Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

Atrial fibrillation (AF) affects over 33 million individuals worldwide1 and has a complex heritability.2 We conducted the largest meta-analysis of genome-wide association studies for AF to date, consisting of over half a million individuals including 65,446 with AF. In total, we identified 97 loci significantly associated with AF including 67 of which were novel in a combined-ancestry analysis, and 3 in a European specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait loci (eQTL) analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown. 1 Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations leading to clonal expansion was associated with both hematologic cancer 2 – 4 and coronary heart disease 5 , a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP). 6 Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico -informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer resulting in increased hematopoietic stem cell self-renewal. Overall, we observe that germline genetic variation shapes hematopoietic stem cell function leading to CHIP through mechanisms that are both specific to clonal hematopoiesis and shared mechanisms leading to somatic mutations across tissues.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death.1,2 Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups.3–7 To further define the genetic basis of atrial fibrillation, we performed large-scale, multi-racial meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 18,398 individuals with atrial fibrillation and 91,536 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,806 cases and 132,612 referents. We identified 12 novel genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate new potential targets for drug discovery.8

Background Recurrent atrial fibrillation (AF) occurs in up to 50 % of patients within 1 year after catheter ablation, and a clinical risk score to predict recurrence remains a critical unmet need. The aim of this study was to (1) develop a simple score for the prediction of rhythm outcome following catheter ablation; (2) compare it with the CHADS2 and CHA2DS2-VASc scores, and (3) validate it in an external cohort. Methods Rhythm outcome between 3 and 12 months after AF catheter ablation were documented. The APPLE score [one point for age>65 years, persistent AF, impaired eGFR (<60 ml/min/1.73 m2), LA diameter ≥43 mm, EF < 50 %] was associated with AF recurrence and was validated in an external cohort in 261 patients with comparable ablation and follow-up. Results In 1145 patients (60 ± 10 years, 65 % male, 62 % paroxysmal AF) the APPLE score showed better prediction of AF recurrences (AUC 0.634, 95 % CI 0.600–0.668, p < 0.001) than CHADS2 (AUC 0.538) and CHA2DS2-VASc (AUC 0.542). Compared to patients with an APPLE score of 0, the odds ratio for AF recurrences was 1.73, 2.79 and 4.70 for APPLE scores 1, 2, or ≥3, respectively (all p < 0.05). In the external validation cohort, the APPLE score showed similar results (AUC 0.624, 95 % CI 0.562–0.687, p < 0.001). Conclusions The novel APPLE score is superior to the CHADS2 and CHA2DS2-VASc scores for prediction of rhythm outcome after catheter ablation. It holds promise as a useful tool to identify patients with low, intermediate, and high risk for AF recurrence.

Importance Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood. Objective To perform large-scale, deep-coverage whole genome sequencing to identify genetic variants related to AF. Design, Setting, Participants The National Heart Lung and Blood Institute’s Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high depth, whole genome sequencing between 2014 and 2017 in 18,526 individuals from the U.S., Mexico, Puerto-Rico, Costa-Rica, Barbados, and Samoa. This case-control study included 2,781 patients with early onset AF from 9 studies and identified 4,959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank and the MyCode Study consisting of 346,546 and 42,782 participants, respectively. Exposures Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome. Main Outcomes and Measures Early-onset AF defined as AF onset < 66 years of age. Due to multiple testing, the significance threshold for the rare variant analysis was P=4.55 X 10−3. Results Among 2,781 early-onset AF cases, 72.1% were male, and the mean age of AF onset was 48.7±10.2 years. Samples underwent whole genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least one LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of cases compared with 1.1% in controls. The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend 4.92×10−4) and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (odds ratio = 5.94; 95% CI, 2.64–13.35; P=1.65×10−5). The association between TTN LOF variants and AF was replicated in an independent 1,582 early-onset AF cases and 41,200 controls (odds ratio = 2.16; 95% CI, 1.19–3.92; P=0.01). Conclusions and Relevance In a case control study, there was a statistically significant association between a LOF variant in the gene TTN and early-onset AF, with the variant present in a small percentage of cases. Further research is required to understand whether this is a causal relationship.

Importance Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records may provide a resource to assess the clinical relevance of rare variants. Objective To determine the clinical phenotypes from electronic medical records in individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes. Design, Setting and Participants This prospective cohort study included 2022 individuals recruited for non-antiarrhythmic drug exposure phenotypes from 10/5/2012 to 9/30/2013 for the Electronic Medical Records and Genomics Network Pharmacogenomics project from seven US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada Syndromes, were assessed for potential pathogenicity by three laboratories with ion channel expertise and the ClinVar database. Relevant phenotypes were determined from electronic medical records, with data available through 9/10/2014. Exposure One or more variants designated as pathogenic in SCN5A or KCNH2. Main Outcome Measures Arrhythmia or electrocardiographic (ECG) phenotypes defined by ICD9 codes, ECG data, and manual electronic medical record review. Results Among 2022 study participants (median age, 61 years [interquartile range, 56–65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen κ = 0.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, −5% to +13%; P = .35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, −10 milliseconds; 95% CI, −16 to +3 milliseconds; P = .17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds. Conclusions and Relevance Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.