Abstract
Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).
Highlights
Based on the use of genomic information and prediction of the genetic merit of animals, genomic selection is changing breeding strategies and approaches in livestock (Goddard and Hayes, 2009)
The assumption that, in the single nucleotide polymorphism (SNP)-best linear unbiased prediction (BLUP) or genomic BLUP (GBLUP) model, each of the SNPs explains equal variance, i.e., that the more complex traits are controlled by very many quantitative trait loci (QTL), each with a tiny effect, could be imprecise if a trait is affected by a small number of QTL, each with a large effect (Meuwissen et al, 2001; VanRaden, 2008)
For the group of traits with low heritability, the calculated a3, b3, c3, and d3 were equal to 0.8225, 0.1413, 0.0324, and 0.0036, respectively. Those parameters were composed in the procedure of FMixFN, as FMixFN starts running, the program determines which group of variances is calculated based on the heritability of the experimental trait
Summary
Based on the use of genomic information and prediction of the genetic merit of animals, genomic selection is changing breeding strategies and approaches in livestock (Goddard and Hayes, 2009). Among many agricultural animals and plants, estimated breeding values (EBV) predicted from genomic information are widely used (Duchemin et al, 2012; Pollak et al, 2012; Preisinger, 2012; Ibáñez-Escriche et al, 2014; Samorè and Fontanesi, 2016; Mrode et al, 2018). Comparative studies on both simulated and real data have shown that genomic EBV (GEBV) tends to have higher accuracy than breeding values estimated using pedigree relationships. Genomic Selection, FCF-MixP, Model affecting the trait and the distribution of their effects (Daetwyler et al, 2008; Goddard, 2009; Meuwissen, 2009). In a simulation study in which the genetic model included a finite number of loci with exponentially distributed effects, the Bayes-based model provided more accurate prediction of genetic value than GBLUP
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