Haipeng Yu

Postdoctoral Research Associate

Iowa State University

About me

I am a postdoctoral research associate in Dr. Jack Dekkers’ lab in the Department of Animal Science at Iowa State University. My research at Iowa focuses 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, Kiho Lee, and Gota Morota. Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science (In Press), 2021.

Sara Pegolo, Haipeng Yu, Gota Morota, Vittoria Bisutti, Guilherme J. M. Rosa, Giovanni Bittante, and Alessio Cecchinato. Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science (In Press), 2021.

Mehdi Momen, Madhav Bhatta, Waseem Hussain, Haipeng Yu, and Gota Morota. Modeling multiple phenotypes in wheatusing data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct (In Press), 2021.

bioRxiv

Sabrina T. Amorim, Haipeng Yu, Mehdi Momen, Lúcia Galvão de Albuquerque, Angélica S. Cravo Pereira, Fernando Baldi, and Gota Morota. An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science, 2020.

DOI PubMed EuropePMC

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, 2020.

DOI bioRxiv PubMed EuropePMC

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.