Biao Zeng

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper presents a long-read RNA-seq atlas of microglia isoforms, identifying novel isoforms and their associations with genetic risk loci in Alzheimer's and Parkinson's disease.

This study identifies 63 AMD risk loci across multiple ancestries, highlighting ancestry-dependent genetic heterogeneity and potential therapeutic targets.

The paper identifies cell-type specific chromatin accessibility QTLs and their functional impact on brain disease risk.

This study identifies 240 novel genetic loci associated with mosaic loss of chromosome Y through a multi-ancestry GWAS of the Million Veteran Program and UK Biobank.

This study identifies 64 new risk loci for major depression, refines its genetic architecture, and shows strong genetic overlap with other psychiatric disorders and educational attainment.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

This study maps chromatin accessibility in Alzheimer's disease using ATAC-seq, identifying regulatory changes and 3D genomic alterations linked to disease.

The paper identifies population-level variation in enhancer expression in the human brain and links enhancer eQTL to schizophrenia heritability.

The paper identifies regulatory mechanisms for 21 Alzheimer's disease risk loci in human microglia using transcriptome and chromatin accessibility profiling.

The paper performs a trans-ethnic eQTL meta-analysis to identify regulatory variants and candidate causal genes for brain-related traits.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

TreeMap identifies causal eQTL variants by accounting for multisite effects and linkage, showing improved accuracy and enabling genome-wide analysis of long-range eQTL.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

This study shows that multi-site regulation of gene expression leads to biased eQTL fine mapping and highlights the need for joint multi-site analysis to improve accuracy.