Wei Jiang

BOSE estimates sample mean and standard deviation from median, range, and interquartile range using Bayesian order statistics, showing robustness and improved performance in small samples and non-normal distributions.

This paper introduces GENJI, a method to estimate genetic correlation using both individual-level and summary-level GWAS data, improving accuracy and efficiency over existing methods.

This study examines the relationship between renal impairment and cardiac structure/function using CMR and genetic methods, finding associations between eGFR reduction and cardiac parameters.

JointPRS improves genetic risk prediction for non-European populations by integrating genetic correlation and multi-population data without individual-level tuning data.

The paper introduces SuSiE2, a method that integrates eQTL information with fine mapping to improve causal variant identification in GWAS by leveraging expression data.

The paper introduces MAJAR, a statistical framework to assess biomarker replicability in meta-analysis, showing improved power and robustness compared to existing methods.

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

The paper uses a Markov Random Field model with protein-protein interaction data to identify 46 candidate genes for congenital heart disease, including known and highly expressed genes.

M-DATA improves statistical power for identifying risk genes by jointly analyzing de novo mutations across multiple traits, demonstrating its effectiveness in congenital heart disease and autism.

This paper introduces HapFM, a haplotype-based fine-mapping framework for plant GWAS, which improves mapping power and resolution compared to existing methods.

This paper identifies a bias in the scGRM method for kinship estimation and proposes an unbiased method, UKin, which reduces bias and improves association mapping power.

This study benchmarks methods for estimating genetic correlation between complex traits using GWAS summary statistics, highlighting challenges like LD dependency and sample overlap.

Xolik identifies cross-linked peptides efficiently by reducing computational complexity and outperforms existing tools in speed and statistical power.