Plant Breeding with Genomic Selection: Gain per Unit Time and Cost
Summary
Advancements in genotyping are rapidly decreasing marker costs and increasing genome coverage. This is facilitating the use of marker-assisted selection (MAS) in plant breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for field-based phenotyping to identify agronomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic estimated breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a plant breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS breeding strategies with equal budgets for a high-investment maize (Zea mays L.) program and a low-investment winter wheat (Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per-year basis even at low GEBV accuracies. Using a previously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We conclude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter breeding cycle.
Crop Science Vol. 50 No. 5, p. 1681-1690
Genomic Selection in Wheat Breeding usingGenotyping-by-Sequencing
Summary
Genomic selection (GS) uses genomewide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS we discovered 41,371 single nucleotide polymorphisms (SNPs) in a set of 254 advanced breeding lines from CIMMYT’s semiarid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate-normal expectation-maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, although no significant differences were observed in the accuracy of genomic-estimated breeding values (GEBVs) among imputation methods. Genomic-estimated breeding value prediction accuracies with GBS were 0.28 to 0.45 for grain yield, an improvement of 0.1 to 0.2 over an established marker platform for wheat. Genotyping-by-sequencing combines marker discovery and genotyping of large populations, making it an excellent marker platform for breeding applications even in the absence of a reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low cost of GBS make this an ideal approach for genomics-assisted breeding.
The Plant Genome doi:10.3835/plantgenome2012.06.0006
Jesse Poland , Jeffrey Endelman, Julie Dawson, Jessica
Rutkoski, Shuangye Wu, Yann Manes, Susanne Dreisigacker, José Crossa, Héctor
Sánchez-Villeda, Mark Sorrells and Jean-Luc Jannink
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
José Crossa and others Trends in Plant Science Volume 22, Issue 11, November 2017, Pages 961-975
https://doi.org/10.1016/j.tplants.2017.08.011
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
Trends
In recent years, the global
climate has changed, resulting in drastic fluctuations in rainfall patterns and
increasing temperature. Sudden climate changes can cause significant economic
losses to countries worldwide.
Genetic improvement of several
economically important crops during the 20th century using phenotypic, pedigree,
and performance data was very successful. However, signs of grain yield
stagnation in some crops, especially in drought-stressed and semi-arid regions,
are evident.
Genomic selection offers the
opportunity to increase grain production in less time. International Maize and
Wheat Improvement Center (CIMMYT) maize breeding research in Sub-Saharan
Africa, India, and Mexico has shown that genomic selection can reduce the
breeding interval cycle to at least half the conventional time and produces
lines that, in hybrid combinations, significantly increase grain yield
performance over that of commercial checks.
Public and private investment
in crop genomic selection research should increase to successfully develop in
less time germplasm that is adapted to sudden climate change.
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 breeding has shown tangible genetic gains. Based on GP
results, we speculate how GS in germplasm enhancement (i.e., prebreeding)
programs could accelerate the flow of genes from gene bank accessions to elite
lines. Recent advances in hyperspectral image technology could be combined with
GS and pedigree-assisted breeding.
José Crossa and others Trends in Plant Science Volume 22, Issue 11, November 2017, Pages 961-975
https://doi.org/10.1016/j.tplants.2017.08.011
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