Jason Torres

This study conducts GWAS on blood pressure traits in 140,559 admixed Mexican adults, identifying novel genetic signals and highlighting ancestry-specific heterogeneity.

The paper improves metabolomic biomarker discovery in admixed American cohorts by training models on diverse data, achieving better predictive performance and more significant associations with cardiometabolic diseases.

The paper investigates the effect of the PKHD1L1 splice donor variant rs17368310 on female fertility, showing reduced child count in homozygous carriers across Finnish and Mexican populations.

The paper introduces Tractor-Mix, a mixed model method for GWAS in admixed cohorts with high relatedness, improving ancestry-specific association detection and controlling false positives.

This study identifies 86 T2D GWAS associations in 125,000 admixed Mexican adults, including 21 novel signals, and highlights the role of common regulatory variation in the endocrine pancreas.

This study identifies 207 height-associated genes using rare protein-altering variants from over 1.4 million individuals, showing large effect sizes and overlap with Mendelian height syndromes.

The paper estimates direct effects of genetic ancestry on complex traits in a Mexican population using family-based analyses and finds significant associations for height and type 2 diabetes but not for educational attainment.

The paper applies the MR-Horse method to reduce selection bias in GWAS of disease progression by accounting for genetic correlation and pleiotropy, showing reduced bias and controlled type 1 error rates.

This study compares clonal haematopoiesis (CH) between admixed Americans and Europeans, finding ancestry-specific genetic factors and lower CH prevalence in admixed populations.

The paper identifies protein-truncating variants in BSN associated with severe adult-onset obesity, type 2 diabetes, and fatty liver disease, with no effect on childhood adiposity.

This paper presents the largest catalog of human protein-coding variation from 985,830 individuals, identifying rare variants and their functional implications for precision medicine.

The paper identifies rare coding variants in CHRNB2 associated with reduced smoking likelihood and links them to downstream smoking-related phenotypes.

The paper uses targeted chromatin interaction profiling to identify causal genes at type 2 diabetes GWAS loci with higher sensitivity and resolution than genome-wide methods.

This paper improves genetic scores for adiposity traits in East Asian populations by using trans-ancestry meta-analysis, increasing variance explained compared to European-derived scores.

The paper genotypes and sequences 140,000 adults from the Mexico City Prospective Study, revealing ancestry diversity and developing imputation resources for genetic studies.

The paper creates TIGER, a resource integrating RNA-seq and genotyping data from human pancreatic islets to identify eQTLs and their associations with type 2 diabetes.

The paper develops a framework to determine tissue-of-action for type 2 diabetes GWAS signals by integrating gene expression and epigenomic data, revealing key tissues and enhancing functional interpretation.

The paper uses deep learning models to predict regulatory variants in pancreatic islets and refines T2D association signals by integrating epigenomic data with genetic fine-mapping.

This study evaluates multi-SNP transcriptome-wide association models for gene expression in human tissues and their ability to identify causal genes for metabolites.

The paper integrates genetic, genomic, and functional data to identify convergent biological pathways in type 2 diabetes, revealing enriched functional connectivity among causal genes.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

The paper integrates chromatin accessibility and DNA methylation data from human pancreatic islets to refine regulatory mechanisms at Type 2 Diabetes susceptibility loci.

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 novel type 2 diabetes genes through integrative cross-tissue gene expression analysis using GWAS summary statistics.