Quantitative Genetics Lab

💡 Cheng Lab at UC Davis

Our research interests are broadly involved in the development of statistical and computational methods for the (genetic) improvement of populations through more accurate, efficient, and biologically meaningful analysis. We have focused on the use of genomics, phenomics, pedigree, and other sources of big data in various species to better predict desired traits. These endeavours include

🧮 theoretical aspects of quantitative genetics including statistical models and computational algorithms (e.g., genomic prediction; genome-wide association studies)
💾 the development of software tools to apply these methods to real-life large genomic and phenomic data
🐖 the application of the software to data sets in routine analysis for a wide range of traits in various species.

📰 Lab News

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timeline
    title Lab News (see Projects for details)
    Jul 2023: Hao has been promoted to Associate Professor
         : Hao gave a talk titled "Exploring Beyond Mixed Models - The Advent of New Models for New Data" at ASAS
				 : Donna attended the Summer Institute in Statistical Genetics (SISG) 
         : Our USDA NIFA AFRI project "Genomic Evaluation using Functional Genomics Data at Individual- and SNP-levels" has started
				 %%: Weijing from Tsinghua University started her summer internship in our lab through the GREAT program 
    Aug 2023: Tianjing embarks on a new journey as an Assistant Professor at UNL!
            : Our DeepGCF paper was published in Cell Genomics.
            : The KBD project, with Hao as co-PI, has started.
						: Mark joins the lab!
    Sep 2023: Olivia joins the lab!
            : Roddur joins the lab!
            : Our project "Platforms and methods for sharing and collaboration on AG2P using public and confidential data" has started 
    Oct 2023: TBA
    Nov 2023: TBA
    Dec 2023: TBA

🚢 Projects

We are working on interesting projects! Click the dropdown menu to find more.

Details

🧮 Software Tools

$$ Software\ Packages $$

<aside> 🧬 **JWAS**

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genotype  = get_genotypes("genotypes.csv")
pedigree  = get_pedigree("pedigree.csv")
model     = build_model("y = intercept + ID + genotype")
set_random(model,"ID",pedigree)
out       = runMCMC(model,phenotypes)

<aside> 🖥️ XSim

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#simulate sequence data and pedigree structures
build_genome(numChr,chrLength,nLoci,nQTL)
sires = sampleFounders(nfounders)
dams  = sampleFounders(nfounders)
sires1,dams1,gen1 = sampleRan(popSize, ngen, sires, dams)

<aside> 🔑 LPChoose

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#Optimizing Sequencing Resources in Genotyped Populations
LPChoose("data.csv",ninds)

$$ Interactive\ Web\ Apps $$

<aside> <img src="https://img.icons8.com/ios/250/000000/linkedin.png" alt="https://img.icons8.com/ios/250/000000/linkedin.png" width="40px" /> ShinyJWAS

</aside>

<aside> 🍋 LMMonBoard

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width="40px" /> Diagnostic Imaging

</aside>