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
📣📣📣 We are hiring fully-funded graduate students and Postdoc! Please contact me at [email protected]
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:
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updated 08/2024
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
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>)