Genetics of myocardial interstitial fibrosis in the human heart and association with disease

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.

Adjusting for common variant polygenic scores improves yield in rare variant association analyses

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10-6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.

Machine Learning to Understand Genetic and Clinical Factors Associated With the Pulse Waveform Dicrotic Notch

BACKGROUND

Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease.

METHODS

In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait.

RESULTS

Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 (P=1.2×10-26), IGFBP3 (P=4.8×10-18), and PHACTR1 (P=1.4×10-13), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data.

CONCLUSIONS

We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.

Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.

LMNA Variants and Risk of Adult-Onset Cardiac Disease

BACKGROUND

Genetic variants in LMNA may cause cardiac disease, but population-level contributions of variants to cardiac disease burden are not well-characterized.

OBJECTIVES

We sought to determine the frequency and contribution of rare LMNA variants to cardiomyopathy and arrhythmia risk among ambulatory adults.

METHODS

We included 185,990 UK Biobank participants with whole-exome sequencing. We annotated rare loss-of-function and missense LMNA variants for functional effect using 30 in silico prediction tools. We assigned a predicted functional effect weight to each variant and calculated a score for each carrier. We tested associations between the LMNA score and arrhythmia (atrial fibrillation, bradyarrhythmia, ventricular arrhythmia) or cardiomyopathy outcomes (dilated cardiomyopathy and heart failure). We also examined associations for variants located upstream vs downstream of the nuclear localization signal.

RESULTS

Overall, 1,167 (0.63%) participants carried an LMNA variant and 15,079 (8.11%) had an arrhythmia or cardiomyopathy event during a median follow-up of 10.9 years. The LMNA score was associated with arrhythmia or cardiomyopathy (OR: 2.21; P < 0.001) and the association was more significant when restricted to variants upstream of the nuclear localization signal (OR: 5.05; P < 0.001). The incidence rate of arrhythmia or cardiomyopathy was 8.43 per 1,000 person-years (95% CI: 6.73-10.12 per 1,000 person-years) among LMNA variant carriers and 6.38 per 1,000 person-years (95% CI: 6.27-6.50 per 1,000 person-years) among noncarriers. Only 3 (1.2%) of the variants were reported as pathogenic in ClinVar.

CONCLUSIONS

Middle-aged adult carriers of rare missense or loss-of-function LMNA variants are at increased risk for arrhythmia and cardiomyopathy.

Rare and Common Genetic Variation Underlying the Risk of Hypertrophic Cardiomyopathy in a National Biobank

Importance

Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in young people. Although rare genetic variants are well-established contributors to HCM risk, common genetic variants have recently been implicated in disease pathogenesis.

Objective

To assess the contributions of rare and common genetic variation to risk of HCM in the general population.

Design, Setting, and Participants

This cohort study of the UK Biobank (data from 2006-2010) and the Mass General Brigham Biobank (2010-2019) assessed the relative and joint contributions of rare genetic variants and a common variant (polygenic) score to risk of HCM. Both rare and common variant predictors were then evaluated in the context of relevant clinical risk factors. Data analysis was conducted from May 2021 to February 2022.

Exposures

Pathogenic rare variants, common-variant (polygenic) score, and clinical risk factors.

Main Outcomes and Measures

Risk of HCM.

Results

The primary study population comprised 184 511 individuals from the UK Biobank. Mean (SD) age was 56 (8) years, 83 690 (45%) of participants were men, and 204 (0.1%) participants had HCM. Of 51 genes included in clinical genetic testing panels for HCM, pathogenic or likely pathogenic variants in 14 core genes (designated by the American College of Medical Genetics and Genomics [ACMG]) were associated with 55-fold higher odds (95% CI, 35-83) of HCM, while those in the remaining 37 non-ACMG genes were not significantly associated with HCM (OR, 1.8; 95% CI, 0.6-4.0). ClinVar pathogenic or likely pathogenic mutations in MYBPC3 (OR, 72; 95% CI, 39-124) and MYH7 (OR, 61; 95% CI, 26-121) were strongly associated with HCM, as were loss-of-function variants in ALPK3 (OR, 13; 95% CI, 4.4-28). A polygenic score was strongly associated with HCM (OR per SD increase in score, 1.6; 95% CI, 1.4-1.8), with concordant results in the Mass General Brigham Biobank. Genetic factors enhanced clinical risk prediction for HCM: addition of rare variant carrier status and the polygenic score to clinical risk factors (obesity, hypertension, atrial fibrillation, and coronary artery disease) improved the area under the receiver operator characteristic curve from 0.71 (95% CI, 0.65-0.77) to 0.82 (95% CI, 0.77-0.87).

Conclusions and Relevance

Both rare and common genetic variants contribute substantially to HCM susceptibility in the general population and improve HCM risk prediction beyond that achieved with clinical factors.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population

BACKGROUND

Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population.

METHODS

We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed.

RESULTS

Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS).

CONCLUSIONS

QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis

BACKGROUND

Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood.

METHODS

Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval).

RESULTS

We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10^-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10^-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.

CONCLUSIONS

Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Associations Between Alcohol Intake and Genetic Predisposition With Atrial Fibrillation Risk in a National Biobank

BACKGROUND

Excess alcohol intake and inherited predisposition may increase risk of atrial fibrillation (AF). We assessed the association between alcohol intake, polygenic predisposition to AF, and incident AF in the UK Biobank, a prospective cohort study.

METHODS

In 376 776 UK Biobank participants enrolled between 2006 and 2010, we tested alcohol consumption (stratified by the Centers of Disease Control and Prevention acceptable range of ≤98 g/wk for women or ≤196 g/wk for men; and as a continuous variable) and an AF polygenic risk score for association with incident AF.

RESULTS

Among participants (47.5% male, mean age 56.9 years), 6293 developed AF during a median of 6.9 years of follow-up. Alcohol consumption was associated with AF (hazard ratio, 1.10 [95% CI, 1.05-1.16] for intake above an acceptable range; hazard ratio, 1.04 per 100 g/wk [95% CI, 1.02-1.06]). An AF polygenic risk score was associated with AF (hazard ratio, 1.38 per SD [95% CI, 1.35-1.41]). In models including both alcohol and the AF polygenic risk score, each remained associated with AF. The 5-year cumulative risk of AF for individuals with alcohol intake above an acceptable range and in the highest decile of polygenic risk was 2.33% (95% CI, 2.07-2.59), compared with 0.69% (95% CI, 0.58-0.80) for those with alcohol intake within an acceptable range and in the lowest decile of polygenic risk.

CONCLUSIONS

Alcohol consumption is associated with increased risk of AF across a range of polygenic predisposition to AF and adds to inherited and clinical predisposition to increase AF susceptibility. Preventive efforts focused on minimizing alcohol intake may be broadly applicable.