Biology subjects

Einson, J.

Publications and source records attributed to Einson, J..

5 recordsLinked to original sources

bioRxiv2024

Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database

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 (pLoF) in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. We identified explanations for the presumed lack of disease manifestation in 701 of the variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders rarely occur, 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.

genomics↗

bioRxiv2023

Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants

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.

genetics↗

bioRxiv2022

The impact of genetically controlled splicing on exon inclusion and protein structure

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.

genomics↗

bioRxiv2019

Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

Rare genetic variation is abundant in the human genome, yet identifying functional rare variants and their impact on traits remains challenging. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants. Here, we expand detection of genetically driven transcriptome abnormalities by evaluating and integrating gene expression, allele-specific expression, and alternative splicing from multi-tissue RNA-sequencing data. We demonstrate that each signal informs unique classes of rare variants. We further develop Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function. Assessing rare variants prioritized by Watershed in the UK Biobank and Million Veterans Program, we identify large effects across 34 traits, and 33 rare variant-trait combinations with both high Watershed scores and large trait effect sizes. Together, we provide a comprehensive analysis of the transcriptomic impact of rare variation and a framework to prioritize functional rare variants and assess their trait relevance.\n\nOne-sentence summaryIntegrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

genomics↗

bioRxiv2019

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

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.\n\nOne Sentence SummaryNew statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients

genomics↗