Open AccessGenomic Prediction using Existing Historical Data Contributing to Selection in Biparental Populations: A Study of Kernel Oil in Maize
Author(s) -
Hao Yangfan,
Wang Hongwu,
Yang Xiaohong,
Zhang Hongwei,
He Cheng,
Li Dongdong,
Li Huihui,
Wang Guoying,
Wang Jianhua,
Fu Junjie
Publication year - 2019
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.3835/plantgenome2018.05.0025
Subject(s) - best linear unbiased prediction , biology , heritability , genomic selection , selection (genetic algorithm) , population , predictive modelling , cross validation , statistics , genetic gain , microbiology and biotechnology , regression , machine learning , mathematics , genetics , computer science , genetic variation , genotype , single nucleotide polymorphism , demography , sociology , gene
Maize ( Zea mays L.) kernel oil provides high‐quality nutrition for animal feed and human health. A certain number of maize breeding programs seek to enhance oil concentration and composition. Genomic selection (GS), which entails selection based on genomic estimated breeding values (GEBVs), has proven to be efficient in breeding programs. Here, we estimate the robustness of predictions for the oil traits of maize kernels in biparental recombination inbred lines (RILs) using a GS model built based on an association population. Most statistical models, including ridge regression–best linear unbiased prediction (RR‐BLUP), showed high prediction accuracy in the training population through a cross validation procedure. The training population size was more important than marker density and a statistical model for prediction performance. Using the optimized GS model, prediction of the biparental RIL population showed medium‐high prediction accuracy (0.68) compared with prediction using only oil associated markers ( r = 0.43). The potential to apply the GS model to another RIL population that is genetically less related to the training population was also examined, showing promising prediction accuracy in the top selected lines. Our results proved that genomic prediction using existing data is robust for the prediction of polygenic traits with moderate to high heritability.