Ellen M. Schmidt

The Michigan Genomics Initiative links genotypes with electronic clinical records in Michigan Medicine patients, showing higher disease prevalence and validating GWAS findings.

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 study identifies 277 T2D loci using diverse populations and improves fine-mapping to assess causal genes and mechanisms, enhancing clinical translation.

This study conducts a meta-analysis of EHR-based lab measurements across two biobanks, identifying novel genetic associations and validating EHR data as a source for GWAS.

The paper presents an open resource for fine-mapping and gene prioritization at GWAS loci, identifying causal variants and genes and improving drug target identification.

This study identifies 111 genetic loci and 163 risk variants associated with atrial fibrillation using a GWAS of over 1 million individuals.

This study uses polygenic risk scores (PRS) for cancers in a phenome-wide association study (PheWAS) to identify associations with multiple phenotypes, finding significant links to both cancer and non-cancer diagnoses.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.

This study identifies a genetic variant associated with delay discounting in European ancestry adults and links it to multiple traits and disorders.

The paper introduces a fast score test-based method for PheWAS with unbalanced case-control ratios, showing it controls type I error and replicates known associations.