Alicia Huerta‐Chagoya

The paper estimates whole-genome heritability of complex traits across diverse populations, finding rare and non-coding variants significantly contribute to heritability.

This paper compares two Bayesian methods for polygenic risk scores (PRS) for hypertension across two ancestry groups, finding improved performance with updated LD panels and functional annotations, but not equitable results between groups.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

This study examines how genetic and lifestyle factors together influence the risk of developing type 2 diabetes in the UK Biobank population.

This paper improves type 2 diabetes polygenic risk scores by including rare and population-specific variants, showing better risk prediction in diverse populations.

This study uses Mendelian randomization to show that higher glycemic traits are inversely associated with thoracic aortic diameters and reduced risk of aneurysm and dissection.

The paper identifies genetic variants near SLC2A2 that influence glycemic response to glimepiride in type 2 diabetes patients.

This study performs a genome-wide association study for Long COVID, identifying a significant association with the FOXP4 locus and highlighting the role of pulmonary dysfunction and COVID-19 severity.

The paper conducts a large-scale rare variant GWAS meta-analysis of type 2 diabetes, identifying novel variants in monogenic diabetes genes and assessing their pathogenicity.

This study integrates multi-omics data to identify genetic variants and molecular mechanisms associated with type 2 diabetes and highlights potential drug targets.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

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

This study identifies 242 genetic loci for glycaemic traits using trans-ancestry meta-analysis, finding 99 novel loci and improved fine-mapping resolution compared to single-ancestry analyses.