Efficient genomic selection using ensemble learning and ensemble feature reduction

Genomic selection (GS) is a popular breeding method that uses genome-wide markers to predict plant phenotypes. Empirical studies and simulations have shown that GS can greatly accelerate the breeding cycle, beyond what is possible with traditional quantitative trait locus (QTL) approaches. GS is a regression problem, where one often uses SNPs to predict the phenotypes. Since the SNP data are extremely high-dimensional, of the order of 100 K dimensions, it is difficult to make accurate phenotypic predictions. Moreover, finding the optimal prediction model is computationally very costly. Out of thousands of SNPs, usually only a few influence a particular phenotypic trait. We first of all show how ensemble-based regression techniques give better prediction accuracy compared to traditional regression methods, which have been used in existing papers. We then further improve the prediction accuracy by using an ensemble of feature selection and feature extraction techniques, which also reduces the time to compute the regression model parameters. We predict three traits: grain yield, time to 50% flowering and plant height for which the existing methods give an accuracy of 0.304, 0.627 and 0.341, respectively. Our proposed regression model gives an accuracy of 0.330, 0.674 and 0.458 for these traits. Additionally, we also propose a computationally efficient regression model that reduces the computation time by as much as 90% and gives an accuracy of 0.342, 0.580 and 0.411, respectively

Genomic Selection and Association Mapping in Rice (<i>Oryza sativa</i>): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

<div><p>Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.</p></div