Haipeng Yu

Ph.D.

Virginia Polytechnic Institute and State University

About me

I will join Dr. Jack Dekkers’ lab this August as a postdoctoral research associate in the Department of Animal Science at Iowa State University. My research topic will be focusing on the integration of biological and whole-genome prediction models. My Ph.D. research in Dr. Gota Morota’s lab at Virginia Tech centered on designing and modeling high-throughput phenotyping data in quantitative genetics and image analysis in computer vision. My overarching research interest is to understand the genotype-phenotype map in animals and plants using computational and statistical genetics. Particularly, I am interested in developing and applying statistical methods to the whole-genome prediction of complex traits, genome-wide association analysis, high-throughput phenotyping data analysis, and image analysis.

Interests

Quantitative Genetics
Statistical Genetics
Image Analysis

Education

Ph.D., Quantitative Genetics, 2020

Virginia Tech
M.S., Animal Breeding and Genetics, 2016

North Dakota State University
B.S., Veterinary Medicine, 2013

Qingdao Agricultural University

Preprints

Malachy T. Campbell, Haipeng Yu, Mehdi Momen, and Gota Morota. Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models. bioRxiv. [bioRxiv]
Haipeng Yu and Gota Morota. GCA: An R package for genetic connectedness analysis using pedigree and genomic data. bioRxiv. [bioRxiv]

Publications

Haipeng Yu, Gota Morota, Elfren F. Celestino Jr, Carl R. Dahlen, Sarah A. Wagner, David G. Riley, and Lauren L. Hulsman Hanna. Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics (in press), 2020.

DOI bioRxiv

Lauren L. Hulsman Hanna, Jordan K. Hieber, Haipeng Yu, Elfren F. Celestino Jr, Carl R. Dahlen, Sarah A. Wagner, and David G. Riley. Blood collection has negligible impact on scoring temperament in Angus-based weaned calves. Livestock Science, 2019.

DOI

Haipeng Yu, Malachy Campbell, Qi Zhang, Harkamal Walia, and Gota Morota. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics, 2019.

DOI PubMed EuropePMC bioRxiv

Haipeng Yu, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. Do stronger measures of genomic connectedness enhance prediction accuracies across management units?. Journal of Animal Science, 2018.

DOI PubMed EuropePMC

Haipeng Yu, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. Genomic relatedness strengthens genetic connectedness across management units. G3: Genes, Genomes, Genetics, 2017.