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Acidity is a critical component of the apple fruit consumption experience. In previous biparental family studies, two large-effect acidity QTLs were reported using freshly harvested fruit. Objectives of this study were to determine the number and location of QTLs for acidity variation in a large apple breeding program and ascertain the quantitative effects and breeding relevance of QTL allelic combinations at harvest and after commercially relevant periods of cold storage. Pedigree-connected germplasm of 16 full-sib families representing nine important breeding parents, genotyped for the 8K SNP array, was assessed for titratable acidity at harvest and after 10- and 20-week storage treatments, for three successive seasons. Using pedigree-based QTL mapping software, FlexQTL™, evidence was found for only two QTLs, on linkage groups 16 (the reported Ma locus) and LG 8 (here called Ma3) that jointly explained 66 ± 5% of the phenotypic variation. An additive allele dosage model for the two QTLs effectively explained most acidity variation, with an average of + 1.8 mg/L at harvest per high-acidity allele. The more high-acidity alleles, the faster the depletion with storage, with all combinations appearing to eventually converge to a common baseline. All parent cultivars and selections had one or two of the four possible high-acidity alleles. Each QTL had a rare second high-acidity allele with stronger or reduced effect. Diagnostic SNP markers were identified for QTL alleles derived from distinct sources. Combined QTL effects highlighted utility of the DNA-based information in new cultivar development for targeting desired fruit acidity levels before or after storage.

Establishing marker-locus-trait associations to enable marker-assisted breeding depends on having an extensive, reliable database for phenotypic traits of interest in relevant germplasm. A reference germplasm set of 467 apple (Malus 3domestica Borkh.) cultivars, selections, and seedlings (referred to as individuals) was identified as part of the USDA-Specialty Crop Research Initiative (SCRI) project, RosBREED. The germplasm set provides efficient allelic representation of current parents in RosBREED demonstration apple breeding programs at Cornell University, Washington State University, and the University of Minnesota. Phenotyping at the three locations was conducted according to standardized protocols, focusing on fruit traits evaluated at harvest and after 10 and 20 weeks of refrigerated storage. Phenotypic data were collected for the sensory texture traits of firmness, crispness, and juiciness as well as for instrumental texture measures. In 2010 and 2011, fruit from 216 and 330 individuals, respectively, were harvested and a total of 369 individuals were evaluated over the two years. Correlations between sensory and instrumental texture measures were high in some instances. Moderate year-to-year repeatability of trait values was observed. Because each location had a largely unique set of individuals, as well as differing environmental conditions, means, ranges, and phenotypic variances differed greatly among locations for some traits. Loss of firmness and crispness during storage was more readily detected instrumentally than by the sensory evaluation. Fruit texture traits, significant to apple breeder decision-making yet unobservable until tree maturity, are ideal candidates for marker-assisted breeding (MAB) and markerassisted selection (MAS). Marker-locus-trait associations, validated in germplasm relevant to a particular breeding program, facilitate MAB (Bliss, 2010). MAB is used to select parents with favorable alleles and MAS is imposed on seedling populations to eliminate those with unfavorable allele combinations. Both MAB and MAS can reduce time and expense for new cultivar development in a tree fruit breeding program. Fruit texture is a focus of breeders because of its role in shaping consumer acceptance of new apple cultivars. Harker et al. (2003) reviewed studies that investigated consumer preferences for apple and factors influencing willingness to buy. They reported that although subsets of consumers vary in fruit quality expectations, most adults respond to texture and acidity as determinants of fruit quality. In a study of New Zealand consumers, adults preferred harder and crisper apples. Although the authors reported that consumers remember differences in apple texture for days, Harker et al. (2003) predicted that fruit quality standards will evolve as consumers’ expectations change. Speeding the breeding process through the use of molecular markers will aid apple breeders in developing higher quality fruit. A study using ‘Red Delicious’, ‘Gala’, and ‘Braeburn’ showed that in certain cultivars, firmness is of high importance to consumers, especially in combination