Literature DB >> 29858950

When less can be better: How can we make genomic selection more cost-effective and accurate in barley?

Amina Abed1,2, Paulino Pérez-Rodríguez3, José Crossa4, François Belzile5,6.   

Abstract

KEY MESSAGE: We were able to obtain good prediction accuracy in genomic selection with ~ 2000 GBS-derived SNPs. SNPs in genic regions did not improve prediction accuracy compared to SNPs in intergenic regions. Since genotyping can represent an important cost in genomic selection, it is important to minimize it without compromising the accuracy of predictions. The objectives of the present study were to explore how a decrease in the unit cost of genotyping impacted: (1) the number of single nucleotide polymorphism (SNP) markers; (2) the accuracy of the resulting genotypic data; (3) the extent of coverage on both physical and genetic maps; and (4) the prediction accuracy (PA) for six important traits in barley. Variations on the genotyping by sequencing protocol were used to generate 16 SNP sets ranging from ~ 500 to ~ 35,000 SNPs. The accuracy of SNP genotypes fluctuated between 95 and 99%. Marker distribution on the physical map was highly skewed toward the terminal regions, whereas a fairly uniform coverage of the genetic map was achieved with all but the smallest set of SNPs. We estimated the PA using three statistical models capturing (or not) the epistatic effect; the one modeling both additivity and epistasis was selected as the best model. The PA obtained with the different SNP sets was measured and found to remain stable, except with the smallest set, where a significant decrease was observed. Finally, we examined if the localization of SNP loci (genic vs. intergenic) affected the PA. No gain in PA was observed using SNPs located in genic regions. In summary, we found that there is considerable scope for decreasing the cost of genotyping in barley (to capture ~ 2000 SNPs) without loss of PA.

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Year:  2018        PMID: 29858950     DOI: 10.1007/s00122-018-3120-8

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  62 in total

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2.  Predicting quantitative traits with regression models for dense molecular markers and pedigree.

Authors:  Gustavo de los Campos; Hugo Naya; Daniel Gianola; José Crossa; Andrés Legarra; Eduardo Manfredi; Kent Weigel; José Miguel Cotes
Journal:  Genetics       Date:  2009-03-16       Impact factor: 4.562

3.  Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Authors:  José Crossa; Gustavo de Los Campos; Paulino Pérez; Daniel Gianola; Juan Burgueño; José Luis Araus; Dan Makumbi; Ravi P Singh; Susanne Dreisigacker; Jianbing Yan; Vivi Arief; Marianne Banziger; Hans-Joachim Braun
Journal:  Genetics       Date:  2010-09-02       Impact factor: 4.562

4.  Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study.

Authors:  Shengqiang Zhong; Jack C M Dekkers; Rohan L Fernando; Jean-Luc Jannink
Journal:  Genetics       Date:  2009-03-18       Impact factor: 4.562

5.  Using genotyping-by-sequencing (GBS) for genomic discovery in cultivated oat.

Authors:  Yung-Fen Huang; Jesse A Poland; Charlene P Wight; Eric W Jackson; Nicholas A Tinker
Journal:  PLoS One       Date:  2014-07-21       Impact factor: 3.240

6.  Potential of genotyping-by-sequencing for genomic selection in livestock populations.

Authors:  Gregor Gorjanc; Matthew A Cleveland; Ross D Houston; John M Hickey
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7.  Locally epistatic genomic relationship matrices for genomic association and prediction.

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Review 8.  Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding.

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Journal:  PLoS One       Date:  2016-05-26       Impact factor: 3.240

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Review 1.  Accelerating crop genetic gains with genomic selection.

Authors:  Kai Peter Voss-Fels; Mark Cooper; Ben John Hayes
Journal:  Theor Appl Genet       Date:  2018-12-19       Impact factor: 5.699

2.  Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection.

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4.  Using Genome-Wide Predictions to Assess the Phenotypic Variation of a Barley (Hordeum sp.) Gene Bank Collection for Important Agronomic Traits and Passport Information.

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5.  Genotyping crossing parents and family bulks can facilitate cost-efficient genomic prediction strategies in small-scale line breeding programs.

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Journal:  Theor Appl Genet       Date:  2021-02-27       Impact factor: 5.699

6.  Role of MdERF3 and MdERF118 natural variations in apple flesh firmness/crispness retainability and development of QTL-based genomics-assisted prediction.

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7.  GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley.

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Review 8.  Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.

Authors:  Neeraj Budhlakoti; Amar Kant Kushwaha; Anil Rai; K K Chaturvedi; Anuj Kumar; Anjan Kumar Pradhan; Uttam Kumar; Rajeev Ranjan Kumar; Philomin Juliana; D C Mishra; Sundeep Kumar
Journal:  Front Genet       Date:  2022-02-09       Impact factor: 4.599

9.  Evaluation of Genomic Selection for Seven Economic Traits in Yellow Drum (Nibea albiflora).

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10.  Genomics-assisted prediction of salt and alkali tolerances and functional marker development in apple rootstocks.

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Journal:  BMC Genomics       Date:  2020-08-10       Impact factor: 3.969

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