Genomic Selection in Oilseed Brassica: Potential, Prospects and Challenges

Rajkumar, Bangkim; Garima, .; Panjabi, Priya

doi:10.1201/9781003214991-7

Cited by 1 publication

(2 citation statements)

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“…Genome prediction offers a significant opportunity to enhance crops by improving selection efficiency and reducing the breeding cycle (Crossa et al, 2017;Rajkumar, 2022;Sharma et al, 2022). Furthermore, GP is a more reliable method and outperforms conventional breeding approaches such as pedigree and QTL-based approaches (Muir, 2007;Nakaya & Isobe, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, GS helps to incorporate unphenotyped and novel germplasm into a breeding program helping to increase the genetic gain and diversity (Budhlakoti et al, 2022). B. napus has a narrow genetic diversity, and a few studies have explored its prediction potential using GS (Li et al, 2016;Rajkumar, 2022;Werner et al, 2017;Werner et al, 2018). RILs developed from NAM populations have broad-genetic diversity due to the intensive reshuffling of the genome making them interesting tools to test the performance of genomic-enabled prediction.…”

Section: Discussionmentioning

confidence: 99%

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Harnessing genome prediction inBrassica napusthrough a nested association mapping population

Perumal,

Higgins,

Sra

et al. 2024

Preprint

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Genome prediction (GP) significantly enhances genetic gain by improving selection efficiency and shortening crop breeding cycles. Using a nested association mapping (NAM) population a set of diverse scenarios were assessed to evaluate GP for vital agronomic traits in B. napus. GP accuracy was examined by employing different models, marker sets, population sizes, marker densities, and incorporating genome-wide association (GWAS) markers. Eight models, including linear and semi-parametric approaches, were tested. The choice of model minimally impacted GP accuracy across traits. Notably, two models, rrBLUP and RKHS, consistently yielded the highest prediction accuracies. Employing a training population of 1500 lines or more resulted in increased prediction accuracies. Inclusion of single nucleotide absence polymorphism (SNaP) markers significantly improved prediction accuracy, with gains of up to 15%. Utilizing the Brassica 60K Illumina SNP array, our study effectively revealed the genetic potential of the B. napus NAM panel. It provided estimates of genomic predictions for crucial agronomic traits through varied prediction scenarios, shedding light on achievable genetic gains. These insights, coupled with marker application, can advance the breeding cycle acceleration in B. napus.

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