MotivationBiclustering has become a major tool for analyzing large datasets given as matrix of samples times features and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. Factor Analysis for Bicluster Acquisition (FABIA), one of the most successful biclustering methods, is a generative model that represents each bicluster by two sparse membership vectors: one for the samples and one for the features. However, FABIA is restricted to about 20 code units because of the high computational complexity of computing the posterior. Furthermore, code units are sometimes insufficiently decorrelated and sample membership is difficult to determine. We propose to use the recently introduced unsupervised Deep Learning approach Rectified Factor Networks (RFNs) to overcome the drawbacks of existing biclustering methods. RFNs efficiently construct very sparse, non-linear, high-dimensional representations of the input via their posterior means. RFN learning is a generalized alternating minimization algorithm based on the posterior regularization method which enforces non-negative and normalized posterior means. Each code unit represents a bicluster, where samples for which the code unit is active belong to the bicluster and features that have activating weights to the code unit belong to the bicluster.ResultsOn 400 benchmark datasets and on three gene expression datasets with known clusters, RFN outperformed 13 other biclustering methods including FABIA. On data of the 1000 Genomes Project, RFN could identify DNA segments which indicate, that interbreeding with other hominins starting already before ancestors of modern humans left Africa.Availability and implementation https://github.com/bioinf-jku/librfn

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome-sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19; (ii) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

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IMPORTANCESequencing studies have identified causal genetic variants for distinct subtypes of heart failure (HF) such as hypertrophic or dilated cardiomyopathy. However, the role of rare, high-impact variants in HF, for which ischemic heart disease is the leading cause, has not been systematically investigated.OBJECTIVE To assess the contribution of rare variants to all-cause HF with and without reduced left ventricular ejection fraction. DESIGN, SETTING, AND PARTICIPANTSThis was a retrospective analysis of clinical trials and a prospective epidemiological resource (UK Biobank). Whole-exome sequencing of patients with HF was conducted from the Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity (CHARM) and Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA) clinical trials. Data were collected from March 1999 to May 2003 for the CHARM studies and September 2003 to July 2007 for the CORONA study. Using a gene-based collapsing approach, the proportion of patients with HF and controls carrying rare and presumed deleterious variants was compared. The burden of pathogenic variants in known cardiomyopathy genes was also investigated to assess the diagnostic yield. Exome sequencing data were generated between January 2018 and October 2018, and analysis began October 2018 and ended April 2020.MAIN OUTCOMES AND MEASURES Odds ratios and P values for genes enriched for rare and presumed deleterious variants in either patients with HF or controls and diagnostic yield of pathogenic variants in known cardiomyopathy genes. RESULTSThis study included 5942 patients with HF and 13 156 controls. The mean (SD) age was 68.9 (9.9) years and 4213 (70.9%) were male. A significant enrichment of protein-truncating variants in the TTN gene (P = 3.35 × 10 −13 ; odds ratio, 2.54; 95% CI, 1.96-3.31) that was further increased after restriction to variants in exons constitutively expressed in the heart (odds ratio, 4.52; 95% CI, 3.10-6.68). Validation using UK Biobank data showed a similar enrichment (odds ratio, 4.97; 95% CI, 3.94-6.19 after restriction). In the clinical trials, 201 of 5916 patients with HF (3.4%) had a pathogenic or likely pathogenic cardiomyopathy variant implicating 21 different genes. Notably, 121 of 201 individuals (60.2%) had ischemic heart disease as the clinically identified etiology for the HF. Individuals with HF and preserved ejection fraction had only a slightly lower yield than individuals with midrange or reduced ejection fraction (20 of 767 [2.6%] vs 15 of 392 [3.8%] vs 166 of 4757 [3.5%]). CONCLUSIONS AND RELEVANCEAn increased burden of diagnostic mendelian cardiomyopathy variants in a broad group of patients with HF of mostly ischemic etiology compared with controls was observed. This work provides further evidence that mendelian genetic conditions may represent an important subset of complex late-onset diseases such as HF, irrespective of the clinical presentation.