Jennifer A. Pacheco

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

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.

The paper identifies a Neandertal introgressed haplotype in the OAS1 gene associated with increased risk of severe COVID-19 and shows it impairs SARS-CoV-2 clearance through genetic regulation of OAS1 expression.

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

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

The paper identifies pathogenic variants in arrhythmia genes in eMERGE-III participants and reclassifies variants of uncertain significance using functional studies.

The paper uses longitudinal biobank data to analyze genetic associations with cardiovascular phenotypes, identifying novel variants near WLS and MYO10 genes.

This study uses NLP to identify diverticular disease from EHRs and performs GWAS and PheWAS across multiple ancestries to find genetic risk variants and clinical associations.

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 assesses agreement between EHR-derived race and genetic ancestry, finding high concordance for European ancestry but lower for African ancestry.