Abstract
This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
Highlights
In mid-2019, the world population reached 7.7 billion inhabitants and a further rise to 9.7 billion by 2050 is estimated [1]
predictive capacity (PC) rises as heritability increases (Figs 1–3)
The use of Regularized Quantile Regression models proved effective in genomic selection studies, for allowing an accelerated development of superior genotypes in relation to traditional Genomic selection (GS) methodologies
Summary
In mid-2019, the world population reached 7.7 billion inhabitants and a further rise to 9.7 billion by 2050 is estimated [1]. Since the Green Revolution in the 1960s, which caused a boost in the production potential of several crops, it is generally expected that plant breeding efforts will be able to secure the required yield gains [4]. Quantile regression for genomic prediction study in autogamous plants considerably, and one of the main reasons is the use of improved cultivars. In Brazil, coffee cultivars that were released and are still in use, e.g., “Mundo Novo”, are 240% more productive than introduced varieties [5]. Aside from focusing on higher yields, require the improvement of several other traits [6], e.g., the development of plants with a more appropriate architecture for higher plant density and mechanical management, better resistance and tolerance to biotic and abiotic stresses, adaptation to and stability in different cultivation environments, and a higher fruit and grain quality [7,8,9,10,11]
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