Title
Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding
Date Issued
01 January 2020
Access level
metadata only access
Resource Type
book part
Author(s)
Dwivedi S.L.
Goldman I.
Ceccarelli S.
Ortiz R.
Publisher(s)
Academic Press Inc.
Abstract
Agriculture production is a major driver of destabilization of the earth's planetary boundaries within which humanity can safely operate. Producing enough food that is safe and nutritious is the biggest challenge in 21st century agriculture. Yield gains through genetic enhancement have either slowed down or not rising to the level needed to meet the ever-growing demand for nutritious food. A continuous supply of high-quality crop germplasm is the key to developing climate-resilient, resource-use efficient, nutritious and productive cultivars. Global efforts are underway to develop pre-breeding populations, by exploiting exotic germplasm including wild and weedy relatives with required characteristics to support breeding programs. Comprehensive profiling of germplasm/breeding lines (relative to uncharacterized lines) and adopting a strategy based on physiological characterization of parental lines have the potential to facilitate the accumulation of favorable alleles to enhance genetic gain in plant breeding. Advances in genomics, phenomics and bioinformatic resources have led to the deployment of several knowledge-intensive approaches to accelerate genetic gains in diverse food crops. Enhanced capability in data storage, retrieval and analysis has greatly facilitated the development of genotype-phenotype models to predict phenotypes, thus enhancing selection efficiency. Genomic-aided breeding has been successful in enhancing genetic gain relative to pedigree-based phenotypic selection. Genes controlling “recombination hotspots” and targeted recombination may provide breeders opportunity to significantly increase genetic gains. Combining genomic selection with doubled haploid technology, speed breeding and high-throughput phenomics with genotype-by-sequencing profiling allows the fast transfer of increased genetic gains per unit time. An open source software system has the potential to increase breeding efficiency through data and code sharing, while open source seed systems should allow for continued seed saving, breeding, and seed exchange without restriction. Taken together, these approaches should provide breeders with the opportunity to make genetic gains through new technologies and through the infusion of useful genetic variation in crop breeding.
Start page
89
End page
142
Volume
162
Language
English
OCDE Knowledge area
Biotecnología ambiental
Subjects
Scopus EID
2-s2.0-85082841312
Source
Advances in Agronomy
Resource of which it is part
Advances in Agronomy
ISSN of the container
00652113
ISBN of the container
978-012820767-3
Source funding
ICRISAT
Sponsor(s)
Sangam L. Dwivedi acknowledges the contribution of Ramesh Kotana of Knowledge Sharing and Innovation Program of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for arranging reprints on genetic gains in plant breeding as valuable literature resources that helped him draft his part of contribution to this manuscript. Rodomiro Ortiz thanks to the Swedish Research Council (Vetenskapsrådet) for providing the grant U-forsk 2017-05522 “Genomic prediction for breeding durum wheat along the Senegal River Basin,” which gave the funding for participating in his writing of this manuscript and making open access this publication.
An important open source computing platform for statistical analysis has now become standard for many breeding programs. Known as R, this programming language is supported by the R Foundation for Statistical Computing. The R program is available under the GNU General Public License ( Stallman, 1985 ), and a number of programs for plant breeding have been developed using its open source code. Yabe et al. (2017) developed the Breeding System Language (BSL) in R, which allows breeders to define their target species, trait genetic architectures, and breeding schemes by writing simple, self-explanatory scripts. The developers imagine that BSL will help breeders evaluate breeding schemes and enable the choice of optimal breeding strategies. Endelman (2011) used R in the development of rrBLUP, which combines ridge regression and BLUP to allow for prediction in genome-wide selection. Rosyara et al. (2016) have developed R-based software for genome-wide association research in polysomic polyploids. The existence of such open source platforms is critical for the continued development of software to facilitate genetic approaches and breeding programs.
Sources of information:
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Scopus