Zhonghao Bai

This study constructs a multi-tissue cell atlas of pigs, identifies cell-type-specific gene regulation, and links these to complex traits and human phenotypes.

This study uses genetic correlations to prioritize drug groups for repurposing in type 2 diabetes, identifying several drug classes with significant genetic relevance.

The paper presents a multi-tissue sheep atlas identifying regulatory variants and their effects on molecular phenotypes, revealing allelic heterogeneity and evolutionary patterns.

The ChickenGTEx project provides a multi-tissue reference of regulatory variants in chicken, analyzing their functional impacts and utility in genetics, genomics, and evolutionary studies.

The paper performs a multi-breed meta-GWAS in pigs to identify genomic regions associated with complex traits and integrates with PigGTEx to prioritize variant-gene-tissue-trait circuits.

This paper evaluates Bayesian Linear Regression derived gene set tests, showing they outperform MAGMA in detecting genetic associations with complex diseases.

This study evaluates Bayesian Linear Regression models for gene set prioritization in complex diseases, showing improved performance in identifying T2D-related pathways using multi-trait analysis.

The paper presents PigGTEx, a resource linking genetic variants to transcriptomic phenotypes in pigs, showing pigs' similarity to humans in gene regulation and complex traits.

This paper evaluates Bayesian Linear Regression models with BayesC and BayesR priors as fine mapping tools, comparing them to existing methods and showing improved F1 scores and credible set properties.

This paper develops DeepGCF, a deep learning method to predict functional conservation between human and pig using epigenome and transcriptome data, showing improved performance and insights into regulatory and heritability patterns.

The paper introduces a web server for multi-species transcriptome-wide association study (TWAS) analysis, enabling gene expression prediction and trait association in humans and farm animals.