In plant breeding, many seedlings must be tested to increase the chance of obtaining desirable genotypes. The number of seedlings used in tree fruit breeding is more restricted than in annual crop breeding because fruit trees are large and slow-growing. The success rate and cost are almost directly related to the space the trees occupy and for how long (Hansche, 1983).
The establishment of molecular markers of a fruit trait to identify the genotype of offspring for a fruit trait at the seedling stage without having to wait 3 to 5 years for the plant to fruit offers a tremendous advantage and saving in time, space, and money. Even for characters evaluated using vegetative organs such as disease resistance, the phenotypic performance can fluctuate depending on the development stage of shoots evaluated and on environmental conditions such as temperature and humidity. Selection using molecular markers that identify a genotype can be done accurately with low cost and time expenditure, regardless of plant and environmental conditions.
Some molecular markers associated with QTLs of fruit have been developed in some fruit crops in genomic studies (Graham et al., 2009; Kenis et al., 2008; Moriya et al., 2010; Quilot et al., 2004; Segura et al., 2009; Zhang et al., 2010). Because important target traits such as fruit ripening time, fruit weight, and sugar content are quantitative, the fluctuation of phenotypic values with environmental conditions hinders the identification of useful molecular markers. The efficiency of detecting molecular markers associated with a target trait depends on broad-sense heritability in QTL analysis in addition to the genetic marker density on linkage maps (Ukai, 2000).
Japanese pear (Pyrus pyrifolia) is an important fruit crop in Japan, ranking third in production after citrus and apple (Kanayama, 2006). ‘Kosui’ and ‘Hosui’, released by the National Institute of Fruit Tree Science (NIFTS), are the current leading cultivars in Japan and occupy 65% of the total area of Japanese pear orchards in Japan (Tamura, 2006). The Japanese pear breeding program, begun in 1909 (Kajiura and Sato, 1990), continues to aim at developing new cultivars that ripen at various times with high productivity and fruit quality, low production costs, high disease resistance, and freedom from physiological disorders. One of the most important targets of the NIFTS Japanese pear breeding program is to develop superior early-ripening cultivars, which are currently lacking; most fruits are harvested after July in Japan.
Some useful DNA markers have been developed for Japanese pear breeding. S4sm-haplotype-specific DNA markers to identify self-compatibility (Okada et al., 2008), a molecular marker associated with the pear scab resistance gene Vnk (Terakami et al., 2006), and one associated with black spot disease (Terakami et al., 2007) facilitate breeding because genotypes for these traits are controlled by a single gene at a single locus.
NIFTS has constructed a genetic linkage map of Japanese pear from simple sequence repeat and amplified-fragment-length polymorphism markers (Iketani et al., 2001; Nishitani et al., 2009; Terakami et al., 2009; Yamamoto et al., 2007).
So far, the effectiveness of detected QTLs and molecular markers associated with them has been often assessed by the percentages of the variance of the detected QTL effects accounting for the observed phenotypic population variance of the trait (Graham et al., 2009; Kenis et al., 2008; Moriya et al., 2010; Quilot et al., 2004; Segura et al., 2009; Zhang et al., 2010). However, phenotypic variance contains both genetic and environmental variance components. The effectiveness of detected QTLs essentially depends on the percentage of the variance of the detected QTL effects accounting for the genetic variance of the trait. When molecular markers associated with the detected QTLs explain most of the genetic variance, the selection based on the molecular markers is very effective even if the percentage of the variance of the detected QTL effects accounting for the phenotypic variance is low. Therefore, it is needed to estimate the genetic variance for assessing the detected QTL effects. However, reports on molecular markers associated with QTLs in tree fruit breeding do not show the genetic variance (Graham et al., 2009; Kenis et al., 2008; Moriya et al., 2010; Quilot et al., 2004; Segura et al., 2009; Zhang et al., 2010).
Genetic variance in tree fruit crops that are propagated vegetatively can be easily estimated by subtracting environmental variance from phenotypic variance. Environmental variance in a given field (orchard) comprises variance among years, among trees within a genotype, among fruit within a tree, the genotype × year interaction variance, and the tree × year interaction variance, all of which can be estimated from repeated measurements of replications of several genotypes (Hansche and Beres, 1966; Hansche and Brooks, 1965; Sato et al., 2000; Yamada et al., 1993, 2002). Once accurate environmental variance components are obtained in the field, environmental variances under any numbers of replications and measurement repetitions can be estimated (Nyquist, 1991; Yamada et al., 1993, 2002).
Abe et al. (1993) reported a small environmental variance in fruit ripening date (FRD) of Japanese pear seedlings, but their experiment covered only 2 years, and they did not estimate the environmental variance components of the tree × year interaction or the tree and fruit effects.
The objective of our study was to obtain accurate estimates of the environmental variance components of FRD, which is usually used for practical phenotypic selection, as used in further studies assessing detecting QTLs, based on a large data set including several yearly repetitions. Environmental variance components have been obtained by analysis of variance (ANOVA) in many fruit crops (Hansche and Beres, 1966; Hansche and Brooks, 1965; Sato et al., 2000; Yamada et al., 1993, 2002). We did the same here.
QTL analysis is often made using an offspring population from a cross (Graham et al., 2009; Kenis et al., 2008; Moriya et al., 2010; Quilot et al., 2004; Segura et al., 2009; Zhang et al., 2010). Genetic variance can be estimated from estimates of environmental variance components in such a population. Therefore, we additionally estimated the genetic variance and heritability of FRD in a family (cross) using some F1 full-sib families.
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