Christopher A. Cassa

This paper uses prime editing to classify LDLR variants by measuring their impact on LDL-C uptake, reclassifying many previously unclassified variants.

NERINE tests gene network hypotheses with rare variant associations, identifying a subnetwork linked to Parkinson's disease.

This paper introduces ClinVar-BERT, a model that prioritizes variants for clinical review by analyzing variant summaries and distinguishing functional evidence, achieving high accuracy in predicting loss of function.

The paper uses language models to analyze ClinVar variant summaries, identifying evidence gaps in Variants of Uncertain Significance and reclassifying some as likely benign or pathogenic.

The paper uses population biobank data to identify and calibrate evidence of variant pathogenicity for 41 genes, finding that 2.6% of participants have rare variants with moderate or stronger evidence of pathogenicity.

The paper develops FUSE, a framework that jointly estimates and imputes variant functional effects using deep mutational scanning data, improving variant impact predictions and clinical discrimination.

This paper improves variant effect quantification in base editing screens by integrating genotypic and phenotypic data using a Bayesian network called BEAN.

The paper introduces REGatta, a method to estimate clinical risk in gene regions using population sequencing data, identifying significant risk differences in breast cancer genes for rare missense variants.

This study identifies 21 genes whose rare variants affect LDL-C levels by altering cholesterol uptake, using CRISPR screening and co-essentiality analysis.

DeMAG improves variant classification by integrating structural and evolutionary epistatic features, reducing false positives and improving accuracy for clinically actionable genes.

The paper investigates the link between genetic associations and regulatory function, finding limited evidence that baseline gene expression explains GWAS associations.

The paper uses prior ClinVar variant classifications to inform ACMG/AMP variant interpretation by identifying SNVs with similar functional consequences to pathogenic variants.

This paper integrates monogenic, polygenic, and clinical risk factors to predict disease risk for carriers of rare variants, identifying elevated risk in 18.22% of UK Biobank participants for breast cancer, familial hypercholesterolemia, and colorectal cancer.

The paper analyzes secondary-variant burden in BBS cohorts and finds enrichment of trans-acting rare nonsynonymous variants in chaperonin-encoding genes.

This study identifies genetic pathways involved in remdesivir cytotoxicity using CRISPR-Cas9 screening and transcriptomics, finding that SLC29A3 and AK2 are key factors in toxicity and efficacy.

The paper examines how polygenic risk modifies the penetrance of monogenic variants in coronary artery disease, breast cancer, and colorectal cancer across multiple cohorts.

The paper assesses the applicability of the mutation-selection balance model to human protein-truncating variants using simulations and data from exome datasets.

This paper reviews literature to improve clinical variant assessment by using citation data to enhance pathogenicity prediction.

The paper estimates genome-wide selection coefficients for heterozygous protein truncating variants using exome data from 60,706 individuals and identifies genes under strong selection.