Quantitative Genetics Lab

💡 Cheng Lab at UC Davis

Our research focuses on the development of statistical, machine learning, and computational methods that bridge the genome and phenome through more accurate, efficient, and biologically interpretable analyses. We leverage genomics, phenomics, and other sources of omics data in various species to better predict desired traits and infer the underlying biological mechanisms. This includes

🧮 Theoretical foundations of quantitative genetics, including statistical models and computational algorithms
💾 Software development for applying these methods to real-world, large-scale omic datasets
🐖 Applied quantitative genetics, employing various methods to analyze extensive datasets covering a wide range of traits across multiple species

🚢 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**

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<aside> 🍋 LMMonBoard

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width="40px" /> Diagnostic Imaging

</aside>

💡 Cheng Lab at UC Davis

🚢 Projects

🧮 Software Tools

🌎 Teaching