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 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.

Abstract 232: A Genome-wide Association of Murine Echocardiographic Measures Implicates Muscle A-type Lamin-interacting Protein (MLIP) in Isoproterenol-induced Interventricular Septal Hypertrophy

2013 ◽  
Vol 113 (suppl_1) ◽  
Author(s):  
Jessica J Wang ◽  
Christoph Rau ◽  
Shuxun Ren ◽  
Marie-Elodie Cattin ◽  
Patrick G Burgon ◽  
...  
Keyword(s):  
Heart Failure ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Population Substructure ◽  
Genome Wide Association Studies ◽  
Interacting Protein ◽  
Genome Wide ◽  
A Genome ◽  
Study Results ◽  
Septal Hypertrophy

Genome-wide association studies of heart failure in humans have yielded limited results, largely due to highly heterogeneous environmental and genetic backgrounds. The Hybrid Mouse Diversity Panel consists of inbred strains that are either fully sequenced or densely genotyped and as a whole displays significant natural variations. We assessed the hypothesis that precise environmental control, a well-defined genetic background, and a careful and uniform phenotyping scheme enable high-resolution mapping of cardiac remodeling in an isoproterenol-induced heart failure mouse model. Eight to ten-week old females from 105 inbred mouse strains (average N per strain = 6.7) were divided into control (average N per strain = 2.5) and treated (average N per strain =4.1) cohorts. Treated mice received 20 μg/g/day of isoproterenol through an abdominally implanted Alzet micropump for 3 weeks. All mice underwent echocardiography at baseline and at weekly time points. Phenotypic data were analyzed using the Efficient Mixed-Model Association (EMMA) algorithm to correct for population substructure. Our study showed that chronic isoproterenol administration resulted in marked inter-strain variations in echocardiographic measurements across the mouse panel. EMMA analysis of the data revealed many significant association peaks across echocardiographic measures. The most significant association signal is in the change of diastolic interventricular septal wall thickness between baseline and 1 week after isoproterenol treatment, a marker of early isoproterenol-induced left ventricular hypertrophy. The peak SNP rs13480288 (p-value of 4.0580e-09) and its surrounding SNPs (within correlation r2 > 0.8) span across 3 genes. Of these, Mlip alone harbors a missence variant and a splice-site variant across our panel. Mlip, also known as muscle A-type lamin-interacting protein, is expressed abundantly in the heart and interacts directly with A-type lamins in the nuclear envelope. Mutations in A-type lamins have been shown to cause both hypertrophic and dilated cardiomyopathy. In conclusion, our study results provide strong evidence that genetic variations in Mlip contribute to differential responses in interverntricular septal hypertrophy to isoproterenol.

scholarly journals Temporal trends in outcome and patient characteristics in dilated cardiomyopathy, data from the Swedish Heart Failure Registry 2003–2015

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Helen Sjöland ◽  
Jonas Silverdal ◽  
Entela Bollano ◽  
Aldina Pivodic ◽  
Ulf Dahlström ◽  
...  
Keyword(s):  
Heart Failure ◽  
Dilated Cardiomyopathy ◽  
Temporal Trends ◽  
Physical Symptoms ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Continuous Change ◽  
Ventricular Ejection Fraction ◽  
Increased Risk ◽  
Over Time

Abstract Background Temporal trends in clinical composition and outcome in dilated cardiomyopathy (DCM) are largely unknown, despite considerable advances in heart failure management. We set out to study clinical characteristics and prognosis over time in DCM in Sweden during 2003–2015. Methods DCM patients (n = 7873) from the Swedish Heart Failure Registry were divided into three calendar periods of inclusion, 2003–2007 (Period 1, n = 2029), 2008–2011 (Period 2, n = 3363), 2012–2015 (Period 3, n = 2481). The primary outcome was the composite of all-cause death, transplantation and hospitalization during 1 year after inclusion into the registry. Results Over the three calendar periods patients were older (p = 0.022), the proportion of females increased (mean 22.5%, 26.4%, 27.6%, p = 0.0001), left ventricular ejection fraction was higher (p = 0.0014), and symptoms by New York Heart Association less severe (p < 0.0001). Device (implantable cardioverter defibrillator and/or cardiac resynchronization) therapy increased by 30% over time (mean 11.6%, 12.3%, 15.1%, p < 0.0001). The event rates for mortality, and hospitalization were consistently decreasing over calendar periods (p < 0.0001 for all), whereas transplantation rate was stable. More advanced physical symptoms correlated with an increased risk of a composite outcome over time (p = 0.0043). Conclusions From 2003 until 2015, we observed declining mortality and hospitalizations in DCM, paralleled by a continuous change in both demographic profile and therapy in the DCM population in Sweden, towards a less affected phenotype.

scholarly journals Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jia Y. Wan ◽  
Deborah L. Goodman ◽  
Emileigh L. Willems ◽  
Alexis R. Freedland ◽  
Trina M. Norden-Krichmar ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Metabolic Syndrome ◽  
Family Study ◽  
Genome Wide Association ◽  
European American ◽  
Japanese American ◽  
Genetic Associations ◽  
Chromosome 11 ◽  
Genome Wide ◽  
American Families

Abstract Background To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. Methods Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina’s Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping. Results Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects. Conclusions This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.

