Michael H. Cho

Newly identified COPD GWAS protein interactors reveal a potential disease network module

The paper identifies protein interactors of COPD GWAS genes using AP-MS and network analysis, revealing a disease network module involving 8 proteins.

TorchGWAS2: Cost-Effective Phenome- and Genome-Wide Association Testing in Related Samples

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

Genetic Determinants of Pulmonary Artery Size in over 50,000 Subjects with and without COPD

This study identifies 44 genetic loci associated with pulmonary artery diameter across COPD and population-based cohorts, highlighting shared causal variation between pulmonary and systemic vascular biology.

Multi-ancestry modeling improves fine-mapping resolution, protein prediction, and discovery for proteome-wide association studies

This study improves proteome-wide association study results by using multi-ancestry data, finding more protein-phenotype associations and better prediction models.

HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility

The paper investigates HHIP's role in epithelial wound healing and links its dysfunction to COPD susceptibility through computational modeling and experimental validation.

AT2-intrinsic Z-AAT expression drives conserved inflammatory and proteotoxic stress responses and predisposes to emphysema

This study shows that Z-AAT expression in AT2 cells causes stress responses and increases emphysema risk in AATD.

Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

Genome-Wide Association Study of Risk for Eosinophilic Granulomatosis with Polyangiitis

This study identifies two genome-wide significant genetic associations with EGPA, one in SSH2 and one in RUNX1, and shows colocalization with molecular traits related to IL5RA and LYN.

Overlap between COPD genetic association results and transcriptional quantitative trait loci

This study identifies 38 COPD GWAS loci with evidence of sQTLs, showing that sQTL analysis in whole blood and lung tissue provides novel insights into COPD mechanisms.

Ancestry Calibration of Polygenic Risk Scores Improves Risk Stratification and Effect Estimation in African American Adults

This study calibrates polygenic risk scores (PRS) for African American adults based on genetic ancestry, improving risk stratification and effect estimation for type 2 diabetes and height.

miRNA-mRNA network analysis identifies PAX5 as a potential regulator of adaptive immune response in COPD

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease

The paper identifies proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations

The paper develops a method to quantify somatic mutation burden in whole-genome sequencing data without paired-tissue samples and identifies genetic and phenotypic correlates of clonal hematopoiesis without known driver mutations.

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases

This paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

This study evaluates trait selection strategies in multi-trait GWAS, finding that diverse trait sets improve variant detection compared to univariate screening and specific methods like JASS outperform others.

Determinants of mosaic chromosomal alteration fitness

The paper estimates mCA fitness in the NHLBI TOPMed cohort and identifies genetic variants associated with clonal expansion rates.

A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

This paper introduces a statistical framework, cWAS, to identify cell types with genetically regulated proportions associated with complex diseases using integrated genetic and transcriptomics data.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

Genetic Regulators of Sputum Mucin Concentration and Their Associations with COPD Phenotypes

This study identifies genetic variants associated with sputum mucin concentrations and links one variant to COPD phenotypes like chronic bronchitis and acute exacerbations.

Clonal hematopoiesis is driven by aberrant activation of TCL1A

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

Somatic mutations in chronic lung disease are associated with reduced lung function

The paper finds that somatic mutations in diseased lung tissue correlate with reduced lung function and cell type proportions in COPD and IPF.

Structural variation across 138,134 samples in the TOPMed consortium

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

RITSS identifies gene-environment interactions in quantitative traits using sample splitting and robust statistics, finding significant interactions in lung function and height data.

A Statistical Physics Approach for Disease Module Detection

This paper introduces a statistical physics approach using the random-field Ising model to detect disease modules, showing improved efficiency and robustness compared to existing methods.

Integrative Analyses Reveal Novel Disease-associated Loci and Genes for Idiopathic Pulmonary Fibrosis

The paper identifies novel disease-associated genes for idiopathic pulmonary fibrosis using integrative genetic and transcriptomic analyses.

Fast computation of principal components of genomic similarity matrices

This paper introduces fast algorithms for computing principal components of genomic similarity matrices, including exact methods for specific matrices and an approximate Jaccard matrix with theoretical guarantees.

Leveraging deep-learning on raw spirograms to improve genetic understanding and risk scoring of COPD despite noisy labels

This paper trains a deep neural network on noisy COPD labels to predict COPD status from spirograms, improving genetic discovery by identifying new loci and replicating existing ones.