Open AccessGenome-wide association study and genomic prediction in citrus: Potential of genomics-assisted breeding for fruit quality traits
Author(s) -
Mai F. Minamikawa,
Kenichi aka,
Eli Kaminuma,
Hiromi KajiyaKanegae,
Akio Onogi,
Shingo Goto,
Terutaka Yoshioka,
Atsushi Imai,
Hiroko Hamada,
Takeshi Hayashi,
Satomi Matsumoto,
Yūichi Katayose,
Atsushi Toyoda,
Asao Fujiyama,
Yasukazu Nakamura,
Tokurou Shimizu,
Hiroyoshi Iwata
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-017-05100-x
Subject(s) - best linear unbiased prediction , genome wide association study , biology , single nucleotide polymorphism , genomics , genetic association , genomic selection , population , genetics , selection (genetic algorithm) , genome , genotype , computer science , medicine , gene , machine learning , environmental health
Novel genomics-based approaches such as genome-wide association studies (GWAS) and genomic selection (GS) are expected to be useful in fruit tree breeding, which requires much time from the cross to the release of a cultivar because of the long generation time. In this study, a citrus parental population (111 varieties) and a breeding population (676 individuals from 35 full-sib families) were genotyped for 1,841 single nucleotide polymorphisms (SNPs) and phenotyped for 17 fruit quality traits. GWAS power and prediction accuracy were increased by combining the parental and breeding populations. A multi-kernel model considering both additive and dominance effects improved prediction accuracy for acidity and juiciness, implying that the effects of both types are important for these traits. Genomic best linear unbiased prediction (GBLUP) with linear ridge kernel regression (RR) was more robust and accurate than GBLUP with non-linear Gaussian kernel regression (GAUSS) in the tails of the phenotypic distribution. The results of this study suggest that both GWAS and GS are effective for genetic improvement of citrus fruit traits. Furthermore, the data collected from breeding populations are beneficial for increasing the detection power of GWAS and the prediction accuracy of GS.