Martin Jinye Zhang

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

This paper introduces scDRS-FM, a method to distinguish causal from tagging disease associations in scRNA-seq data, showing it improves power and accuracy in identifying disease-relevant cell populations.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data by allowing causal effect sizes to vary across ancestries, achieving higher power and lower computational costs.

DALE-Eval benchmarks cell type-specific expression deconvolution methods with new metrics and performance insights.

This study uses a deep learning framework to analyze whole-genome data from ME/CFS patients, identifying risk genes and linking them to disease symptoms and other complex traits.

This paper introduces TGFM, a method for fine-mapping causal tissues and genes at disease-associated loci using GWAS and eQTL data, identifying 147 causal elements per disease with 11% as gene-tissue pairs.

KGWAS improves GWAS power in small cohorts by integrating a functional knowledge graph, discovering more associations for rare diseases.

This study uses spatial transcriptomics to identify medullary niche features in thymoma and thymic hyperplasia associated with myasthenia gravis pathogenesis.

The paper introduces LDSPEC to estimate correlations between causal disease effects of proximal SNPs, finding significant correlations that vary with distance, allele frequency, LD, and functional annotations, implicating stabilizing selection.

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.

The paper investigates epigenomic and 3D chromatin conformation changes in human frontal cortex and hippocampus development using sn-m3C-seq, finding distinct chromatin conformations in neurons and glial cells and linking risk variants to regulatory regions.

This paper introduces scDRS, a method to identify disease-associated cells in scRNA-seq data using polygenic risk scores, finding novel subpopulations linked to diseases like IBD and schizophrenia.

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

The paper introduces MACIE, a framework for predicting functional variants by integrating diverse annotations, and applies it to fine-mapping using GWAS data.

The paper analyzes the Tabula Muris Senis dataset to identify aging-related gene expression changes across 76 tissue-cell types in mice, revealing both shared and cell-type specific aging signatures.

AdaFDR improves multiple hypothesis testing by using covariates to increase discovery power while controlling false discovery rate, outperforming BH and IHW in eQTL analysis.

The paper determines the optimal sequencing depth for single-cell RNA-Seq as one read per cell per gene and identifies an empirical Bayes estimator as optimal for gene property estimation.