Chao Chen

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

The paper assembles haploid genomes of Muscovy ducks and analyzes genetic, expression, and regulatory patterns of parental alleles in the ZW system, finding differences in chromatin accessibility and gene expression between maternal and paternal haplotypes.

The paper identifies eQTLs, rQTLs, and pQTLs in the human brain and shows that many eQTL effects are attenuated post-transcriptionally, yet still correlate with schizophrenia GWAS signals.

This study evaluates precision and accuracy in sc/snRNA-seq data, identifies factors affecting reproducibility, and introduces a tool to assess data quality and differential expression reliability.

The paper analyzes psychosis biotypes using ancestry-adjusted polygenic risk scores and identifies genomic associations with specific biotypes in brain transcriptomes.

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.

The paper evaluates deconvolution methods on human brain data, finding dtangle and bMIND perform best, and identifies decon-eQTLs that explain more schizophrenia heritability than bulk or single-cell eQTLs.

This study maps the cell-type-specific afferent and efferent projections of the mouse claustrum using viral tracing and single-neuron reconstruction, revealing its widespread connections and regional specificity.

The paper investigates the impact of the NLGN3 R451C mutation on human neurons, finding it reduces protein levels but enhances excitatory synapses, potentially contributing to ASD pathogenesis.

The paper identifies MAPK3 as a driver gene in 16p11.2 deletion-associated neurodevelopmental disorders, showing haplotype-specific expression and cis-regulatory enhancer SNPs contribute to variable phenotypes.

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.

The paper compiles and analyzes transcriptomic data from six neuropsychiatric disorders, identifying differentially expressed genes and co-expression networks across brain regions.

This study identifies fever and infection as major agonal factors affecting gene expression in postmortem brains and recommends correcting for these factors using PEER or SVA.

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.

The paper uses cell-type deconvolution to analyze gene expression in schizophrenia and finds upper-layer neurons in the DLPFC are associated with SCZ risk.

Complete morphologies of 1,708 neurons, sparsely labelled and imaged whole-brain with fMOST, expose region-specific long-range projection rules and finer diversity than current transcriptomic subtypes capture.

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 evaluates brain cell marker genes using differential and co-expression data, identifying 22 gold standard and 16 novel candidates with improved specificity.

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