Siming Zhao

The paper shows that transcriptional perturbations have structured effects beyond a global trend, with specific patterns transferable across datasets.

SCADS integrates GWAS data with scATAC-seq to identify disease-relevant cell states and regulatory programs, showing heterogeneity in immune cell types for inflammatory bowel disease.

DiffDriver identifies context-specific selection of cancer driver genes by accounting for mutation rate variations and functional sequence information, revealing differential selection in clinical traits and immune subtypes.

This study develops M-cTWAS to integrate multi-omics and multi-context QTL data with GWAS, improving the discovery of risk genes for complex traits.

MIRAGE improves rare variant association analysis by incorporating variant functional information, outperforming existing methods in simulations and autism exome-sequencing data.

The paper identifies eQTLs associated with COVID-19 disease severity and progression using multi-omics data from Korean patients, revealing genetic mechanisms involving HLA genes and cytokine regulation.

This paper introduces a method to adjust for genetic confounders in transcriptome-wide association studies, improving reliability in detecting causal genes.

The paper identifies allele-specific open chromatin (ASoC) variants in human iPSC-derived neurons and shows their strong enrichment in brain disorder risk variants.

driverMAPS improves cancer driver gene detection by modeling positive selection and mutation patterns, identifying 159 new potential driver genes including RNA methyltransferases.

This paper introduces a statistical framework to identify risk genes from de novo mutations in whole-genome sequencing studies, finding new autism risk genes.

The paper examines H3K9me3 marking of transposable elements in human and chimpanzee induced pluripotent stem cells, finding limited species-specificity and no strong link to gene expression divergence.