Abstract 439: Trajectory Analysis of Left Ventricular Dimensions From Biobank Data Uncovers Novel Genetic Associations

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Tess Pottinger ◽  
Megan J Puckelwartz ◽  
Lorenzo L Pesce ◽  
Anthony Gacita ◽  
Isabella Salamone ◽  
...  
Keyword(s):  
Heart Failure ◽  
Longitudinal Data ◽  
Association Studies ◽  
The United States ◽  
Left Ventricular ◽  
Genome Wide Association Studies ◽  
Qtc Interval ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Over Time

Background: Approximately 6 million adults in the United States have heart failure. The progression of heart failure is variable arising from differences in sex, age, genetic background including ancestry, and medication response. Many population-based genetic studies of heart failure have been cross-sectional in nature, failing to gain additional power from longitudinal analyses. As heart failure is known to change over time, using longitudinal data trajectories as a quantitative trait will increase power in genome wide association studies (GWAS). Methods: We used the electronic health record in a racially and ethnically diverse medical biobank from a single, metropolitan US center. We used whole genome data from 896 unrelated participants analyzed, including 494 who had at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (average of 3 observations per person). A mixture model based semiparametric latent growth curve model was used to cluster outcome measures used for genome-wide analyses. Results: GWAS on the trajectory probability of QTc interval identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes Wntless, a Wnt ligand secretion mediator. WLS was previously associated with QTc and myocardial infarction, thus confirming the power of the method. GWAS on the trajectory probability of left ventricular diameter (LVIDd) identified significant associations with variants in regulatory regions near MYO10 , which encodes unconventional Myosin-10. MYO10 was previously associated with obesity and metabolic syndrome. Conclusions: This is the first study to show an association with variants in or near MYO10 and left ventricular dimension changes over time. Further, we found that using trajectory probabilities can provide increased power to find novel associations with longitudinal data. This reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts, such as those found in biobanks. This approach should be useful in the study of rare diseases and underrepresented populations.

scholarly journals Genome-wide association and multi-omic analyses reveal new mechanisms for Heart Failure

2019 ◽  
Author(s):  
Marios Arvanitis ◽  
Yanxiao Zhang ◽  
Wei Wang ◽  
Adam Auton ◽  
Ali Keramati ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiac Muscle ◽  
Association Studies ◽  
Meta Analysis ◽  
Regulatory Region ◽  
Causal Variant ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractHeart failure is a major medical and economic burden in the healthcare system affecting over 23 million people worldwide. Although recent pedigree studies estimate heart failure heritability around 26%, genome-wide association studies (GWAS) have had limited success in explaining disease pathogenesis. We conducted the largest meta-analysis of heart failure GWAS to-date and replicated our findings in a comparable sized cohort to identify one known and two novel variants associated with heart failure. Leveraging heart failure sub-phenotyping and fine-mapping, we reveal a putative causal variant found in a cardiac muscle specific regulatory region that binds to the ACTN2 cardiac sarcolemmal gene and affects left ventricular adverse remodeling and clinical heart failure in response to different initial cardiac muscle insults. Via genetic correlation, we show evidence of broadly shared heritability between heart failure and multiple musculoskeletal traits. Our findings extend our understanding of biological mechanisms underlying heart failure.

scholarly journals Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sonia Shah ◽  
◽  
Albert Henry ◽  
Carolina Roselli ◽  
Honghuang Lin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Cardiac Development ◽  
Association Studies ◽  
Meta Analysis ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractHeart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

scholarly journals Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure

2019 ◽  
Author(s):  
Sonia Shah ◽  
Albert Henry ◽  
Carolina Roselli ◽  
Honghuang Lin ◽  
Garðar Sveinbjörnsson ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Meta Analysis ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractHeart failure (HF) is a leading cause of morbidity and mortality worldwide1. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained2–4. We report the largest GWAS meta-analysis of HF to-date, comprising 47,309 cases and 930,014 controls. We identify 12 independent variant associations with HF at 11 genomic loci, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function suggesting shared genetic aetiology. Expression quantitative trait analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homeostasis (BAG3), and cellular senescence (CDKN1A). Using Mendelian randomisation analysis we provide new evidence supporting previously equivocal causal roles for several HF risk factors identified in observational studies, and demonstrate CAD-independent effects for atrial fibrillation, body mass index, hypertension and triglycerides. These findings extend our knowledge of the genes and pathways underlying HF and may inform the development of new therapeutic approaches.

scholarly journals Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

2020 ◽  
Author(s):  
Tess D. Pottinger ◽  
Lorenzo L. Pesce ◽  
Anthony Gacita ◽  
Lindsey Montefiori ◽  
Nathan Hodge ◽  
...  
Keyword(s):  
Heart Failure ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Genetic Associations ◽  
Regulatory Regions ◽  
Left Ventricular Diameter ◽  
Biologically Relevant ◽  
Genome Wide ◽  
The Us ◽  
Over Time

