Marylyn DeRiggi Ritchie

The paper introduces ARCHER-LD, a GPU-accelerated method to compute long-range linkage disequilibrium at biobank scale, achieving significant speedups over existing tools.

The paper reports NAT2 star allele frequencies across three biobanks, identifying common alleles and population-specific differences in metabolizer phenotypes.

This study evaluates the GIRA approach in a health system, finding 30.4% of patients labeled high-risk based on genomic data, with ancestry differences and social deprivation influences.

The paper identifies platform-dependent effects of APOL1 variants on plasma levels measured by different proteomic technologies.

The paper shows that suppressing both COX-1 and COX-2 does not cause intestinal injury in mice unless combined with specific NSAIDs, and that gut microbiome changes amplify NSAID-induced enteropathy.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

DRIVE-KG integrates multi-omics data into a knowledge graph to discover SNP-phenotype associations and predict endometriosis status, uncovering new variant-phenotype links and improving classification performance.

CASTER-DTA predicts drug-target affinity using equivariant graph neural networks, achieving state-of-the-art performance and generating a resource of binding affinities for all FDA-approved drugs against the human proteome.

This paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

This study develops a protein risk score model to predict cardiovascular events in CKD patients, showing better performance than clinical and polygenic risk models.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This study performs a genome-wide association study for Long COVID, identifying a significant association with the FOXP4 locus and highlighting the role of pulmonary dysfunction and COVID-19 severity.

The paper identifies a higher prevalence of NF1 pathogenic variants using a genotype-first approach, finding many cases with somatic mosaicism and increased cancer risk.

PennPRS provides a cloud platform for training polygenic risk scores without individual-level data, validated with simulations and real data across 6,000 phenotypes.

The paper examines how covariates affect polygenic score performance across European and African cohorts, finding significant differences in R2 and interaction effects, and proposes methods to improve model performance.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

This paper introduces CCC, a machine learning-based correlation coefficient that detects both linear and nonlinear patterns in gene expression data, outperforming linear-only methods in identifying biologically relevant relationships.

This study uses multi-omics data to connect MRI-derived cardiac measures with gene and protein changes in heart failure, identifying shared pathways and novel insights into disease mechanisms.

This study identifies sixteen genome-wide significant loci associated with brain age gaps derived from MRI modalities and highlights potential causal relationships between brain aging and diseases.

This study uses PheWAS to find cross-phenotype associations between Alzheimer's Disease and other conditions, identifying immune and cardiac disease links.

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

This study investigates mtDNA copy number (mtCN) in a diverse cohort, finding ancestry-specific genetic and phenotypic associations and highlighting the impact of blood cell composition on results.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

The paper identifies epistatic SNP-SNP interactions in long-range LD across five diseases using UK Biobank and eMERGE data, finding associations involving conserved genes and essential genes.

The paper evaluates the frequency and impact of pharmacogenetic alleles in a diverse biobank population, finding high prevalence of actionable alleles and disparities by ancestry.

This study evaluates how ancestry, GWAS sample size, and LD reference panels affect BMI PRS performance across different age groups and ancestries.

The paper introduces a mediation model to estimate the effect size of hidden causal factors between SNPs and a continuous trait, using UK Biobank data to identify a potential hidden mediator for waist-hip ratio.

ColocQuiaL performs colocalization analysis between GWAS and eQTL/sQTL data to identify candidate causal genes for GWAS signals.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

The paper identifies a Neandertal introgressed haplotype in the OAS1 gene associated with increased risk of severe COVID-19 and shows it impairs SARS-CoV-2 clearance through genetic regulation of OAS1 expression.