Carolina Roselli

BACKGROUND: Clinical factors discriminate incident atrial fibrillation (AF) risk with moderate accuracy, with only modest improvement after incorporation of polygenic risk scores. Whether emerging large-scale proteomic profiling can augment AF risk estimation is unknown. METHODS: In the UK Biobank cohort, we derived and validated a machine learning model to predict incident AF risk using serum proteins (Pro-AF). We compared Pro-AF to a validated clinical risk score (Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation, CHARGE-AF) and an AF polygenic risk score. Models were evaluated in a multiply resampled test set from nested cross-validation (internal test set), and a sample of UK Biobank participants separate from model development (hold-out test set). Metrics included discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic curve and net reclassification. RESULTS: Trained in 32 631 UK Biobank participants, Pro-AF predicts incident AF using 121 protein levels (out of 2911 protein analytes). When assessed in the internal test set comprising 30 632 individuals (mean age 57±8 years, 54% women, 2045 AF events) and hold-out test set comprising 13 998 individuals (mean age 57±8 years, 54% women, 870 AF events), discrimination of 5-year incident AF was highest using Pro-AF (area under the receiver operating characteristic curve internal: 0.761 [95% CI, 0.745–0.780], hold-out: 0.763 [0.734–0.784]), followed by CHARGE-AF (0.719 [0.700–0.737]; 0.702 [0.668–0.730]) and the polygenic risk score (0.686 [0.668–0.702]; 0.682 [0.660–0.710]). AF risk estimates were well-calibrated, and the addition of Pro-AF led to substantial continuous net reclassification improvement over CHARGE-AF (eg, internal test set 0.410 [0.330–0.492]). A simplified Pro-AF including only the 5 most influential proteins (NT-proBNP [N-terminal pro-brain natriuretic peptide], EDA2R [ectodysplasin A2 receptor], NPPB [B-type natriuretic peptide], BCAN [brevican core protein], and GDF15 [growth/differentiation factor 15]), retained favorable discriminative value (area under the receiver operating characteristic curve internal: 0.750 [0.733–0.768]; hold-out: 0.759 [0.732–0.790]). CONCLUSIONS: A machine learning-based protein score discriminates 5-year incident AF risk favorably compared with clinical and genetic risk factors. Large-scale proteomic analysis may assist in the prioritization of individuals at risk for AF for screening and related preventive interventions.

BACKGROUND: Because treatment with anticoagulants can prevent recurrent strokes, identification of patients at risk for incident atrial fibrillation (AF) after stroke is crucial. We aimed to investigate whether the addition of AF polygenic risk scores (PRSs) to existing clinical risk predictors could improve prediction of AF after stroke. METHODS: Patients diagnosed with ischemic stroke at the Massachusetts General Hospital between 2003 and 2017 were included. Clinical AF risk was estimated using the Recalibrated Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation model, and genetic risk was estimated using a contemporary AF PRS from 1 093 050 variants. Patients were divided into clinical and genetic risk tertiles. Cox proportional hazards models at different follow-up windows were fit, and C indices and percentile-based net reclassification index were used to determine the improvement of clinical risk models with the addition of AF PRS. RESULTS: Of 1004 stroke survivors, 900 (90%) were non-Hispanic White, 413 (41%) were female, and the mean age was 67 (SD, 14) years. Of 1004 survivors, 239 (23.8%) had prevalent AF and 87 of 765 (11.4%) remaining patients developed incident AF during 5 years of follow-up. AF PRS was associated with greater risk of incident AF after stroke (hazard ratio, 1.21 [95% CI, 0.97–1.50] per 1-SD increase), although the association was not statistically significant. PRS improved discrimination in the first month (area under the curve, 0.78 [95% CI, 0.70–0.82] versus 0.71 [95% CI, 0.60–0.82]; P =0.05), with more modest estimates across longer time windows. CONCLUSIONS: Addition of an AF PRS to clinical risk models may improve identification of individuals at risk of AF after stroke, particularly within the first month.

Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B. We further identified associations between AF and rare structural variants owing to deletions in CTNNA3 and duplications of GATA4. We broadly replicated our findings in independent samples from MyCode, deCODE and UK Biobank. Finally, we found that CRISPR knockout of KDM5B in stem-cell-derived atrial cardiomyocytes led to a shortening of the action potential duration and widespread transcriptomic dysregulation of genes relevant to atrial homeostasis and conduction. Our results highlight the contribution of rare coding and structural variants to AF, including genetic links between AF and cardiomyopathies, and expand our understanding of the rare variant architecture for this common arrhythmia.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respectively. Four well-known loci (SCN5A/SCN10A, CCDC141, TBX20 and CAMK2D) were shared for SND and DCD, while others were more specific for SND or DCD. SND and DCD showed a moderate genetic correlation (r_g = 0.63). Cardiomyocyte-expressed genes were enriched for contributions to DCD heritability. Rare-variant analyses implicated LMNA for all bradyarrhythmia phenotypes, SMAD6 and SCN5A for DCD and TTN, MYBPC3 and SCN5A for PM. These results show that variation in multiple genetic pathways (for example, ion channel function, cardiac developmental programs, sarcomeric structure and cellular homeostasis) appear critical to the development of bradyarrhythmias.

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (β_Deming = 0.7–1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

BACKGROUND: Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS: We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS: Analyses comprised 2384 AVNRT cases and 106 489 referents, and 2811 AVAP/AVRT cases and 1,483 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A , SCN10A , and TTN/CCDC141 . Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS: Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.