Zhe Zhang

This paper introduces Pinal, a foundation model that designs proteins from natural language descriptions, achieving functional alignment and experimental success in four design tasks.

The paper presents CattleGTEx, a resource characterizing regulatory effects in cattle, resolving GWAS signals and providing insights into adaptive evolution and selection.

OmiGA improves molQTL mapping in complex populations by using an efficient linear mixed model, enhancing power and reducing false positives.

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 paper presents an analytical solution for estimating SNP-heritability in biobank-scale and distributed datasets, achieving high precision and efficiency.

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 presents a near-complete pig genome assembly, improving upon the existing reference by closing gaps and enhancing gene prediction, which supports better genetic research and applications.

The paper introduces two LD estimation algorithms to create high-resolution LD grids across species, revealing evolutionary patterns and population structure.

This study identifies a potent PVC-dechlorinating activity in gut microbiota of insect larvae using comparative metagenomics.

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 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.

PHARP provides a high-quality pig haplotype reference panel for genotype imputation, enabling improved GWAS and genomic prediction.