Elisabeth A. Rosenthal

The paper constructs a high-resolution human pangenome structural variant resource, identifies disease-associated SVs, and demonstrates its utility in filtering variants from probands and associating SVs with traits in a large cohort.

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

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 optimizes variant prioritization parameters for Exomiser and Genomiser to improve diagnostic yield in rare disease cases using WES and WGS data.

The paper uses a polygenic risk score for colorectal cancer in a PheWAS to find genetic correlations with other phenotypes from electronic health records.

Pooling Fiber-seq gDNA with concatenated MAS-Seq cDNA in one PacBio run gives phased genome, methylome, chromatin, and transcriptome, exposing four gene disruptions from one X;13 translocation.

The paper compares ancestry calibration methods for a colorectal cancer polygenic risk score using All of Us data, finding that using admixture and diverse training sets improves normality and tail accuracy.

This study investigates the clinical consequences of a genetic predisposition to lower white blood cell counts using a polygenic score and finds associations with bone marrow biopsy outcomes and treatment responses.

The paper uses full-length isoform sequencing to identify a splicing defect in MFN2 causing CMT2A in a patient with undiagnosed neuropathy.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This study improves polygenic risk scores for colorectal cancer by combining Asian and European ancestry data, enhancing risk prediction across diverse racial and ethnic groups.

This study identifies 118 candidate causal genes and 21 novel lipid loci on the X chromosome through a multi-ancestry meta-analysis of blood lipid levels, revealing sex-biased effects and pleiotropy with cardiovascular and hormonal traits.

This paper integrates GWAS data with functional genomic datasets to identify regulatory mechanisms and causal genes underlying lipid-related traits.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.