Incomplete penetrance, or absence of disease phenotype in an individual with a disease-associated variant, is a major challenge in variant interpretation. Studying individuals with apparent incomplete penetrance can shed light on underlying drivers of altered phenotype penetrance. Here, we investigate clinically relevant variants from ClinVar in 807,162 individuals from the Genome Aggregation Database (gnomAD), demonstrating improved representation in gnomAD version 4. We then conduct a comprehensive case-by-case assessment of 734 predicted loss of function variants in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. Here, we identify explanations for the presumed lack of disease manifestation in 701 of 734 variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders are rare, underscoring the need and power of deep case-by-case assessment presented here to minimize false assignments of disease risk, particularly in unaffected individuals with higher rates of secondary properties that result in rescue.

BackgroundTranscriptomic analysis is common in large cohort studies but is generally restricted to cells in blood, which limits inferences about organs of interest, and direct organ sampling is mostly infeasible in large cohorts. New techniques for RNA-seq from noninvasive biosamples may provide the opportunity to profile transcriptomes of additional tissues for more organ-relevant insights at scale. We investigated the feasibility and utility of hair follicle gene expression profiling in a multi-center study of chronic obstructive pulmonary disease (COPD).MethodsBulk RNA-seq was performed on hair follicles collected in the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS), a multi-center longitudinal study of COPD (n = 97). The resulting hair follicle gene expression data were characterized and compared both to gene expression in whole blood and bronchial epithelium previously measured in SPIROMICS and to Genotype-Tissue Expression (GTEx) project tissue gene expression by principal component analysis and single-sample gene enrichment analysis, used to estimate hair follicle cell type proportions, and tested for association with disease-relevant lung phenotypes. eQTL discovery was also performed and colocalization with a genome-wide association study for lung function was tested.ResultsHair follicles reliably produced transcriptomic data of sufficient quality and number for cell type composition, which revealed mostly epithelial and fibroblast cells. Comparison to other tissues previously profiled in SPIROMICS and GTEx project demonstrated transcriptomes from hair follicles were much more similar to those from lung parenchyma than blood. Combining these data with rich clinical, imaging, and genomic profiling in SPIROMICS, we found that they provided an attractive approach for discovery of associations with complex lung phenotypes, particularly of the airways. Finally, we investigated hair follicle genetic architecture through expression quantitative trait locus (eQTL) discovery and demonstrated better colocalization with lung-related genetic associations than blood.ConclusionHere, we demonstrated that RNA-seq applied to hair follicle transcriptomic profiling can be scaled up successfully in a multi-center study to yield inferences not available from blood transcriptomics.

Inference of directed biological networks is an important but notoriously challenging problem. The recent proliferation of large-scale CRISPR perturbation data provides a new opportunity to tackle this problem by leveraging the transcriptional response to the presence of a gene-targeting guide. Here, we introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre discovers a network with small-world and scale-free properties. We integrate our network estimate with external data, finding relationships between gene eigencentrality and both measures of gene essentiality and gene expression heritability. Our analysis helps to elucidate the structure of networks that may underlie complex traits.

Background and Aims Translating human genomic discoveries into mechanistic insights requires linking genetic variations to candidate genes and their causal functional phenotypes. Genome-wide association studies have consistently identified LIPA (lipase A, lysosomal acid type) as a risk locus for coronary artery disease, with previous analyses prioritising LIPA as a likely causal gene. However, functional studies elucidating causal variants, regulatory mechanisms, target cell types, and their causal impact on atherosclerosis have been lacking. This study aims to address this gap by establishing the variant-to-function relationship at the LIPA locus.Methods Post-genome-wide association study pipelines and molecular biology techniques, including expression quantitative trait loci analysis, Tri-HiC, luciferase assay, CRISPRi, allele-specific binding, motif analysis, and electrophoretic mobility shift assay, were used to link functional variants to target genes and define the direction of their regulatory effects in causal cell types. To determine how increased myeloid LIPA impacts atherosclerosis, myeloid-specific Lipa overexpression mice on an Ldlr-/- background were generated.Results Coronary artery disease-risk alleles in the LIPA locus increase LIPA expression and enzyme activity specifically in monocytes/macrophages by enhancing PU.1 binding to an intronic enhancer region that interacts with the LIPA promoter. Myeloid-specific Lipa overexpression in Ldlr-/- mice fed a western diet resulted in larger atherosclerotic lesions, accompanied by altered macrophage function, characterized by increased accumulation of lesional macrophages derived from circulating monocytes, reduced neutral lipid content, and up-regulation of integrin and extracellular matrix pathway genes.Conclusions The work establishes a direct causal link between LIPA-risk alleles and increased monocyte/macrophage LIPA that exacerbates atherosclerosis, bridging human functional genomic evidence to the mechanistic understanding of coronary artery disease.

Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each gene’s DE fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes, not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing data sets from in vitro stimulus response and neuropsychiatric disease experiments. Compared to the standard approach, our method adjusts the bias in discovery toward highly variable genes and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. Tissue-specific recalibration increases detection of known disease-relevant processes. Altogether, our method provides a novel view on DE and contributes toward bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease-causing molecular processes and enhance the discovery of therapeutic targets.

Understanding the relationship between genotypes and phenotypes is crucial for advancing personalized medicine. Expression quantitative trait loci (eQTL) mapping plays a significant role by correlating genetic variants to gene expression levels. Despite the progress made by large-scale projects, eQTL mapping still faces challenges in statistical power and privacy concerns. Multi-site studies can increase sample sizes but are hindered by privacy issues. We present privateQTL, a novel framework leveraging secure multi-party computation for secure and federated eQTL mapping. When tested in a real-world scenario with data from different studies, privateQTL outperformed meta-analysis by accurately correcting for covariates and batch effect and retaining higher accuracy and precision for both eGene-eVariant mapping and effect size estimation. In addition, privateQTL is modular and scalable, making it adaptable for other molecular phenotypes and large-scale studies. Our results indicate that privateQTL is a practical solution for privacy-preserving collaborative eQTL mapping.

Understanding the role of transcription and transcription factors (TFs) in cellular identity and disease, such as cancer, is essential. However, comprehensive data resources for cell line-specific TF-to-target gene annotations are currently limited. To address this, we employed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and TF binding data, we generated regulons for 40 common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving TF knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable pipeline and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.

The genetic contributions to early onset heart failure (HF) are incompletely understood. Genetic testing in advanced HF patients undergoing heart transplantation (HTx) may yield clinical benefits, but data is limited. We performed deep-coverage whole genome sequencing (WGS) in 102 Swedish HTx recipients. Gene lists were compiled through a systematic literature review. Variants were prioritized for pathogenicity and classified manually. We also compared polygenic HF risk scores to a population-based cohort. We found a pathogenic (LP/P) variant in 34 individuals (34%). Testing yield was highest in hypertrophic (63% LP/P carriers), dilated (40%) and arrhythmogenic right ventricular (33%) cardiomyopathy and lower in ischemic cardiomyopathy (10%). A family history was more common in LP/P variant carriers than in non-carriers but was present in less than half of carriers (44% vs 13%, P < 0.001), whereas age was similar. Polygenic risk scores were similar in HTx recipients and the population cohort. In conclusion, we observed a high prevalence of pathogenic cardiomyopathy gene variants in individuals with early-onset advanced HF, which could not accurately be ruled out by family history and age. In contrast, we did not observe higher polygenic risk scores in early onset advanced HF cases than in the general population.