Ana Ines Vazquez

This paper improves polygenic score prediction for underrepresented groups using transfer learning methods implemented in the GPTL R-package, showing better performance than traditional approaches.

This paper uses local Bayesian regressions to identify genomic regions with significant genetic covariance between chronic kidney disease and hyperuricemia, finding both novel and previously reported shared loci.

The paper uses multi-omic integration to reclassify tumors into eight groups based on molecular signatures, revealing distinct clinical and genomic characteristics beyond tissue of origin.

This paper develops a local Bayesian regression method to detect sex-specific genetic interactions in complex traits, finding many new interactions with small effects.

The paper introduces two methods for analysis of variance in high-dimensional data and evaluates their performance on genomic datasets.

The paper quantifies genetic architecture differences between European-Americans and African-Americans for four traits using random-effect interaction models, finding significant effect heterogeneity.

The paper constructs genomic predictors for human height, heel bone density, and educational attainment using machine learning, capturing significant variance and resolving part of the missing heritability problem.