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.

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

Peripheral T cell lymphoma not otherwise specified (PTCL-NOS) comprises heterogeneous lymphoid malignancies characterized by pleomorphic lymphocytes and variable inflammatory cell-rich tumor microenvironment. Genetic drivers in PTCL-NOS include genomic alterations affecting the VAV1 oncogene; however, their specific role and mechanisms in PTCL-NOS remain incompletely understood. Here we show that expression of Vav1-Myo1f, a recurrent PTCL-associated VAV1 fusion, induces oncogenic transformation of CD4+ T cells. Notably, mouse Vav1-Myo1f lymphomas show T helper type 2 features analogous to high-risk GATA3+ human PTCL. Single-cell transcriptome analysis reveals that Vav1-Myo1f alters T cell differentiation and leads to accumulation of tumor-associated macrophages (TAMs) in the tumor microenvironment, a feature linked with aggressiveness in human PTCL. Importantly, therapeutic targeting of TAMs induces strong anti-lymphoma effects, highlighting the lymphoma cells' dependency on the microenvironment. These results demonstrate an oncogenic role for Vav1-Myo1f in the pathogenesis of PTCL, involving deregulation in T cell polarization, and identify the lymphoma-associated macrophage-tumor microenvironment as a therapeutic target in PTCL.

BACKGROUND:White blood cell (WBC) DNA may contain methylation patterns that are associated with subsequent breast cancer risk. Using a high-throughput array and samples collected, on average, 1.3 years prior to diagnosis, a case-cohort analysis nested in the prospective Sister Study identified 250 individual CpG sites that were differentially methylated between breast cancer cases and noncases. We examined five of the top 40 CpG sites in a case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort. METHODS:We investigated the associations between prediagnostic WBC DNA methylation in 297 breast cancer cases and 297 frequency-matched controls. Two WBC DNA specimens from each participant were used: a proximate sample collected 1 to 2.9 years and a distant sample collected 4.2-7.3 years prior to diagnosis in cases or the comparable timepoints in controls. WBC DNA methylation level was measured using targeted bisulfite amplification sequencing. We used logistic regression to obtain ORs and 95% confidence intervals (CI). RESULTS:A one-unit increase in percent methylation in ERCC1 in proximate WBC DNA was associated with increased breast cancer risk (adjusted OR = 1.29; 95% CI, 1.06-1.57). However, a one-unit increase in percent methylation in ERCC1 in distant WBC DNA was inversely associated with breast cancer risk (adjusted OR = 0.83; 95% CI, 0.69-0.98). None of the other ORs met the threshold for statistical significance. CONCLUSIONS:There was no convincing pattern between percent methylation in the five CpG sites and breast cancer risk. IMPACT:The link between prediagnostic WBC DNA methylation marks and breast cancer, if any, is poorly understood.

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

Added even more descriptive file naming

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data. One Sentence Summary New statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients

Fermented vegetables play a major role in global food systems and are widely consumed by various global cultures. In this study, we investigated an industrial facility that produces spontaneous fermented sauerkraut without the aid of starter cultures. This provides a unique system to explore and track the origins of an “in-house” microbiome in an industrial environment. Raw vegetables and the surfaces on which they are handled were identified as the likely source of bacterial communities rather than human contamination. As fermented vegetables increase in popularity on a global scale, understanding their production environment may help maintain quality and safety goals.

Diet is an important factor influencing the composition and function of the gut microbiome, but the effect of antimicrobial agents present within foods is currently not understood. In this study, we investigated the effect of the food-grade cationic antimicrobial ε-polylysine on the gut microbiome structure and predicted metagenomic function in a mouse model. The relative abundances of predominant phyla and genera, as well as the overall community structure, were perturbed in response to the incorporation of dietary ε-polylysine. Unexpectedly, this modification to the gut microbiome was experienced transiently and resolved to the initial basal composition at the final sampling point. In addition, a differential non-random assembly was observed in the microbiomes characterized from male and female co-housed animals, although their perturbation trajectories in response to diet remain consistent. In conclusion, antimicrobial ε-polylysine incorporated into food systems transiently alters gut microbial communities in mice, as well as their predicted function. This indicates a dynamic but resilient microbiome that adapts to microbial-active dietary components.

A scientific consensus has emerged that dietary components could guide the establishment and maintenance of microbial communities that colonize the distal gastrointestinal tract. Although ubiquitous in various food systems, it is unknown to what extent that antimicrobial compounds interact with endogenous gut microbiota once consumed. In this study, we investigated the influence of food‐grade biopolymers (i.e. ɛ‐polylysine, pectin, and ɛ‐polylysine‐pectin complexes) on the murine microbiota. Accordingly, CD‐1 mice were fed biopolymers incorporated into a high‐fat diet over a 9‐week time course. Microbial phylogenetic diversity present in fecal extracts were ascertained by high‐throughput amplicon sequencing of the 16s rRNA gene. Moreover, community function was predicted from relative abundances of taxa using PICRUSt. Interestingly, dietary ɛ‐polylysine transiently altered the gut microbiome prior to restoration of the initial microbial population structure at the end of the feeding trial. This is indicative of a community adaptive response to ɛ‐polylysine that is now known to be active against microbes in vivo. Moreover when the cationic ɛ‐polylysine is associated with the anionic polymer pectin, this micro‐bioactive perturbation is mitigated. Finally, the absolute population structure of these murine gut microbial communities was observed to be sex‐dependent, but did not alter the response trajectory to exogenous biopolymers.Support or Funding InformationUSDA