Lucila Ohno-machado A

The paper identifies STARD9 as a causal gene for dyslipidemias and atherosclerosis, showing how its variants alter lysosomal positioning and cholesterol trafficking with opposing effects on HDL and triglycerides.

This study introduces a DNA foundation model to assess functional genetic variation across diverse populations, demonstrating its utility in understanding Alzheimer's disease risk and ancestry-specific gene expression.

This study finds that higher polygenic susceptibility to type 2 diabetes is associated with worse glycemic control and higher risk of cardiovascular events in stroke survivors.

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

The paper analyzes whole-genome sequences from 98,622 participants to identify common and rare variants associated with cardiometabolic traits, replicating known associations and finding new loci.

This paper develops secure genotype imputation methods using homomorphic encryption, showing comparable accuracy and efficiency to non-secure methods while protecting genetic data privacy.

The paper introduces metadata elements in the DATS schema to describe data access, use conditions, and consent for biomedical data reuse.

The paper proposes a framework to enrich public biomedical datasets with admixture information and diversity scores, using The Cancer Genome Atlas data to demonstrate its utility.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

The paper introduces DATS, a metadata model for dataset discoverability, and describes its implementation for the DataMed data discovery index.

DataMed enhances data discoverability by creating a unified index of biomedical datasets across repositories, supporting FAIR principles for better knowledge discovery.