Single-step genomic prediction of fruit-quality traits using phenotypic records of non-genotyped relatives in citrus.

Atsushi Imai,Nobuhito Mitani,Terutaka Yoshioka,Masashi Yamamoto,Keisuke Nonaka,Takeshi Hayashi,Naofumi Hiehata,Hiroshi Fukamachi,Takeshi Kuniga,David D Fang

doi:10.1371/journal.pone.0221880

Abstract

The potential of genomic selection (GS) is currently being evaluated for fruit breeding. GS models are usually constructed based on information from both the genotype and phenotype of population. However, information from phenotyped but non-genotyped relatives can also be used to construct GS models, and this additional information can improve their accuracy. In the present study, we evaluated the utility of single-step genomic best linear unbiased prediction (ssGBLUP) in citrus breeding, which is a genomic prediction method that combines the kinship information from genotyped and non-genotyped relatives into a single relationship matrix for a mixed model to apply GS. Fruit weight, sugar content, and acid content of 1,935 citrus individuals, of which 483 had genotype data of 2,354 genome-wide single nucleotide polymorphisms, were evaluated from 2009–2012. The prediction accuracy of ssGBLUP for genotyped individuals was similar to or higher than that of usual genomic best linear unbiased prediction method using only genotyped individuals, especially for sugar content. Therefore, ssGBLUP could yield higher accuracy in genotyped individuals by adding information from non-genotyped relatives. The prediction accuracy of ssGBLUP for non-genotyped individuals was also slightly higher than that of conventional best linear unbiased prediction method using pedigree information. This indicates that ssGBLUP can enhance prediction accuracy of breeding values for non-genotyped individuals using genomic information of genotyped relatives. These results demonstrate the potential of ssGBLUP for fruit breeding, including citrus.

Highlights

Genomic selection (GS) is considered to be a practical tool for accelerating genetic improvement in plant breeding [1,2], and the potential of GS is being evaluated for use in fruit breeding [3]
The genotypic data necessary for GS can only be obtained from living individuals, most individuals evaluated in breeding programs are culled after selection
Obtaining both genotype and phenotype records for GS model construction is more difficult for fruit breeding than it is for animal breeding or other crop breeding

Summary

Introduction

Genomic selection (GS) is considered to be a practical tool for accelerating genetic improvement in plant breeding [1,2], and the potential of GS is being evaluated for use in fruit breeding [3]. Statistical GS models are generally constructed based on information from both the genotypes and phenotypes of a population [5]. Phenotypic data from non-genotyped relatives can be used to construct GS models when full pedigree records are available [6]. This situation is common in fruit breeding because an organized fruit breeding program has a well-defined recording system and continuously accumulates phenotypic records along with pedigree information, such as in [7,8]. Phenotypic and pedigree information from non-genotyped relatives could be used to improve the accuracy of GS modeling in fruit breeding

Methods

Results

Conclusion