🧬 Statistical and Quantitative Genetics Lab

The Statistical and Quantitative Genetics Lab at the University of Nebraska–Lincoln is focused on addressing challenges in genetic and genomic analysis in the era of big data and multi-omics data. Specifically, we 1) develop new statistical models to incorporate new data (e.g., multi-omics) into genomic data analysis, 2) participate in developing software tools for whole-genome data analysis, 3) develop new algorithms to speed up computations, and 4) conduct statistical analyses of quantitative genetics.

JWAS software ｜ ResearchGate ｜ GoogleScholar

Internal files

📣📣📣 We are hiring fully-funded graduate students and Postdoc! Please contact me at [email protected]

😄 About me

I'm an assistant professor ****in theoretical quantitative genetics at the University of Nebraska-Lincoln (webpage). I am interested in utilizing statistics as a tool to address challenges in genetics. Specifically, I:

• develop new models to address challenges in whole-genome analysis, such as genomic prediction and genome-wide association studies:
  • new data: incorporate multi-omics data (e.g., transcriptomic), functional genomics data, and functional annotations into genome-to-phenome analysis.
  • big data: propose fast parallelized algorithms ****to speed up Bayesian linear mixed models.
  • data privacy: apply homomorphic encryption to genotypes and phenotypes for collaborative genomic analyses.
• I also actively contribute to maintaining and developing new models for the open-source software tool "JWAS" (JWAS)

🧾  CV

📔 Publications

👣  Blogs

🌐  Resources

☎️  Contact

🧾  CV

updated 02/07/2024

updated 02/07/2024

📔 Highlighted Publications

Peer-reviewed papers:

• Tianjing Zhao, Fangyi Wang, Richard Mott, Jack Dekkers, and Hao Cheng. “Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality”. GENETICS, 2023.

  Keywords: homomorphic encryption, mixed model, genomic prediction, GWAS, joint analysis

  https://doi.org/10.1093/genetics/iyad210

• Jinghui Li, Tianjing Zhao, …, Lingzhao Fang, and Hao Cheng. “Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits”. Cell Genomics, 2023.

  Keywords: deep learning, functional conservation, human, pig, complex trait, gene expression

   [<https://doi.org/10.1016/j.xgen.2023.100390>](<https://doi.org/10.1016/j.xgen.2023.100390>)
  

• Tianjing Zhao, and Hao Cheng, “Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes”, accepted by Genetics Selection Evolution, 2023

  Keywords: neural network, mixed model, single-step, genomics prediction

  https://doi.org/10.1101/2022.07.18.500526