Xiang Li

MACHINE improves multi-ancestry fine-mapping by using a continuous prior and FDR control, identifying new causal variants in lipid traits and schizophrenia.

This paper introduces a Bayesian fine-mapping model, h2-D2, which outperforms existing methods in identifying causal variants and applies it to prostate cancer, discovering new variants and associated genes and pathways.

The paper generates transcriptome and 3D genome atlases of developing mouse brain cells, revealing structure-type-based cell identity and experience-independent developmental changes.

The paper introduces GeACT, a genomic database for human mid-gestation fetus cells, linking gene expression to chromatin states and identifying key transcription factors.

The paper identifies correlated gene modules using single-cell RNA-seq, revealing biological functions and regulatory relationships through covariance analysis.