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Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes
Author(s) -
Matias Filipe Inacio,
Galli Giovanni,
Correia Granato Italo Stefanine,
FritscheNeto Roberto
Publication year - 2017
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2017.01.0022
Subject(s) - biology , haplotype , single nucleotide polymorphism , genetics , snp , selection (genetic algorithm) , oryza sativa , population , context (archaeology) , marker assisted selection , genetic marker , computational biology , genotype , gene , machine learning , paleontology , demography , sociology , computer science
The 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.