Ron Do

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 establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

This study identifies 619 IBD risk signals, resolves 81 high-confidence variants, and prioritizes 664 candidate effector genes using GWAS, eQTL, and exome sequencing.

The paper identifies a metabolomic signature of resilience to glaucoma in high-risk individuals and validates pyruvate's protective role in a mouse model.

This study uses Mendelian Randomization to identify causal relationships between circulating biomarkers and diseases, finding novel associations between glucose and cystatin C with bipolar disorder.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

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.

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.

The paper constructs a tissue-level phenome-wide network map linking genes and phenotypes using colocalized signals from the UK Biobank and Genotype-Tissue Expression project.

This study develops a method to detect recessive purifying selection in genes using population genetics simulations and applies it to empirical gene sets, finding significant enrichments for recessive selection in specific gene categories.

This paper introduces MOI-Pred, a tool for predicting recessive inheritance of missense variants, showing high accuracy in distinguishing recessive disease-causing variants.

The paper quantifies disease risk associated with 37,772 clinical variants across 72,434 individuals, revealing heterogeneity in penetrance and limitations of current variant classification systems.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper calculates a deleterious load score using purifying selection weights and finds strong associations with 27 medical traits in the UK Biobank.

This study identifies robust gene-phenotype associations by analyzing rare loss-of-function variants in exome-wide data linked to electronic health records.

The paper quantifies horizontal pleiotropy across 372 phenotypes using UK Biobank data and finds it is widespread, especially in polygenic traits and regulatory regions.

This study shows that natural language processing applied to electronic health records can accurately identify patients with non-alcoholic fatty liver disease and detect those at risk for disease progression.

This study identifies genetic mutations in 78% of Diamond-Blackfan anemia patients using whole exome sequencing, finds new RP genes, and reports genotype-phenotype associations.

This study uses unsupervised machine learning on EHR data to identify two subphenotypes of sepsis-associated AKI with differing mortality rates, emphasizing the importance of intra-patient variability.

The paper conducts a GWAS in non-European populations, identifies new genetic loci, and highlights the importance of diverse cohorts for understanding complex traits.

MR-PRESSO detects pleiotropic outlier instruments in summary-level Mendelian randomization; across 82 traits it found pleiotropy in 41% of significant causal estimates, with distortions from −168% to 189%.

This paper analyzes protein-coding genetic variation in 60,706 humans to catalog diversity, assess pathogenicity, and identify genes under strong selection.

The paper identifies homozygous null mutations in 961 genes in a consanguineous Pakistani cohort and reports their phenotypic consequences.

This paper introduces LDpred, a method that improves polygenic risk score accuracy by modeling linkage disequilibrium, showing increased R2 values in schizophrenia and multiple sclerosis datasets.

The paper introduces a statistical test to infer recessive selection by analyzing the response to population bottlenecks and re-expansion.

The paper tests if natural selection removed deleterious mutations less effectively in Europeans than West Africans, finding no significant difference.