ABSTRACTApproximately 6 million adults in the US have heart failure (HF). HF progression is variable due in part to differences in sex, age, and genetic ancestry. Previous population-based genetic studies have largely focused on cross-sectional data related to HF, a disease known to change over time. Utilizing longitudinal data trajectory probabilities as a continuous trait may increase the likelihood of finding significant, biologically relevant associations in a genome-wide association (GWA) analysis. We analyzed data from the electronic health record in a medical biobank from a single, metropolitan US center to gather clinically pertinent data for analyses. We evaluated whole genome sequencing of 896 unrelated biobank participants, including 494 with at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (∼3 observations per person). A censored normal distribution multivariable mixture model was used to cluster phenotype measures for genome-wide analyses. GWA analysis on the trajectory probability of the corrected QT measurement (QTc) taken from electrocardiograms identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes the Wnt ligand secretion mediator, Wntless. WLS was previously associated with QT length using of approximately 16,000 participants supporting the utility of this method to uncover significant genetic associations in small datasets. GWA analysis on the trajectory probability of left ventricular diameter as taken from echocardiograms identified novel significant associations with variants in regulatory regions near MYO10, which encodes the unconventional Myosin-10. We found that trajectory probabilities improved the ability to discover significant and relevant genetic associations. This novel approach increased yield from smaller, well-phenotyped cohorts with longitudinal data from a medical biobank.AUTHOR SUMMARYApproximately 6 million adults in the US have heart failure, a disease known to change over time. In a hospital based electronic health record, electrocardiograms and echocardiograms, used to evaluate heart failure, can be tracked over time. We utilized these data to create a novel trait that can be applied to genetic analyses. We analyzed genome sequence of 896 biobank participants from diverse racial/ethnic backgrounds. Genome-wide association (GWA) analyses were performed on a subset of these individuals for heart failure outcomes. A statistical model that incorporates cardiac data that are tracked over time was used to cluster these data using a probabilistic approach. These probabilities were used for a GWA analysis for corrected QT measurement (QTc) and left ventricular diameter (LVID). The QTc interval analysis identified significant correlations with variants in regulatory regions near the WLS gene which encodes the Wnt ligand secretion mediator, Wntless. Analysis of LVID identified significant associations with variants in regulatory regions near the MYO10 gene which encodes the unconventional Myosin-10. Through these analyses, we found that using the trajectory probabilities can facilitate the discovery of novel significant, biologically relevant associations. This method reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts.

Abstract 233: Isoproterenol-induced Cardiac Hypertrophy And Failure In Mice: Gene Network Modeling

2013 ◽  
Vol 113 (suppl_1) ◽  
Author(s):  
Christoph D Rau ◽  
Jessica Wang ◽  
Shuxun Ren ◽  
Zhihua Wang ◽  
Hongmei Ruan ◽  
...  
Keyword(s):  
Heart Failure ◽  
Association Studies ◽  
Inbred Strains ◽  
Genome Wide Association Studies ◽  
Strong Correlations ◽  
Hotspot Analysis ◽  
Linear Relationships ◽  
Genome Wide ◽  
Echocardiographic Parameters ◽  
Hybrid Mouse Diversity Panel

Heart failure is highly heterogeneous and as a result, relatively few insights into the pathways and drivers of heart failure have been identified using system-wide methods such as genome-wide association studies (GWAS). We have developed a resource, the Hybrid Mouse Diversity Panel (HMDP) for high resolution GWAS and systems genetics in mice. Eight week old female mice from 93 unique inbred strains of the HMDP were given 20 μg/g/day of isoproterenol through an abdominally implanted Alzet micropump. Three weeks post-implantation, all mice were sacrificed, along with age-matched controls. The mice exhibited widely varying degrees of hypertrophy and heart functioning. A portion of the left ventricle was processed and arrayed on an Illumina Mouse Ref 8.0 platform. We used Maximal Information Component Analysis, a novel method of network construction which allows for non-linear relationships between genes as well as non-binary partitioning of genes into sub-networks to subdivide the expression data into a series of modules. In order to identify modules which may contribute to Isoproterenol-induced hypertrophy and failure, we examined the correlation of each module to clinically relevant cardiac traits traits such as organ weights and echocardiographic parameters. We identified several modules with strong correlations to multiple heart failure-related clinical traits, including one module of 41 genes which contained several genes of interest, including Lgals3, a diagnostic marker for heart failure. Utilizing eQTL hotspot analysis, we have identified a locus which is involved in the regulation of this module. A gene within this locus, Magi2, regulates the turnover of the β-adrenergic receptor and represents a likely candidate for the response to isoproterenol.

scholarly journals Pharmacogenomic study of heart failure and candesartan response from the CHARM programme

2021 ◽  
Author(s):  
Marie-Pierre Dubé ◽  
Olympe Chazara ◽  
Audrey Lemaçon ◽  
Géraldine Asselin ◽  
Sylvie Provost ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Genetic Variant ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Genome Wide Association Studies ◽  
Preserved Ejection Fraction ◽  
Reduced Ejection Fraction ◽  
Genome Wide ◽  
Cardiovascular Endpoint

