Shared Genetic and Environmental Architecture of Cardiac Phenotypes Assessed via Echocardiography: The Framingham Heart Study.

Abstract

HomeCirculation: Genomic and Precision MedicineVol. 14, No. 2Shared Genetic and Environmental Architecture of Cardiac Phenotypes Assessed via Echocardiography Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBShared Genetic and Environmental Architecture of Cardiac Phenotypes Assessed via EchocardiographyThe Framingham Heart Study Honghuang Lin, PhD, Cecilia Castro-Diehl, MD, MPH, DrPH, Meghan I. Short, PhD, Vanessa Xanthakis, PhD, Ibrahim M. Yola, MD, MPH, Alan C. Kwan, MD, Gary F. Mitchell, MD, Martin G. Larson, ScD, Ramachandran S. Vasan, MD and Susan Cheng, MD, MPH Honghuang LinHonghuang Lin https://orcid.org/0000-0003-3043-3942 Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA (H.L., V.X., M.G.L., R.S.V.). Section of Computational Biomedicine, Department of Medicine (H.L.), Boston University School of Medicine, MA. *H. Lin and C. Castro-Diehl contribution equally. Search for more papers by this author , Cecilia Castro-DiehlCecilia Castro-Diehl https://orcid.org/0000-0003-3650-4684 Section of Preventive Medicine and Epidemiology, Department of Medicine (C.C.-D., V.X., I.M.Y., R.S.V.), Boston University School of Medicine, MA. *H. Lin and C. Castro-Diehl contribution equally. Search for more papers by this author , Meghan I. ShortMeghan I. Short Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio (M.I.S.). Department of Biostatistics (M.I.S., V.X., M.G.L.), Boston University School of Public Health, MA. Search for more papers by this author , Vanessa XanthakisVanessa Xanthakis https://orcid.org/0000-0002-7352-621X Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA (H.L., V.X., M.G.L., R.S.V.). Section of Preventive Medicine and Epidemiology, Department of Medicine (C.C.-D., V.X., I.M.Y., R.S.V.), Boston University School of Medicine, MA. Department of Biostatistics (M.I.S., V.X., M.G.L.), Boston University School of Public Health, MA. Search for more papers by this author , Ibrahim M. YolaIbrahim M. Yola https://orcid.org/0000-0002-2898-9792 Section of Preventive Medicine and Epidemiology, Department of Medicine (C.C.-D., V.X., I.M.Y., R.S.V.), Boston University School of Medicine, MA. Search for more papers by this author , Alan C. KwanAlan C. Kwan https://orcid.org/0000-0002-4393-1011 Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (A.C.K., S.C.). Search for more papers by this author , Gary F. MitchellGary F. Mitchell https://orcid.org/0000-0001-5643-3145 Cardiovascular Engineering, Inc, Norwood, MA (G.F.M.). Search for more papers by this author , Martin G. LarsonMartin G. Larson Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA (H.L., V.X., M.G.L., R.S.V.). Department of Biostatistics (M.I.S., V.X., M.G.L.), Boston University School of Public Health, MA. Search for more papers by this author , Ramachandran S. VasanRamachandran S. Vasan https://orcid.org/0000-0001-7357-5970 Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA (H.L., V.X., M.G.L., R.S.V.). Section of Preventive Medicine and Epidemiology, Department of Medicine (C.C.-D., V.X., I.M.Y., R.S.V.), Boston University School of Medicine, MA. Section of Cardiovascular Medicine, Department of Medicine (R.S.V.), Boston University School of Medicine, MA. Department of Epidemiology (R.S.V.), Boston University School of Public Health, MA. Center for Computing and Data Sciences, Boston University, MA (R.S.V.). Search for more papers by this author and Susan ChengSusan Cheng Correspondence to: Susan Cheng, MD, MPH, Department of Cardiology, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd, Suite A3100, Los Angeles, CA 90048. Email E-mail Address: [email protected] https://orcid.org/0000-0002-4977-036X Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (A.C.K., S.C.). Search for more papers by this author Originally published19 Apr 2021https://doi.org/10.1161/CIRCGEN.120.003244Circulation: Genomic and Precision Medicine. 2021;14:e003244Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: April 19, 2021: Ahead of Print Echocardiographic measures of cardiac structure and function represent important predictors of heart failure.1 Evidence suggests that both genetic and environmental factors contribute to phenotypic variation of echocardiographic traits.2 Identifying these factors could facilitate targeted interventions for optimizing individual risk profiles. However, the degree to which key cardiac traits are influenced by genetic versus environmental exposures, and the interaction between these exposures, remains unknown. Our objective was to investigate the heritability of multiple echocardiographic traits and their environmental correlations.We studied Framingham Heart Study attendants of Offspring Cohort exam 8 (2005–2008) or Third Generation Cohort exam 1 (2002–2005) with standardized echocardiography performed per guidelines.3 The Third Generation Cohort included children of the Offspring Cohort. Individuals with heart failure or ≥1 missing echocardiographic measure were excluded, providing N=5674 participants (mean age 49 years, 54% women). We selected 5 cardiac structural and 6 functional traits for heritability and paired analyses based on their representing key measures that have been previously related to outcomes and their being reliably and reproducibly measured in our and other core laboratories. The structural traits included: left ventricle (LV) mass index, diastolic dimension, LV wall thickness (LVWT), left atrial systolic dimension, and aortic root diameter. The functional traits included: LV ejection fraction (LVEF), global circumferential and longitudinal strain (GCS, global longitudinal strain), longitudinal synchrony (LSS), MAPSE, and E/e′. All the echocardiographic measures have been deposited in the dbGaP database (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000007.v32.p13). The Framingham Heart Study was approved by the Institutional Review Boards of Boston University Medical Center, and all participants provided written informed consent.For all analyses, LV mass index, LSS, and E/e′ were natural log-transformed. For each echocardiographic trait, the