Lea M. Starita

The paper measures variant effects across the MAPK pathway in human cells, revealing how cell context influences variant function and identifying 14,412 previously unknown dominant negative variants.

MaveMD provides a clinical interface for MAVE data to classify variants of uncertain significance and improve genetic medicine outcomes.

The paper evaluates how different truthset strategies affect evidence points (EPs) in clinical validation of functional assays across 2,120 gene-truthset-assay combinations.

The paper establishes consensus guidelines for constructing truthsets to validate functional assays in cancer variant interpretation, focusing on missense variants and clinical validation frameworks.

Abundance scores for 8,891 TSC2 missense variants, plus mTOR pathway activity scores for 391, were calibrated to ACMG/AMP evidence. They putatively reclassify 212 of 276 clinical-cohort missense VUS.

The paper uses saturation genome editing to assess the functional impact of RAD51D and XRCC2 variants, showing high accuracy in distinguishing pathogenic from benign variants.

The paper surveys genetics professionals on their use of functional evidence in variant classification for hearing and vision loss, highlighting challenges in data quality, accessibility, and integration into clinical practice.

The paper recalibrates BAP1 MAVE data for different disease phenotypes to enable accurate variant classification across related disorders.

This study explores patient experiences with genetic variant reclassifications, finding that re-contact practices significantly impact trust and perceived value of genetic testing.

The paper introduces VIS-seq, an image-based method to assess variant effects on cellular phenotypes, revealing multidimensional variant impacts across different diseases.

The paper develops TRoLR, a pipeline using long-read sequencing to detect and prioritize pathogenic tandem repeat expansions in diverse populations, identifying known and novel expansions with clinical relevance.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

This paper maps 6,888 RAD51D variants to identify biochemical functions critical for homologous recombination and olaparib response, revealing key regions and mechanisms of variant impact.

The paper develops iPSC-SGE to assess variant effects in diverse genetic and cell contexts, identifying pathogenic variants and essential disease genes.

The paper uses saturation genome editing to evaluate BARD1 variants, resolving most variants of uncertain significance and confirming their role in tumor suppression.

This study profiles 5.3 million cells from 55 rhesus macaques across their lifespan to map age-related changes in brain cell types and identify molecular signatures linked to aging and Alzheimer's disease.

This study shows plasmids can form chromatin structures in mammalian cells and uses plasmid Fiber-seq to map these structures and their functional consequences.

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

This study identifies barriers to using functional data in clinical variant interpretation and highlights the need for better access and standardized interpretation guidelines.

The paper surveys genetic professionals on functional evidence use in variant classification and identifies the need for expert recommendations and guidelines.

The paper analyzes variant classification disparities between European-like and non-European-like genetic ancestries and shows that MAVE data can reduce these disparities in VUS reclassification.

The paper describes a CRISPRa screening method combining multiplex perturbations with sc-RNA-seq to identify cell-type-specific regulatory elements and their target genes.

The paper introduces an information content score to quantify the clinical value of MAVEs, showing it captures more information than just reclassifying variants.

This study uses quantitative proteomics to analyze human and mouse gastruloids at four stages, revealing protein dynamics, discordance with transcriptomes, and gene networks linked to developmental defects.

The paper uses single-nucleus ATAC-Seq and RNA-Seq to analyze age and sex differences in human heart cells, identifying gene expression and chromatin changes linked to these factors.

The paper creates a single-cell multi-omic atlas of the rhesus macaque brain, identifying cell-type-specific regulatory elements and providing insights into brain evolution and disease.

This study uses hiPSC-derived cardiomyocytes to assess MYH7 variants' effects, showing pathogenic variants reduce protein abundance and cell survival.

This paper introduces prime-SGE, a prime editing framework for assessing drug resistance variants across multiple oncogenes in a single experiment.

CRaTER enriches for on-target gene-edited cells in hiPSCs, enabling efficient generation of variant libraries for studying cardiomyopathy-associated mutations.