Abstract 16003: Genetic Variation Associated With Favorable Exercise Response in Patients With Systolic Heart Failure: A Hf-action Trial Substudy

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Sara Coles ◽  
Stephanie Giamberardino ◽  
Carol Haynes ◽  
Ruicong She ◽  
Hongsheng Gui ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Trial ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Systolic Heart Failure ◽  
Genome Wide Association ◽  
Supervised Exercise ◽  
Genome Wide ◽  
A Genome ◽  
Response To Exercise

Background: Exercise has shown benefit in patients with systolic heart failure, including in the clinical trial Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION). There is heterogeneity in who derives benefit from exercise, and the biologic mechanisms of favorable response to exercise in systolic heart failure are not well understood. Hypothesis: Genetic variation is an underlying factor influencing heterogeneity in response to exercise in patients with systolic heart failure. Methods: The HF-ACTION trial randomized individuals with systolic heart failure (left ventricular ejection fraction <35%) to supervised exercise versus usual care. In this study, we performed a genome wide association study (GWAS) in the HF-ACTION biorepository using the Axiom Biobank1 genotyping array (13,403,591 single nucleotide polymorphisms [SNPs] after quality control on directly genotyped and 1000 genomes imputed data), in N=377 study subjects who completed the supervised exercise arm. Using change in peak VO2 as our outcome, we ran within-ancestry GWASes, modeling SNP effects as both additive and dominant, and conducted across-ancestry meta-analysis within each genetic model. Results: Five loci met genome-wide significance in the European ancestry analyses, 5 loci in the African ancestry, and 8 in the meta-analyses. The two most significantly associated loci across both additive and dominant meta-analysis models were rs111577308 located in the histone acetylation for transcription elongator complex 3 gene ( ELP3, p=1.212x10 -9 ) and rs75444785 located in the phosphodiesterase 4D gene ( PDE4D , p=1.565x10 -9 ). ELP3 is responsible for histone modifications related to DNA transcription factor complexes, and PDE4D is involved in cyclic AMP cell signaling. In silico analysis of these loci showed that they are in linkage with regions associated with skeletal muscle and peripheral vascular disease phenotypes. Conclusions: Using a genome-wide association study in a well-phenotyped clinical trial of exercise in systolic heart failure, we found common genetic variants in genes involved in DNA transcription histone modification and cyclic AMP cell signaling that are associated with a more favorable response to exercise.

Genome-wide association for heart failure: from discovery to clinical use

2021 ◽  
Author(s):  
Dominic E Fullenkamp ◽  
Megan J Puckelwartz ◽  
Elizabeth M McNally
Keyword(s):  
Heart Failure ◽  
Genome Wide Association ◽  
Clinical Use ◽  
Genome Wide

Abstract MP171: Transcription Start Site Profiling Defines Promoter and Enhancer Regions for Cardiomyopathy Genes

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Anthony M Gacita ◽  
Lisa Dellefave-Castillo ◽  
Patrick G Page ◽  
David Y Barefield ◽  
Andrew Wasserstrom ◽  
...  
Keyword(s):  
Heart Failure ◽  
Genetic Variation ◽  
Cardiac Tissue ◽  
Autosomal Dominant ◽  
Gene Promoters ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Reporter Assays ◽  
Transcriptional Start Sites ◽  
Putative Enhancer

Background: Mutations in more than 100 genes lead to dilated, hypertrophic and other forms of cardiomyopathy. Autosomal dominant mutations in the MYH7 and LMNA genes cause autosomal dominant hypertrophic and dilated cardiomyopathy, respectively. Individual mutations display a range of clinical expression from severe early onset disease to minimal or no symptoms. Genetic variation in noncoding gene regulatory regions including enhancers is expected to modify expression of cardiomyopathy genes and disease expressivity. In addition, heart failure is associated a fetal gene re-expression program, mediated by genetic regulatory regions. The contribution of noncoding genetic variation to cardiomyopathy and heart failure has been hampered by limited genome wide descriptions of human cardiac regulatory regions. Methods and Results: We used C ap A nalysis of G ene E xpression by sequencing (CAGE-seq) to profile the transcriptional start sites in healthy and failed human hearts. CAGE-seq detects the unidirectional signals of gene promoters and the bidirectional signal of transcribed enhancer regions. We identified ~17,000 transcriptional start sites associated with gene promoters and ~1,500 putative enhancer regions active in cardiac tissue. These CAGE-defined regulatory regions carried histone modifications and transcription factor binding properties characteristic of enhancers or promoters. We specifically identified promoter switching and differential enhancer usage between healthy and failed hearts. We intersected CAGE-defined enhancers with additional epigenomic datasets to identify regulatory regions for MYH7 and LMNA genes. We identified 13 putative enhancer regions and validated the functionality of a subset of these regulatory regions using reporter assays and gene editing. Conclusions: This CAGE-seq dataset defines the regulatory environment for heart failure. These promoter and enhancer regions could be used to target heart-failure associated gene expression changes. Additionally, this data can be used to identify enhancer regions regulating cardiomyopathy genes.

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