Matthew Aguirre

The paper investigates how gene regulatory network topology influences the genetic architecture of gene expression, finding that local motifs and hub regulators shape heritability distribution.

The paper benchmarks five machine learning-based variant annotation methods for rare variant association testing across 350,377 UK Biobank participants, finding CADD provides best signal separation and GPN-MSA highest enrichment for loss-of-function intolerant genes.

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 simulates gene regulatory networks and models perturbation effects to understand network structure and function.

The paper introduces epiTree, a pipeline using tree-based models to detect higher-order epistatic interactions in genomic data, validated on UK Biobank data for red hair and MS prediction.

The paper identifies novel transcripts, eQTLs, and caQTLs in African populations and fine-maps GWAS signals for complex diseases.

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 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.

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 evaluates if GWAS using digital phenotyping data from UK Biobank improves genetic association detection for diseases.

The paper uses DeGAs to identify genetic components across 2,138 phenotypes in the UK Biobank, revealing novel adipocyte biology through variant and gene contributions to obesity-related traits.

The paper assesses the phenome-wide effects of copy number variations in 472,228 UK Biobank participants, identifying known and novel associations with medical traits.

UK Biobank genetics of 38 blood and urine lab tests yields 1,857 independent loci; combining biomarker and disease polygenic scores improves FinnGen prediction of incident renal failure and alcoholic cirrhosis.

This paper identifies significant heritability of suicide attempt using two independent datasets and shows genetic overlap with insomnia and psychiatric disorders.

Global Biobank Engine enables browsing of genotype-phenotype relationships using biobank summary statistics from large cohorts like UK Biobank.