David R. Crosslin

The paper develops a standardized case-level categorization metric for genomic results across the CSER2 consortium to assess diagnostic yield and identify resolvable factors in inconclusive cases.

This paper identifies challenges faced by novice genomic researchers with bioinformatics tools and proposes an evaluation rubric to improve software usability.

This study quantifies how patient-level factors and site-level practices affect diagnostic yield in exome and genome sequencing across five clinical sites.

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

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 20 genome-wide significant loci associated with serum IgA levels and finds genetic correlations with various immune and metabolic traits.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

This study uses EHR data to identify gene-phenotype associations and develop phenotype risk scores for variant interpretation in hereditary cancer genes.

Neptune delivers structured genomic data to EHR systems through customizable reports and variant reanalysis, supporting over 15,000 clinical reports.

This paper describes the implementation of HL7 FHIR standards for representing clinical genomics results in the eMERGE Networks, improving interoperability for genetic data management.

The paper develops CCFEA, a tool to analyze gene expression in Collaborative Cross founder strains infected with SARS-CoV or influenza A virus, linking genetic variation to infection and disease phenotypes.

This paper identifies novel genetic variants associated with PCOS using EHR-based GWAS and validates them in independent cohorts.

This study develops a polygenic and phenotypic risk score for PCOS using EHR data, showing improved detection and identifying shared metabolic and endocrine comorbidities.

The paper analyzes genetic variants at the PCSK9 locus to assess their phenotypic effects on lipid levels and disease outcomes, finding concordance with PCSK9 inhibitor trials for myocardial infarction but discordance for type 2 diabetes.

Atlas-CNV identifies single-exon CNVs in targeted sequencing data with reduced false discovery rate using C-scores and ExonQC thresholds.

The paper links genetic variation to pathogen-induced cellular traits and disease by integrating GWAS data with clinical and experimental validation.