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Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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http://dx.doi.org/10.1016/j.molp.2024.03.007 | DOI Listing |
BMC Genomics
December 2024
Feed and Forage Development, International Livestock Research Institute, Addis Ababa, Ethiopia.
Background: Lablab is one of the conventionally grown multi-purpose crops that originated in Africa. It is an annual or short-lived perennial forage legume which has versatile uses (as a vegetable and dry seeds, as food or feed, or as green manure) but is yet to receive adequate research attention and hence remains underexploited. To develop new and highly productive lablab varieties, using genomics-assisted selection, the present study aimed to identify quantitative trait loci associated with agronomically important traits in lablab and to assess the stability of these traits across two different agro-ecologies in Ethiopia.
View Article and Find Full Text PDFPlant Methods
December 2024
Department of Molecular Genetics, Dong-A University, Saha-gu Nakdong-Daero 550 beongil 37, Busan, 49315, Republic of Korea.
Background: Genetic markers are crucial for breeding crops with desired agronomic traits, and their development can be expedited using next-generation sequencing (NGS) and bioinformatics tools. Numerous tools have been developed to design molecular markers, enhancing the convenience, accuracy, and efficiency of molecular breeding. However, these tools primarily focus on genetic variants within short user-input sequences, despite the availability of extensive omics data for genomic variants.
View Article and Find Full Text PDFPlant Signal Behav
December 2025
Institute of Integrative Plant Biology, School of Life Sciences, Jiangsu Normal University, Xuzhou, China.
Bacterial leaf blight (BLB) caused by pv. () has shown a high incidence rate in rice fields in recent years. Rice resistance breeding is considered as the most effective method for achieving economical and sustainable management of BLB disease.
View Article and Find Full Text PDFG3 (Bethesda)
December 2024
Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA.
Mate selection plays an important role in breeding programs. The Usefulness Criterion was proposed to improve mate selection, combining information on both the mean and standard deviation of the potential offspring of a cross, particularly in clonally propagated species where large family sizes are possible. Predicting the mean value of a cross is generally easier than predicting the standard deviation, especially in outbred species when the linkage of alleles is unknown and phasing is required.
View Article and Find Full Text PDFMol Biol Rep
December 2024
ICAR - Indian Institute of Pulses Research, Regional Station, Khordha, 752055, India.
Background: Charcoal Rot (CR) poses a significant threat to mung bean crops by reducing yield, making the development of resistant varieties crucial for stable production and food security. This study evaluated 19 newly identified mung bean landraces using biochemical traits and SSR markers, revealing genetic variability, CR disease reactions, and traits influencing yield and resistance, which provide valuable insights for breeding CR-resistant, high-yielding varieties.
Methods And Results: Mung bean landraces were evaluated for their response to CR using 4 biochemical parameters, and 10 SSR markers to assess genetic variability and disease resistance.
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