-course.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality, affecting both developed and developing countries. Whilst it is well recognized that our risk of CVD can be determined by the interaction between our genetics and lifestyle, this only partly explains the variability at the population level. Based on these well-known risk factors, for many years, intervention and primary prevention strategies have focused on modifying lifestyle factors in adulthood. However, research shows that our risk of CVD can be pre-determined by our early life environment and this area of research is known as the Developmental Origins of Health and Disease. The aim of this review is to evaluate our current understanding of mechanisms underlying the programming of CVD. This article is part of a special issue entitled CV Aging.H.L. Blackmore is funded by the British Heart Foundation. S.E. Ozanne is a member of the MRC Metabolic Diseases Unit and funded by MRC grantMC_UU_12012/4.This is the accepted manuscript of a paper published in the Journal of Molecular and Cellular Cardiology (Blackmore HL, Ozanne SE, Journal of Molecular and Cellular Cardiology 2014, doi:10.1016/j.yjmcc.2014.12.006)

Characterization of adiposity and inflammation genetic pleiotropy underlying cardiovascular risk factors in Hispanics.

The observed overlap between genetic variants associated with both adiposity and inflammatory markers suggests that changes in both adiposity and inflammation could be partially mediated by common pathways. The pervasive but sparsely characterized “pleiotropic” genetic variants associated with both adiposity and inflammation have been hypothesized to provide insight into the shared biology. This study explored and characterized the genetic pleiotropy underpinning adiposity and inflammation using genetic and phenotypic observations from the Cameron County Hispanic Cohort (CCHC). A total of 3,313 samples and \u3e9 million single nucleotide polymorphisms (SNPs) were examined in this study. Mixed model genome-wide association studies (GWAS) were performed for 9 phenotypes including C-reactive protein (CRP), Interleukin (IL)-6, IL-8, fibrinogen, body mass index (BMI), waist circumference (WC) in males and females, and waist to hip ratio (WHR) in males and females (separately). GWAS for WHR and WC were meta-analyzed to obtain sex-combined results. Pleiotropy assessment was completed using adaptive Sum of Powered Score (aSPU) test. Three genetic loci with evidence of pleiotropy on chromosome 3, 12 and 18 were fine-mapped to distinguish the set of likely vi causal variants. Causal mediation analysis was used to assess whether likely causal variants were independently associated with both inflammation and adiposity. At least 3 signals, on chromosomes 3, 12, and 12, were identified that suggested the presence of SNPs with strong pleiotropic p-values (\u3c 5 × 10−6 ). The fine-mapping of these three suspected pleiotropic regions distinguished 22 variants with posterior causality probabilities greater than 50%. The mediation analysis indicated that rs60505812, on chromosome 3, was independently associated with both an inflammatory marker (IL-6) and an adiposity measure (BMI). For the variant rs73093474, on chromosome 12, results indicated both a direct association with CRP and an indirect association (via WHR). The identification of likely pleiotropic variants indicated that 1) a considerable degree of overlapping genetic pleiotropy exists between adiposity and inflammation, and 2) evidence exists to support both the direct and indirect pleiotropy. The results showed the potential of these genetic variants to provide biological insight, intended to improve the cardiovascular health of the Hispanics, and by extension all populations

Systematic analysis of relationships between plasma branched-chain amino acid concentrations and cardiometabolic parameters:an association and Mendelian randomization study

Abstract Background Branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) are essential amino acids that are associated with an increased risk of cardiometabolic diseases (CMD). However, there are still only limited insights into potential direct associations between BCAAs and a wide range of CMD parameters, especially those remaining after correcting for covariates and underlying causal relationships. Methods To shed light on these relationships, we systematically characterized the associations between plasma BCAA concentrations and a large panel of 537 CMD parameters (including atherosclerosis-related parameters, fat distribution, plasma cytokine concentrations and cell counts, circulating concentrations of cardiovascular-related proteins and plasma metabolites) in 1400 individuals from the Dutch population cohort LifeLines DEEP and 294 overweight individuals from the 300OB cohort. After correcting for age, sex, and BMI, we assessed associations between individual BCAAs and CMD parameters. We further assessed the underlying causality using Mendelian randomization. Results A total of 838 significant associations were detected for 409 CMD parameters. BCAAs showed both common and specific associations, with the most specific associations being detected for isoleucine. Further, we found that obesity status substantially affected the strength and direction of associations for valine, which cannot be corrected for using BMI as a covariate. Subsequent univariable Mendelian randomization (UVMR), after removing BMI-associated SNPs, identified seven significant causal relationships from four CMD traits to BCAA levels, mostly for diabetes-related parameters. However, no causal effects of BCAAs on CMD parameters were supported. Conclusions Our cross-sectional association study reports a large number of associations between BCAAs and CMD parameters. Our results highlight some specific associations for isoleucine, as well as obesity-specific effects for valine. MR-based causality analysis suggests that altered BCAA levels can be a consequence of diabetes and alteration in lipid metabolism. We found no MR evidence to support a causal role for BCAAs in CMD. These findings provide evidence to (re)evaluate the clinical importance of individual BCAAs in CMD diagnosis, prevention, and treatment

Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions

With mouse age comes wisdom : a review and suggestions of relevant mouse models for age-related conditions

Ageing is a complex multifactorial process that results in many changes in physiological changes processes that ultimately increase susceptibility to a wide range of diseases. As such an ageing population is resulting in a pressing need for more and improved treatments across an assortment of diseases. Such treatments can come from a better understanding of the pathogenic pathways which, in turn, can be derived from models of disease. Therefore the more closely the model resembles the disease situation the more likely relevant the data will be that is generated from them. Here we review the state of knowledge of mouse models of a range of diseases and aspects of an ageing physiology that are all germane to ageing. We also give recommendations on the most common mouse models on their relevance to the clinical situations occurring in aged patients and look forward as to how research in ageing models can be carried out. As we continue to elucidate the pathophysiology of disease, often through mouse models, we also learn what is needed to refine these models. Such factors can include better models, reflecting the ageing patient population, or a better phenotypic understanding of existing models