Carla Márquez‐Luna

The paper introduces the omnicausal model to assess how phenome-wide risk factors collectively explain causality in complex diseases, showing they explain more variance than known risk factors.

The paper evaluates how updates to the Sherloc variant classification framework reduced variants of uncertain significance (VUS) and improved accuracy over time.

This study uses Mendelian randomization to identify causal effects of plasma triglycerides on 2,600 disease traits, finding 19 significant associations including cardiovascular and non-atherosclerotic diseases.

The paper assesses and improves Mendelian disease-derived pathogenicity scores for common disease using stratified LD score regression and a gradient boosting framework.

This paper introduces PolyFun, a method that improves fine-mapping accuracy by integrating genome-wide functional data, leading to more causal variant identifications and polygenic localization of trait heritability.

LDpred-funct adds baseline-LD functional priors to polygenic scores: average prediction R2 0.144 across 21 UK Biobank traits, a 4.6% relative gain over SBayesR.

This paper compares S-LDSC and LDAK models, showing the baseline-LD model better fits UK Biobank data and produces more consistent functional enrichment estimates.

Combining large European GWAS summary statistics with a small training sample from the target population raised type 2 diabetes prediction R2 from 0.027 to 0.047 in Latinos.