PMID- 31290920
OWN - NLM
STAT- MEDLINE
DCOM- 20191211
LR  - 20191217
IS  - 1940-3372 (Electronic)
IS  - 1940-3372 (Linking)
VI  - 12
IP  - 2
DP  - 2019 Jun
TI  - Genomic Prediction of Pumpkin Hybrid Performance.
LID - 10.3835/plantgenome2018.10.0082 [doi]
AB  - Genomic prediction has become an increasingly popular tool for hybrid performance 
      evaluation in plant breeding mainly because that it can reduce cost and 
      accelerate a breeding program. In this study, we propose a systematic procedure 
      to predict hybrid performance using a genomic selection (GS) model that takes 
      both additive and dominance marker effects into account. We first demonstrate the 
      advantage of the additive-dominance effects model over the only additive effects 
      model through a simulation study. Based on the additive-dominance model, we 
      predict genomic estimated breeding values (GEBVs) for individual hybrid 
      combinations and their parental lines. The GEBV-based specific combining ability 
      (SCA) for each hybrid and general combining ability (GCA) for its parental lines 
      are then derived to quantify the degree of midparent heterosis (MPH) or 
      better-parent heterosis (BPH) of the hybrid. Finally, we estimate the variance 
      components resulting from additive and dominance gene action effects and 
      heritability using a genomic best linear unbiased predictor (g-BLUP) model. These 
      estimates are used to justify the results of the genomic prediction study. A 
      pumpkin ( spp.) data set is given to illustrate the provided procedure. The data 
      set consists of 320 parental lines with 61,179 collected single nucleotide 
      polymorphism (SNP) markers; 119, 120, and 120 phenotypic values of hybrids on 
      three quantitative traits within maxima Duchesne; and 89, 111, and 90 phenotypic 
      values of hybrids on the same three quantitative traits within Dechesne.
CI  - (c) 2019 The Author(s).
FAU - Wu, Po-Ya
AU  - Wu PY
FAU - Tung, Chih-Wei
AU  - Tung CW
FAU - Lee, Chieh-Ying
AU  - Lee CY
FAU - Liao, Chen-Tuo
AU  - Liao CT
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Plant Genome
JT  - The plant genome
JID - 101273919
SB  - IM
MH  - Bayes Theorem
MH  - Computer Simulation
MH  - Cucurbita/genetics/*physiology
MH  - Datasets as Topic
MH  - Genetic Variation
MH  - *Hybridization, Genetic
MH  - Inheritance Patterns
MH  - Models, Genetic
MH  - Models, Statistical
MH  - *Plant Breeding
MH  - Selection, Genetic
EDAT- 2019/07/11 06:00
MHDA- 2019/12/18 06:00
CRDT- 2019/07/11 06:00
PHST- 2019/07/11 06:00 [entrez]
PHST- 2019/07/11 06:00 [pubmed]
PHST- 2019/12/18 06:00 [medline]
AID - 10.3835/plantgenome2018.10.0082 [doi]
PST - ppublish
SO  - Plant Genome. 2019 Jun;12(2). doi: 10.3835/plantgenome2018.10.0082.