Scott C. Ritchie

OmicsPred collects 3,339,469 genetic prediction models for transcript, protein and metabolite traits with harmonised metadata and MetaXcan/PGSC-Calculator files, plus multi-ancestry Million Veterans Program phenome-wide association results.

This study identifies genetic determinants of plasma protein variance across ancestries and shows their impact on cardiometabolic disease risk.

This study identifies genetic loci influencing variance in blood cell traits and shows that incorporating variance polygenic scores improves prediction accuracy.

This study identifies metabolic reaction fluxes that interact with CAD risk alleles, revealing their role as amplifiers or buffers in disease susceptibility.

The paper identifies plasma proteomic markers linked to polygenic risk of type 2 diabetes and related comorbidities using proteogenomic data from biobanks and clinical trials.

This study identifies genetic determinants of blood gene expression and splicing, linking them to molecular phenotypes and health outcomes through QTL analysis and colocalization.

The paper introduces new features and tools for the PGS Catalog to enhance FAIR research, enabling reproducible and equitable polygenic score calculations.

This study integrates polygenic risk scores and gut metagenomic data to improve prediction of common diseases, showing enhanced predictive performance over conventional risk factors.

The paper develops genetic scores for 17,227 molecular traits using multi-omic data from INTERVAL and validates them across ancestries, demonstrating their utility in pathway analysis and disease association discovery.

The paper develops genetically personalised organ-specific metabolic models to identify associations between metabolic fluxes and disease risk, finding 97 fluxes linked to coronary artery disease.

In 5,959 Finns with genotypes and stool metagenomes, GWAS found 583 independent SNP-taxon associations; dairy intake modified LCT effects on Bifidobacterium, and ABO-linked Faecalicatena lactaris tracked secretor status.

The paper uses polygenic scores to identify plasma proteins causally linked to cardiometabolic diseases and finds some are targets for existing therapies.

PGS Catalog standardizes 192 curated polygenic scores and metadata (86 traits); reapplied in UK Biobank, eight of nine colorectal cancer scores predicted disease in European-ancestry participants.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

This study identifies 118 genetic loci associated with 2,319 metabolite features and finds 46 causal effects of metabolites on common diseases using genome-wide association and Mendelian randomization.

This study compares machine learning and univariate methods for constructing polygenic scores for 26 blood cell traits using UK Biobank and INTERVAL data, showing improved performance with learning methods.

This study identifies genetic variants with pleiotropic effects on cytokines and related traits using a multivariate genome-wide association analysis.

The paper identifies alpha-1 antitrypsin as a key contributor to GlycA's predictive power for disease risk and mortality across multiple conditions.

This study evaluates how adding polygenic risk scores and clinical biochemistry markers to conventional CVD risk models improves prediction accuracy and informs statin therapy decisions in a UK primary care setting.

FastSpar rapidly infers correlation networks and calculates p-values for microbiome compositional data, significantly reducing computation time compared to SparCC.

This study investigates eQTL study design and analysis, finding that FDR control inflates false discoveries and effect sizes are overestimated, with a proposed bootstrap method to improve accuracy.

The paper integrates transcriptomic, metabolomic, and genomic data to identify gene networks and their genetic regulation linked to immune function and circulating metabolites.

The paper introduces NetRep, a permutation-based method to assess gene coexpression module preservation across tissues, finding 52% of modules are preserved in at least one tissue and 25% in all four tissues.