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
%%{init: { 'logLevel': 'debug','theme': 'dark', 'themeVariables': { 'fontSize': '15px', 'fontFamily': 'Inter'} } }%%
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 USDA NIFA AG2PI project "Platforms and methods for sharing and collaboration on AG2P using public and confidential data" has started
Oct 2023: Our single-step NNMM paper was published in GSE.
: Hao gave a workshop "Mixed Models for Agricultural Science" at ASA
Nov 2023: Our paper "Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality" was accpeted in Genetics.
: Congratulations to Donna for receiving the Henry A. Jastro Graduate Research Award.
Dec 2023: Daniel joins the lab!
: Happy Holidays!
Jan 2024: Jiayi is back!
: Hao gave a talk at PAG.
We are working on interesting projects! Click the dropdown menu to find more.
$$ Software\ Packages $$
<aside> 🧬 **JWAS**
</aside>
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
</aside>
#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
</aside>
#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
</aside>
<aside> <img 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width="40px" /> Diagnostic Imaging
</aside>
