Gabriel Cuéllar-Partida

This study compares polygenic risk scores for Parkinson's disease across seven ancestries, finding varying predictive accuracy and highlighting the need for ancestry-specific models.

DINGO improves locus discovery in GWAS of perinatal traits by combining maternal and fetal GWAS data through meta-analysis and novel statistical tests, identifying more genetic loci than separate analyses.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

This paper conducts a multi-ancestry genome-wide meta-analysis of Parkinson's disease, identifying 78 independent loci and 23 genes associated with PD risk.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

This study identifies 14 traits with potential causal genetic effects on stroke risk using GWAS and causal inference methods, highlighting shared genetic architectures and biological pathways.

This study identifies 49 genetic loci associated with sleep apnoea risk using multi-trait GWAS, with 29 replicated in a large cohort, and finds genetic correlations with several complex traits.

This study identifies 135 genomic loci associated with venous thromboembolism risk through cross-ancestry GWAS and provides insights into biological pathways and potential therapeutic targets.

This study introduces UnTANGLeD, an unsupervised method that organizes genes into programs based on gene-trait associations, revealing 173 clusters with distinct biological functions and disease relevance.

This study identifies 12,111 SNPs associated with height from 5.4 million individuals, explaining most common SNP-based heritability and showing ancestry-specific prediction accuracy.

This study identifies 54 new genetic risk loci for cataract through a multiethnic GWAS meta-analysis and finds sex-specific effects and gene expression relevance in mouse models.

This study uses GWAS summary data to identify 110 traits with significant causal associations with obesity, revealing its broad impact on cardiovascular, anthropometric, and psychiatric conditions.

This study identifies genetic correlations and causal associations between circadian preference and white matter microstructure using GWAS and DTI data.

This paper estimates indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half-sibling pairs, demonstrating the feasibility of this approach with GWAS data.

The paper develops an automated pipeline using the latent causal variable approach to infer phenome-wide causal relationships, applying it to dental caries and periodontitis to show they are downstream consequences of other traits.

This study identifies 48 genetic variants associated with handedness using a large GWAS, highlighting polygenic nature and genetic links to neuropsychiatric traits.

This study identifies genetic and environmental risk factors for chronic pain using GWAS and Mendelian randomization, finding associations with depression, anxiety, smoking, and BMI.

This study identifies 42 genetic loci associated with snoring using UK Biobank data and finds that body fat mass influences snoring through Mendelian randomisation.

A web platform bundling LD-score regression, DEPICT, MetaXcan, SMR, GSMR and fastBAT over uploaded or 1,500-plus public GWAS summary statistics, with precomputed gene, pathway and tissue results.

The paper develops a method to partition phenotypic variance attributable to parent-of-origin effects using genomic relatedness matrices.

The paper identifies 25 genetic loci associated with subcortical brain structure volumes in over 40,000 individuals, revealing novel biology and potential drug targets.

The paper identifies 35 novel and 50 known imprinted loci using DNA methylation and GWAS data, showing parent-of-origin effects that enable parental allelic transmission modeling in unrelated individuals.