Aims. The Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity (CHARM) programme consisted of three parallel, randomised, double-blind clinical trials comparing candesartan with placebo in patients with heart failure (HF) categorised according to left ventricular ejection fraction and tolerability to an ACE inhibitor. We conducted a pharmacogenomic study of the CHARM studies to identify genetic predictors of heart failure progression and the efficacy and safety of treatment with candesartan. Methods. We performed genome-wide association studies (GWAS) with the composite endpoint of cardiovascular death or hospitalisation for heart failure in 2727 patients from CHARM-Overall and stratified by CHARM study according to preserved and reduced ejection fraction. The safety endpoints were hyperkalaemia, renal dysfunction, hypotension, and change in systolic blood pressure. We also conducted a genome-wide gene-level collapsing analysis from whole-exome sequencing data with the composite cardiovascular endpoint. Results. We found the genetic variant rs66886237 at 8p21.3 near the gene GFRA2 to be associated with the composite cardiovascular endpoint in 1029 HF patients with preserved ejection fraction from the CHARM-Preserved study [hazard ratio (HR): 1.91, 95% confidence interval (CI): 1.55-2.35; P=1.7x10-9], but not in patients with reduced ejection fraction. None of the GWAS for candesartan safety or efficacy passed the significance threshold. Conclusions. We have identified a candidate genetic variant potentially predictive of the progression of heart failure in patients with preserved ejection fraction. The findings require further replication and we cannot exclude the possibility that the results may be chance findings.

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-Wide Association Studies of Conotruncal Heart Defects with Normally Related Great Vessels in the United States

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1030
Author(s):  
Omobola O. Oluwafemi ◽  
Fadi I. Musfee ◽  
Laura E. Mitchell ◽  
Elizabeth Goldmuntz ◽  
Hongbo M. Xie ◽  
...  
Keyword(s):  
Association Studies ◽  
Heart Defects ◽  
22Q11.2 Deletion Syndrome ◽  
The United States ◽  
Deletion Syndrome ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide ◽  
Gene Level ◽  
Great Vessels

Conotruncal defects with normally related great vessels (CTD-NRGVs) occur in both patients with and without 22q11.2 deletion syndrome (22q11.2DS), but it is unclear to what extent the genetically complex etiologies of these heart defects may overlap across these two groups, potentially involving variation within and/or outside of the 22q11.2 region. To explore this potential overlap, we conducted genome-wide SNP-level, gene-level, and gene set analyses using common variants, separately in each of five cohorts, including two with 22q11.2DS (N = 1472 total cases) and three without 22q11.2DS (N = 935 total cases). Results from the SNP-level analyses were combined in meta-analyses, and summary statistics from these analyses were also used in gene and gene set analyses. Across all these analyses, no association was significant after correction for multiple comparisons. However, several SNPs, genes, and gene sets with suggestive evidence of association were identified. For common inherited variants, we did not identify strong evidence for shared genomic mechanisms for CTD-NRGVs across individuals with and without 22q11.2 deletions. Nevertheless, several of our top gene-level and gene set results have been linked to cardiogenesis and may represent candidates for future work.

scholarly journals Sex Differences in the Genetics of Sarcoidosis Across European and African Ancestry Populations

2021 ◽  
Author(s):  
Ying Xiong ◽  
Susanna Kullberg ◽  
Lori Garman ◽  
Nathan Pezant ◽  
David Ellinghaus ◽  
...  
Keyword(s):  
Sex Differences ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
The United States ◽  
Genetic Variations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome ◽  
Löfgren’S Syndrome

Abstract Background: Sex differences in the susceptibility of sarcoidosis are unknown. The study aims to identify sex-dependent genetic variations in two sarcoidosis clinical phenotypes: Löfgren's syndrome (LS) and non- Löfgren's syndrome (non-LS).Methods: A meta-analysis of genome-wide association studies was conducted in Europeans and African Americans, totaling 10,103 individuals from three population-based cohorts, Sweden (n = 3,843), Germany (n = 3,342), and the United States (n = 2,918), followed by replication look-up in the UK Biobank (n = 387,945). A genome-wide association study based on Immunochip data consisting of 141,000 single nucleotide polymorphisms (SNPs) was conducted in males and females in each cohort, respectively. The association test was based on logistic regression using the additive model in LS and non-LS independently. Additionally, gene-based analysis, expression quantitative trait loci (eQTL) assessments, and enrichment analysis were performed to discover functionally relevant mechanisms related to biological sex. Results: In LS sarcoidosis, we identified various sex-dependent genetic variations (798 SNPs in males and 703 SNPs in females). Genetic findings in sex groups were explicitly located in the extended major histocompatibility complex. In non-LS, we detected 16 SNPs in males and 38 in females, primarily localized to the MHC class II region. Additionally, the ANXA11 gene, a well-documented locus in sarcoidosis, was associated exclusively with non-LS males. Gene-based, eQTL assessment and enrichment analyses revealed distinct sex-dependent genomic loci and gene expression variation in the sex groups. Conclusions: Our findings provide new evidence of the existence of sex-dependent genetic variations underlying sarcoidosis genetic architecture. These findings suggest a sex bias in molecular mechanisms of sarcoidosis.

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