Manuel A. Rivas

This study uses multi-ancestry admixture mapping in the UK Biobank to identify ancestry-associated disease loci, finding 13 genome-wide significant associations and four novel risk loci.

The paper uses information theory to show that biological organization cannot be fully specified by an organism's own program, but instead arises from a coarse-grained specification by a shared physical background.

CuGen is a GPU-accelerated framework for large-scale genomics that enables efficient GWAS and fine-mapping with robust confounding control.

This paper develops an in silico framework to evaluate the impact of polygenic risk score (PRS)-guided prognostic enrichment on clinical trial design using genomic and electronic health record data.

The paper identifies a loss-of-function variant in GFRAL associated with increased alcohol consumption in humans and shows GDF15 reduces alcohol drinking in mice.

The paper uses genome-wide meta-analysis and machine learning to study prescription opioid use in 860,000 individuals, identifying genetic loci and distinguishing between pain-related and dose-intensity traits.

This paper introduces a spherical-harmonics regression method for PheWAS to detect localized genetic effects and summarize spatial complexity using rotation-invariant descriptors.

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

The paper introduces efficient storage and regression methods for whole genome sequencing studies, reducing computational costs and enabling broader access to genetic research.

The paper develops a method to identify genetic predictors of cardiometabolic disease progression using time-to-event data from the UK Biobank, finding ten polygenic hazard scores that significantly predict disease progression.

The paper presents a unified meta regression model for rare variant association studies using pathogenicity, constraint, and variant type features to analyze 1,144 UK Biobank phenotypes.

This study conducts a genome-wide meta-analysis across three biobanks to identify new genetic loci associated with lumbar disc herniation, expanding the known genetic landscape.

This study identifies nine genetic loci associated with transient global amnesia and highlights a vascular role in memory through pericyte cell enrichment.

The paper presents a unified meta-regression model that identifies epilepsy-associated genes by integrating constraint predictions, pathogenicity scores, and variant annotations from multiple datasets.

The paper trains machine learning models on multi-omics UK Biobank data to predict disease risk, finding metabolomics provides limited improvement over standard features but can replace them when unavailable.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

This study integrates rare large-effect expression variants into polygenic risk scores, showing improved prediction of complex traits by including these variants in risk assessment.

The paper develops sparse polygenic risk scores for 813 traits using UK Biobank data, showing significant predictive performance and a correlation between variant count and performance.

The paper introduces a Bayesian model comparison approach, MRP, for rare variant association studies using summary statistics, demonstrating improved power and discovery of gene-trait associations across multiple phenotypes.

Cross-population GWAS of 220 phenotypes in BioBank Japan, UK Biobank, and FinnGen (n=628,000) identifies 5,343 previously unreported loci and decomposes summary statistics into latent genetic components for disease subtyping.

The paper introduces a Bayesian mixture model to cluster rare-variant effects across multiple phenotypes and demonstrates its application using UK Biobank data.

The paper assesses NMD efficiency for PTVs across tissues and individuals, finding high concordance and demonstrating in silico prediction using peripheral tissues.

The paper develops efficient solvers for large-scale regularized regressions on genetic data, reducing memory usage and improving computational speed.

This study performs GWAS on three molecular traits, identifying core genes and pathways while highlighting a polygenic background contributing to trait variance.

This paper introduces a latent factor model for polygenic risk scores that decomposes genetic risk into interpretable components, showing improved interpretability over standard PRS for traits like BMI and myocardial infarction.

This paper introduces a scalable framework for sparse regression on large-scale data, demonstrating improved prediction performance on UK Biobank traits using the BASIL method and snpnet package.

This study identifies sex-specific genetic variants and causal links for testosterone using UK Biobank data and Bayesian modeling.

This study identifies genetic variants in APOE and LPA associated with statin selection and higher intensity statin doses in the UK Biobank.

The paper introduces a group-regularized Cox regression method for survival analysis of rare events using common responses, showing improved prediction performance on UK Biobank data.