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Genomic selection of agronomic traits in hybrid rice using an NCII population

Published on Dec 1, 2018in Rice3.513
· DOI :10.1186/s12284-018-0223-4
Yang Xu4
Estimated H-index: 4
(YZU: Yangzhou University),
Xin Wang4
Estimated H-index: 4
(YZU: Yangzhou University)
+ 4 AuthorsZhongli Hu6
Estimated H-index: 6
(WHU: Wuhan University)
Sources
Abstract
Background Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance.
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References35
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#1Ulrike Beukert (Leibniz Association)H-Index: 1
#2Zuo Li (Leibniz Association)H-Index: 4
Last. Jochen C. Reif (Leibniz Association)H-Index: 45
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#1José Crossa (CIMMYT: International Maize and Wheat Improvement Center)H-Index: 64
Last. Rajeev K. Varshney (ICRISAT: International Crops Research Institute for the Semi-Arid Tropics)H-Index: 78
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Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype × environment (G × E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize bre...
130 CitationsSource
#1John M. Hickey (Edin.: University of Edinburgh)H-Index: 28
#2Tinashe Chiurugwi (National Institute of Agricultural Botany)H-Index: 1
Last. Yoseph BeyeneH-Index: 21
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Wayne Powell and colleagues compare the different tools and approaches used by the plant breeding community versus the animal breeding community for crop and livestock improvement. They argue that the two disciplines can be united via adoption of genomic selection along with the exchange of resources and techniques between the two areas.
61 CitationsSource
#1Xinbing WangH-Index: 36
#2L LiH-Index: 1
Last. Zhiqiu HuH-Index: 13
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Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II
27 CitationsSource
#1Shizhong Xu (UCR: University of California, Riverside)H-Index: 43
Genomic prediction is a statistical method to predict phenotypes of polygenic traits using high-throughput genomic data. Most diseases and behaviors in humans and animals are polygenic traits. The majority of agronomic traits in crops are also polygenic. Accurate prediction of these traits can help medical professionals diagnose acute diseases and breeders to increase food products, and therefore significantly contribute to human health and global food security. The best linear unbiased predicti...
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#1Xin Wang (YZU: Yangzhou University)H-Index: 4
#2Zefeng Yang (YZU: Yangzhou University)H-Index: 15
Last. Chenwu Xu (YZU: Yangzhou University)H-Index: 16
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Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection (GS), and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction (RR-BLUP), genomic best linear unbiased predi...
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#1Anita Ehret (CAU: University of Kiel)H-Index: 3
#2David Hochstuhl (CAU: University of Kiel)H-Index: 9
Last. Georg Thaller (CAU: University of Kiel)H-Index: 24
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Background Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can capture cryptic relationships between SNPs (single nucleotide polymorphisms) and phenotypic values without the need of explicitly defining a genetic model. This concept is attractive for high-dimensional and noisy data, especially when the genetic architect...
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#1Xuecai ZhangH-Index: 14
Last. José CrossaH-Index: 64
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One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms ...
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#1Jennifer Spindel (Cornell University)H-Index: 9
#2Hasina Begum (IRRI: International Rice Research Institute)H-Index: 3
Last. Susan R. McCouch (Cornell University)H-Index: 91
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Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the Int...
204 CitationsSource
#1Jia-Yang LiH-Index: 1
#2Jun WangH-Index: 1
Last. Robert S. Zeigler (IRRI: International Rice Research Institute)H-Index: 5
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Rice is the world’s most important staple grown by millions of small-holder farmers. Sustaining rice production relies on the intelligent use of rice diversity. The 3,000 Rice Genomes Project is a giga-dataset of publically available genome sequences (averaging 14× depth of coverage) derived from 3,000 accessions of rice with global representation of genetic and functional diversity. The seed of these accessions is available from the International Rice Genebank Collection. Together, they are an ...
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#1Theresa Ankamah-Yeboah (UCPH: University of Copenhagen)
#2L.L.G. Janss (AU: Aarhus University)H-Index: 3
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With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programmes are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the...
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#1A. I. Seye (Université Paris-Saclay)
#2Cyril Bauland (Université Paris-Saclay)H-Index: 9
Last. Laurence Moreau (Université Paris-Saclay)H-Index: 2
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Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use singl...
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#1Takuya Oishi (University of Toyama)H-Index: 1
#2Yoshihiro Hayashi (University of Toyama)H-Index: 7
Last. Yoshinori Onuki (University of Toyama)H-Index: 15
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Abstract Our aim was to understand better the causal relationships between material attributes (MAs), process parameters (PPs), and critical quality attributes (CQAs) using an originally created large dataset and regularized linear regression models. In this study, we focused on the following three points: (1) creation of a dataset comprising several tablet production methods, (2) the influence of interaction terms of MAs and/or PPs, and (3) comparison of regularized linear regression models wit...
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#1Yunbi Xu (FOSU: Foshan University)H-Index: 41
#2Xiaogang Liu (CIMMYT: International Maize and Wheat Improvement Center)H-Index: 1
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Abstract Although long term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet the human’s demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock’s significantly higher indivi...
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#1Sangam L. DwivediH-Index: 2
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The usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP datasets and models. Using data of 18 traits across six plant species with different marker densities and training popul...
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#1Matheus Massariol Suela (UFV: Universidade Federal de Viçosa)H-Index: 1
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