Michael Inouye

This paper demonstrates that injectable antisense oligonucleotides (ASO) can prevent and treat obesity in mice without significant loss of lean mass.

The paper improves metabolomic biomarker discovery in admixed American cohorts by training models on diverse data, achieving better predictive performance and more significant associations with cardiometabolic diseases.

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.

The paper analyzes bacterial diversity and strain dynamics in the infant respiratory microbiome using whole genome sequencing and metagenomics, revealing age-dependent shifts in dominant taxa and expanding reference genomes for airway microbes.

The paper combines natural and engineered genetic variation to identify 22 non-coding variants affecting haematological traits and shows how a CUX1 variant impacts megakaryopoiesis.

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

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

The paper shows that environmental variance increases with age, reducing heritability and PRS accuracy for multiple traits and diseases, suggesting younger cohorts are better for risk prediction models.

This study identifies key considerations for accrediting polygenic risk score testing through stakeholder consultations and proposes checklists for quality assessment.

This paper introduces PHBC to estimate pleiotropic heritability across diseases, finding that about half of common disease heritability is shared with other diseases.

This study evaluates deep-learning methods for improving polygenic scores, finding limited benefits due to confounding effects from linkage disequilibrium.

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.

This study shows that muscle-specific PolG dysfunction causes mitochondrial dysfunction and cachexia by activating the mtISR and altering the mitochondrial folate cycle.

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.

This study evaluates deep learning methods for polygenic prediction, showing that MLP benefits most from larger sample sizes and one-hot encoding improves performance across methods.

The GWAS Catalog now holds 625,113 curated SNP-trait associations from 6,921 publications plus >85K summary statistics datasets, with a new GWAS-SSF submission standard and GRCh38 harmonisation.

This study identifies gene modules, hubs, and pathways associated with TBM mortality using whole blood RNA sequencing and develops a 4-gene biomarker for predicting death from TBM.

This study evaluates the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm compared to current strategies.

The paper identifies misexpression of inactive genes in whole blood and links it to nearby rare structural variants using RNA and genome sequencing data.

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.

This study evaluates polygenic scoring methods across five biobanks and finds that biobank differences outweigh method differences, with ensemble learning improving performance.

This paper introduces RGStraP, a tool to infer genetic population structure from RNAseq data and shows it can control for population structure in gene expression analyses.

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.

This study identifies major technical variations in microbiome data and evaluates a batch correction tool to minimize their impact on downstream analyses.

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.

This paper introduces an age-dependent topic modelling method to identify disease subtypes with differential genetic risk using UK Biobank and All of Us data.

This study assesses the transferability of genetic loci and polygenic scores for cardiometabolic traits from European-ancestry samples to British Pakistanis and Bangladeshis, finding high transferability for most loci but variable performance for polygenic scores.