Sheng Qian

GEMS associates GWAS variants with cell types using sequence-to-function predictions, revealing cell-type-specific mechanisms for disease risk variants.

The paper identifies context-specific genetic effects in neuronal activation using single-cell multiomics, revealing NPD risk variants and genes active during stimulation.

This paper introduces FGMB, a multi-context regulome-wide association atlas, and applies it to Alzheimer's disease to identify 327 regulatory associations and resolve 86 gene-molecular-trait pairs.

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.

The paper investigates DNA breathing's role in TF-DNA interactions using in vitro and in vivo data, finding correlations between breathing features near motifs and binding affinity across multiple TFs.

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

The paper introduces MetaChrom, a deep transfer learning framework for predicting epigenomic functions of genetic variants in neurodevelopment, showing improved accuracy over existing methods.

The paper evaluates common misuses of PCA in SNP data analysis and recommends best practices for improving population structure elucidation.

This paper predicts adverse drug-drug interactions using genetic interaction features and a gradient boosting classifier, identifying 432 novel interactions and providing mechanistic insights.