associations with age, sex, and height were adjusted for in linear regression models; residuals of regression models were then transformed using a rank-based inverse normal transformation. The normalized values (adjusted for age, sex, height) were used for heritability analyses. Singular and bivariate quantitative trait linkage analysis was performed using Sequential Oligogenic Linkage Analysis Routines,4 which estimates additive genetic heritability h2 using pedigree information (spousal correlation was considered 0 in the genetic correlation). No shared versus unshared environmental conditions were estimated. Phenotypic correlations among all pairs of traits were calculated and partitioned into genetic and environmental correlations using the variance components method.4,5We observed single trait heritabilities ranging from 0.27 (LVWT) to 0.42 (aortic root diameter) for cardiac structural traits and 0.19 (MAPSE) to 0.41 (log[LSS]) for functional traits (P<0.0001 for all; Figure). Heritability of structural traits tended to be higher than for functional traits. Results were unchanged after adjusting for systolic blood pressure and body mass index for traits associated with these characteristics (LV mass index, LV diastolic dimension, and LVWT). Phenotypic correlations between traits ranged from −0.44 (LVEF and GCS) to 0.68 (LV mass index and LVWT). We used bivariate quantitative analysis to examine genetic and environmental correlation between trait pairs. We observed moderate-to-strong genetic correlations among several traits: 0.61 (LVWT with left atrial systolic dimension), and −0.44 (global longitudinal strain with LVEF) to −0.79 (GCS with LVEF). Shared genetic correlation due to pleiotropy ranged from 0.0008 (Log [LSS] and MAPSE) to 0.632 (LVEF and GCS). Modest environmental correlations emerged for other traits: 0.26 (LV diastolic dimension with left atrial systolic dimension), and −0.32 (GCS with LVEF). There were also unmatched pairs with significant genetic but no environmental correlation, or vice versa.Download figureDownload PowerPointFigure. Heritability estimates and genetic and environmental correlations among echocardiographic traits. Upper diagonal values are genetic correlations; lower diagonal values are environmental correlations. AoD indicates aortic root diameter; GCS, global circumferential strain; GLS, global longitudinal strain; LAD, left atrial systolic dimension; LSS, longitudinal synchrony; LV, left ventricle/left ventricular; LVDD, LV diastolic dimension; LVEF, LV ejection fraction; LVMi, LV mass index; LVWT, LV wall thickness; and MAPSE, mitral annular plane systolic excursion. All models adjusted for age, sex, and height. Values in bold indicate significance: *P<0.05; †P≤ 0.01; ‡P≤0.001; §P<0.0001.Overall, we observed that cardiac structural traits tended to demonstrate greater heritability than cardiac functional traits. Prior cardiac phenotyping studies have reported a wide range of heritability estimates, including ranges for LV mass from 0.24 in cohort-based studies to 0.84 in twin studies. We observed particularly strong genetic and weak environmental correlations between LVWT and left atrial systolic dimension, suggesting that genetic factors play a more important role than environmental factors in parallel left ventricular-atrial remodeling—a complex endophenotype that may be especially susceptible to progression towards heart failure in the setting of certain risk exposures. Extending from prior work, we analyzed advanced measures of cardiac function that have been relatively understudied previously. We observed moderate heritability for several of these traits (eg, LSS, GCS), suggesting that novel genetic loci may be identified for these traits in future investigations. Interestingly, for GCS, we found both strong genetic and strong environmental correlation with LVEF, suggesting that these cardiac functional traits have mechanistic features derived from shared genetic origins in addition to similar patterns of pathophysiologic response to risk exposures.Our analyses were restricted to two related cohorts comprising predominantly individuals of European ancestry and without independent replication. Notwithstanding these limitations, our study involved careful and standardized phenotyping performed in a core laboratory with established quality control and included more than twice as many participants than prior studies, providing statistical power to identify cardiac traits with even modest heritability.In our large community-based sample, we observed moderate heritability for several key cardiac traits including multiple structural and functional phenotypes. Shared genetic influences among cardiac traits suggest common genetic programming, while shared environmental correlates may reflect conjoint cardiac responses to common risk exposures as well as chronic hemodynamic loading conditions.Nonstandard Abbreviations and AcronymsGCSglobal circumferential strainLSSlongitudinal synchronyLVleft ventricle/left ventricularLVEFleft ventricular ejection fractionLVMleft ventricular massLVWTleft ventricular wall thicknessMAPSEmitral annular plane systolic excursionSources of FundingThe study was supported by National Institutes of Health (NIH) contracts 75N92019D00031, HHSN268201500001I, and N01-HC 25195 to the Framingham Heart Study. The study was also supported by NIH grants R01HL093328, R01HL107385, R01HL126136, R01HL143227, and R01HL142983. R.S. Vasan is also supported in part by the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine. C. Castro-Diehl was supported by the Multidisciplinary Training Program (T32) in Cardiovascular Epidemiology (5T32HL125232). A.C. Kwan is supported by the Doris Duke Charitable Foundation Grant 2020059. We acknowledge the dedication of the Framingham Heart Study participants without whom this research would not be possible.Disclosures None.Footnotes*H. Lin and C. Castro-Diehl contribution equally.For Sources of Funding and Disclosures, see page 247.Correspondence to: Susan Cheng, MD, MPH, Department of Cardiology, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd, Suite A3100, Los Angeles, CA 90048. Email susan.[email protected]org