Zhi Yu

This study evaluates polygenic risk scores for cardiovascular diseases in predicting carotid stenosis, finding that PAD and CAD PRSs show significant associations and improved discrimination over traditional risk factors.

The paper identifies 800 exome-wide significant rare coding variants associated with blood lipids in over a million diverse individuals, including 130 driven by non-European-like populations.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

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

This paper develops a machine learning framework to impute cross-platform proteomic data, improving replication of epidemiological findings and protein quality scoring.

The paper evaluates individual-level performance of polygenic risk scores (PRSs) using a benchmark cohort of early-onset coronary artery disease patients, showing variability in PRS accuracy and highlighting the need for individual-level validation.

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.

The paper identifies plasma proteins associated with clonal hematopoiesis and links them to cardiovascular disease through proteomic and genetic analyses.

The paper benchmarks somatic variant and indel detection at low allele fractions using short- and long-read data, finding sensitivity and precision around 80% at 300x coverage.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

This paper introduces MRLE, a method for Mendelian Randomization analysis of latent exposures using multiple biomarkers, showing improved power and evidence for inflammation's causal role in diseases.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

The paper identifies IL1RAP and AIM2 as key modifiers of CHIP-associated cardiovascular risk using UK Biobank data and CRISPR experiments in mice.

The paper assesses if a polygenic score for reduced kidney function predicts adverse outcomes in chronic kidney disease patients, finding significant associations but no improvement in prediction over existing risk equations.

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

This study identifies shared immune pathways between coronary artery disease and pneumonia through genome-wide pleiotropy analysis, highlighting ADAMTS7 and IL6R as key genes with opposing effects on the two conditions.

This study develops a polygenic risk score for eGFR and shows its association with kidney disease outcomes and plasma proteins in the ARIC study.

This study uses Mendelian randomisation to show higher kidney function causally lowers blood pressure, but blood pressure does not significantly affect kidney function.