Xiaoquan Wen

SEBULA detects multiplets in snATAC-seq using a semi-parametric empirical Bayes model and integrates with scRNA-seq data, showing superior performance on benchmark datasets.

This paper introduces a framework for multi-resolution clustering of genomic data to uncover nested biological relationships and reconcile clustering discrepancies across datasets.

This study performs a GWAS meta-analysis of psoriasis, identifying 109 susceptibility loci, including 45 new ones, and highlights potential therapeutic targets and disease mechanisms.

The paper uses scATAC-seq to analyze chromatin accessibility and gene expression dynamics in immune cells from asthmatic children, identifying eQTLs and their regulatory roles in asthma risk.

This paper introduces INTERFACE, a probabilistic fine-mapping method that improves the identification of causal genes by integrating colocalization and transcriptome-wide association evidence.

The paper argues that causal gene identification methods should be evaluated using statistical techniques instead of gold-standard gene sets due to their incompleteness and labeling biases.

The paper introduces Multi-INTACT, a method that integrates transcriptome-wide association studies with protein QTL data to identify causal genes for metabolites, finding that both gene products are relevant for most gene nominations.

The paper identifies genetic variants that modify gene expression in response to caffeine in vascular endothelial cells and shows their potential role in cardiovascular disease.

This paper introduces ReAD, a method for replicability analysis in GWAS that accounts for linkage disequilibrium, improving power and FDR control compared to existing methods.

JUMP improves replicability analysis in high-dimensional data by controlling FDR and increasing power, applied to spatial transcriptomic datasets.

The paper uses single-cell RNA-seq to study how genetic variants influence the transcriptional response to glucocorticoids and immune stimuli in children, identifying treatment-specific gene regulation.

The paper identifies high-resolution eQTLs in kidney tissues using Bayesian fine mapping with integrative priors, improving resolution and functional relevance.

The paper identifies gene expression changes in immune cells during pubertal development in children with asthma, showing shifts in immunity and correlations with asthma-related gene expression.

This paper introduces a probabilistic framework to integrate TWAS and colocalization analysis, improving gene implication and pathway identification for complex traits.

This paper investigates the inferential reproducibility between transcriptome-wide association studies and colocalization analysis, identifying two classes of inconsistent results and proposing a novel approach to reconcile them.

This study identifies 303 novel genetic signals associated with plasma metabolites in Finnish men, revealing biological insights into disease mechanisms.

The paper investigates how interspecies variation in hominid gut microbiota affects host gene regulation in colonic epithelial cells, finding species-specific gene responses linked to inflammatory diseases.

The paper identifies 174 asthma-associated genes regulated by psychosocial factors and 349 gene-environment interactions using RNA-sequencing and machine learning in children with asthma.

The paper identifies gene-environment interactions in gene regulation across cell types and treatments, revealing many genes missed by GTEx.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

This study introduces a framework to integrate functional annotations with GWAS data to improve gene-based association analysis power and accuracy.

The paper evaluates colocalization methods for genetic variants, identifying limitations and recommending best practices for accurate analysis of complex traits and molecular QTLs.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

The paper assesses the impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx, finding that local ancestry adjustment improves power but has limited effect on colocalization probabilities.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper improves causal gene identification and transcriptome prediction by integrating fine-mapping and QTL tissue-sharing information into transcriptomic imputation models.

BAGSE provides a Bayesian hierarchical model for gene set enrichment analysis, offering accurate enrichment quantification and improved power in gene discovery.