Lisa A. Bastarache

This paper evaluates the diagnostic accuracy of large language models for rare diseases through a systematic review and meta-analysis, finding substantial variation in performance across benchmarks and high risk of bias in all studies.

The paper identifies TMEM208 as a transcriptome-wide predictor of hydrocephalus in African ancestry patients, linking it to inflammation and infection pathways.

This study sequenced 35,024 individuals with predominantly African ancestry to address gaps in genomics and healthcare, finding under-documented disease variants and novel associations with drug adverse events and glycemic control.

This study identifies a novel tumor-predisposing syndrome linked to long telomeres through common and rare genetic variants.

This study quantifies the expansion of clinical genetic testing at Vanderbilt University Medical Center from 2002 to 2022, showing increased testing rates, broader disease coverage, and a growing number of variants of uncertain significance.

The paper introduces a phenotype-genotype reference map for replicating GWAS results in biobank data and demonstrates its utility in detecting data corruption and assessing reproducibility.

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 develops an EHR-based model to study the diagnostic trajectory of Mendelian disorders and finds that phenotypic features are often documented after clinical suspicion, a phenomenon termed phenotypic convergence.

The paper introduces the phers R package, which enables PheRS-based analysis of linked EHR and genetic data to study Mendelian disease and rare variants.

This study conducts a trans-ancestry GWAS using chronic ALT elevation as a proxy for NAFLD, identifies 77 significant loci, and validates 17 SNPs with histology and imaging data, revealing genetic links to metabolic and inflammatory traits.

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.

The paper develops a model to identify patients likely to have a genetic disease using EHR data, achieving high accuracy in predicting genetic testing needs.

This paper conducts a genome-wide association study (GWAS) to identify genetic risk factors for trigeminal neuralgia, finding two significant loci associated with the condition.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

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.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

The paper identifies 70 gene-level associations with 26 infectious disease traits using transcriptome-wide association studies and explores their molecular basis and implications for host-pathogen interactions.

The paper develops and evaluates mappings from ICD-10 and ICD-10-CM codes to PheCodes, showing high coverage and reproducibility for chronic diseases.

This study shows Cox regression improves power to detect genotype-phenotype associations in EHR-linked genomic studies compared to logistic regression.

WikiMedMap maps phenotype strings to clinical vocabularies using Wikipedia, outperforming existing tools in matching accuracy.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

The paper identifies HFE E168Q as a novel pathogenic variant associated with hereditary hemochromatosis using Phenotype Risk Scores and validates its mechanism through experimental assays.

This study uses EHR data to analyze sleep behaviors and their associations with health outcomes, finding links between weekend sleep patterns and mental health.

This paper introduces SAIGE, a method that improves the accuracy of genetic association tests in unbalanced case-control studies by using saddlepoint approximation, and demonstrates its effectiveness on UK Biobank data.