Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes

Abstract

2951 RESEARCHG enomic selection (GS) is based on the use of genomic information to predict the genetic value of phenotyped or nonphenotyped individuals (Heffner et al., 2009). It has been effectively used in animal (de Campos et al., 2015;Farah et al., 2016) and plant breeding ( Jarquin et al., 2016;Huang et al., 2016). This strategy seeks to exploit information from markers in linkage disequilibrium (LD) with chromosomal regions that control the traits under evaluation (Heslot et al., 2015). To best explore this information, the development of genotyping technologies continues to generate significant quantities of markers. However, as marker densities increase, collinearity also increases. Additionally, evaluating a large number of individuals and complex experimental designs may lead to infeasibility of computational analysis.To reduce the number of genomic variables maintaining information, markers can be rearranged in LD blocks, known as haplotype blocks (Cuyabano et al., 2014). Capturing epistatic interactions of nearby single-nucleotide polymorphisms (SNPs), improving estimates of alleles identical by descent, and reducing the number of tests in association studies (which leads to the reduction in type I error rate) are some advantages of using haplotypes. Besides, it infers groups of correlated genes and alleles, ABSTRACTThe implementation of single-nucleotide polymorphism (SNP)-based genomic selection has demonstrated great predictive potential in plants. However, its application is sometimes limited to the biallelism of the marker. In this context, the use of haplotype blocks as multiallelic markers might improve genomic prediction. This study was performed to compare the predictive ability of Bayesian genomic prediction models using haplotypes (confidence interval and four-gamete), individual SNPs, and sets of SNPs selected according to haplotype construction. The use of haplotype matrices increased the predictive ability and selection coincidence with the phenotypic selection for the maize (Zea mays L.) breeding population. However, this was not observed for the rice (Oryza sativa L.) population, in which the use of the nonreduced SNP matrix was more efficient. Overall, the use of reduced SNP matrices did not lead to better predictive abilities. No difference was observed between the genomic prediction methods used. We found that the use of haplotypes has potential to increase predictive ability of genomic prediction in breeding populations of allogamous plants or plants with high multiallelism.

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

“…Another type of GWAS is haplotype-block-based (BM) GWAS models. Close SNPs are more likely to be inherited together; haplotype blocks are important in genetic studies [21], such as diversity studies [22], GWAS, and genomic selection [23][24][25]. The use of haplotypes in the genomic prediction of traits of allogamous plants can increase its predictive ability by 20% [23].…”

“…Close SNPs are more likely to be inherited together; haplotype blocks are important in genetic studies [21], such as diversity studies [22], GWAS, and genomic selection [23][24][25]. The use of haplotypes in the genomic prediction of traits of allogamous plants can increase its predictive ability by 20% [23]. A restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS) procedure [26] is one recently proposed BM [27][28][29].…”

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

“…On the other hand, Calus et al [28] determined that the inclusion of haplotypes in genomic prediction models was beneficial for low-heritability traits. Matias et al [29] found that the use of haplotypes in the prediction of complex traits of maize increased the predictive ability by 20%. From these and other studies, it appears that the haplotype approach emerges as a methodological variant that can improve not only the predictive abilities but also the precision in the detection of genomic regions in association studies [30,31,32,33].…”

“…Additionally, the use of haplotypes reduces the degrees of freedom in the models of prediction or genomic association (reduction of dimensionality), which contributes to greater precision in the detection of QTL [40]. It should be noted that there are few studies that have evaluated the combined use of GS and haplotypes in plants [29,41,42]. In particular, these methods have been implemented in predominantly self-pollinated (autogamous) species—for example, soybean and wheat [41,42]—in which extensive LD values can be found in their genomes, which favors the identification of haplotypes, while in outcrossing species (allogamous) such as Eucalyptus and most species of forest interest, LD usually decays at short genomic distances [43,44], which allows the identification of smaller haplotype blocks and those conformed by a smaller number of alleles.…”

Genomic prediction enables early but low‐intensity selection in soybean segregating progenies

“…We obtained the PA, selection coincidence as described by Matias, Galli, Correia Granato, and Fritsche‐Neto (2017), and the predicted mean of the improved population (population mean + genetic gain) for 5, 10, 25, and 50% of percentage selected, and based on that, we attained the selection gain (%). Additionally, using 10,000 random samples of the phenotype vector, we checked what proportion of these random resamplings resulted in a mean superior to improved population.…”