Yan Xia

This study identifies population-specific eQTLs in brain tissues from European, African American, and Asian ancestry individuals, revealing new schizophrenia risk genes and variants missed in European-only datasets.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper identifies correlated DNA methylation and gene expression pairs across tissues and brain development stages, finding most are tissue-specific and revealing novel regulatory mechanisms.

This paper identifies potential nootropic drug targets by integrating large-scale cognitive GWAS and transcriptomic data to find credible druggable genes.

This study identifies eQTL and sQTL in East Asian brains, finds convergence in transcriptional regulatory architecture with European populations, and links these to schizophrenia risk genes and pathways.

DRAMS detects and corrects sample mix-ups in multi-omics data by aligning genetic and omics data, improving statistical power and result quality.

csuWGCNA combines signed and unsigned WGCNA to better detect negative correlations, improving module detection and biological interpretation in gene regulation.

The paper identifies positional effects in Illumina Methylation Arrays and shows how they impact methylation measurements, recommending ComBat for correction.