10.5061/DRYAD.KG26J
Wang, X.
Yangzhou University
Li, L.
Hunan Agricultural University
Yang, Z.
Yangzhou University
Zheng, X.
Wuhan University
Yu, S.
Huazhong Agricultural University
Xu, C.
Yangzhou University
Hu, Z.
Wuhan University
Data from: Predicting rice hybrid performance using univariate and
multivariate GBLUP models based on North Carolina mating design II
Dryad
dataset
2016
univariate
Genomic selection
Genetics
rice hybrid
multivariate
2016-08-09T16:10:16Z
2016-08-09T16:10:16Z
en
https://doi.org/10.1038/hdy.2016.87
74243715 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Genomic selection (GS) is more efficient than traditional phenotype-based
methods in hybrid breeding. The present study investigated the predictive
ability of genomic best linear unbiased prediction models for rice hybrids
based on the North Carolina mating design II, in which a total of 115
inbred rice lines were crossed with 5 male sterile lines. Using 8 traits
of the 575 (115 × 5) hybrids from two environments, both univariate (UV)
and multivariate (MV) prediction analyses, including additive and
dominance effects, were performed. Using UV models, the prediction results
of cross-validation indicated that including dominance effects could
improve the predictive ability for some traits in rice hybrids.
Additionally, we could take advantage of GS even for a low-heritability
trait, such as grain yield per plant (GY), because a modest increase in
the number of top selection could generate a higher, more stable mean
phenotypic value for rice hybrids. Thus this strategy was used to select
superior potential crosses between the 115 inbred lines and those between
the 5 male sterile lines and other genotyped varieties. In our MV
research, an MV model (MV-ADV) was developed utilizing a MV relationship
matrix constructed with auxiliary variates. Based on joint analysis with
multi-trait (MT) or with multi-environment, the prediction results
confirmed the superiority of MV-ADV over an UV model, particularly in the
MT scenario for a low-heritability target trait (such as GY), with highly
correlated auxiliary traits. For a high-heritability trait (such as
thousand-grain weight), MT prediction is unnecessary, and UV prediction is
sufficient.
Phenotype dataEight traits of all the hybrids and their inbred parentsSNP
dataSNP data of 120 rice inbred lines