Buu Minh Thanh Truong

The paper identifies genetic associations with liver radiomic features and their links to metabolic diseases using UK Biobank data.

This paper constructs phenome-derived polygenic scores and evaluates how they, along with social determinants of health, influence disease risk across different populations and diseases.

This study uses ancient DNA and genomic data to show natural selection has strengthened the immune system over 10,000 years, with both protective and harmful effects on immune-related diseases.

This study examines associations between leukocyte telomere length and genomic, phenomic, and geographic factors in diverse U.S. populations, identifying novel loci and genes.

The paper identifies pleiotropic loci across 1,167 traits using GWAS meta-analysis and shows they are enriched for functional annotations and improve polygenic score predictions.

The paper evaluates the instability of polygenic risk scores (PRS) across different publications and introduces an integrative scoring framework to improve classification consistency.

Cross-trait Elastic Net mixing of PGS Catalog scores (PRSmix+) lifted mean PRS accuracy 1.72-fold in Europeans and 1.42-fold in South Asians over the best single score.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

The paper integrates GWAS with scATAC-seq and scRNA-seq data to identify disease-critical fetal and adult brain cell types for multiple traits.

This paper introduces a method to estimate cross-ancestry genetic correlation using individual-level data, finding significant genetic heterogeneity in obesity between African and European ancestry groups.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

This paper introduces a new hyper-parameter, the scale factor, to improve the prediction accuracy of the HBLUP method in genetic evaluation using simulated and real cattle data.

This paper introduces PAN, a method to create sample-specific gene networks using annotations, improving breast cancer relapse prediction compared to existing methods.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper presents methods and tools for predicting cell locations in Drosophila embryos using single-cell transcriptomic data, achieving top results in two sub-challenges.

The paper introduces miRsyn, a causal inference framework to identify miRNA synergism using observational data, finding that many miRNA pairs with shared targets do not synergize at the expression level.

This paper identifies miRNA-mRNA regulatory relationships in breast cancer using invariant causal prediction and shows improved performance over existing methods.