with other fruit quality factors: firm apples, above a 53Newton threshold, can be improved on by changes in titratable acidity (TA) and soluble solids content (SSC), but soft apple acceptance cannot be improved on with changes in TA or SSC (Harker et al., 2008). These findings highlighted the use of genetic markers to select for fruit texture traits. Studies of apple texture have used both sensory panels and instrumental measures (e.g., Evans et al., 2010; Ioannides et al., 2007; McKay et al., 2011; Zdunek et al., 2011). Differences in terms used to describe texture as well as their definitions make comparing sensory panel results difficult. For instance, the meaning of the term ‘‘crispness’’ differs across studies. Fillion and Kilcast (2002), using a trained sensory panel and a consumer panel, defined the term ‘‘crunchy’’ as describing lower-pitched sounds that continue throughout chewing, whereas ‘‘crisp’’ described a higher-pitched sound resulting from the clean split of the first bite. Both crisp and crunchy designations, when applied to food, express that the material breaks in the mouth rather than buckling or deforming. By studying sounds during biting dry and wet crisp foods, Vickers and Bourne (1976) defined the crispness sensation as a characteristic sound of a range of frequencies emitted during biting. For a thorough discussion of the crispness sensation, refer to Roudaut et al. (2002). In our study, described by Evans et al. (2012), ‘‘crispness’’ refers to the intensity of the cracking noise of the first bite. ‘‘Firmness’’ is equivalent to ‘‘hardness’’ and determined while chewing. ‘‘Juiciness’’ is expressed juice on chewing. A trained sensory panel, as small as three experienced individuals, has been shown to be reliable in a postharvest study of fruit texture (Brookfield et al., 2011). That panel was able to discern greater separation among cultivars than was achieved with instrumental measures. Although sensory panels more closely mimic consumer perception of fruit texture, they can be time-consuming and difficult to standardize. Puncture tests, performed with various mechanized penetrometers, are typically used to determine firmness and juiciness (e.g., Harker et al., 2006). Harker et al. (2002) found puncture tests superior to chewing sounds and tensile measurements in forecasting sensory panelists’ perception of texture traits. The Mohr Digi-Test (Mohr and Associates, Richland, WA) computerized penetrometer captures data that correlate well with sensory firmness and sensory crispness by collecting constant velocity measurements (Evans et al., 2010). This is especially useful, because crispness has proven difficult to measure instrumentally with other devices. Received for publication 19 Dec. 2012. Accepted for publication 22 Jan. 2013. Funded by the Specialty Crop Research Initiative Competitive Grant 2009-51181-05808 of the USDA’s National Institute of Food and Agriculture. To whom reprint requests should be addressed; e-mail lubyx001@umn.edu. 296 HORTSCIENCE VOL. 48(3) MARCH 2013 Establishing marker-locus-trait associations for texture traits depends on having an extensive, reliable phenotype database for traits of interest in breeding germplasm. Without high-quality phenotypic data, association statistics that link genomic sequences to traits cannot realize full potential (Bassil and Volk, 2010). Moreover, when standardized phenotyping protocols are used across several breeding programs, the resulting large data sets give more power to studies that detect and characterize quantitative trait loci (QTL) than would be had if each program conducted a smaller, isolated study. A reference germplasm set of 467 individual genotypes including cultivars, selections, and seedlings was identified as part of the USDA-SCRI RosBREED project. The germplasm set provides efficient allelic representation of current parents in the large, publicly funded U.S. apple breeding programs of Cornell University (CU), Washington State University (WSU), and the University of Minnesota (UMN). Extensive phenotypic data, including instrumental and sensory measures of fruit texture, were collected on these individuals at each location in the years 2010 and 2011 under three regimes: at harvest, after 10 weeks of cold storage and 1 week at room temperature, and after 20 weeks of cold storage and 1 week at room temperature. Phenotypic data were collected adhering to a standardized protocol (Evans et al., 2012). The objective in this article is to elaborate on methods used to obtain data on sensory and instrumental measures of fruit texture traits in the RosBREED apple Crop Reference Set (CRS) and describe variation and repeatability observed for these traits. We also report correlations between sensory and instrumental measures used in this study. Materials and Methods Plant material. The RosBREED apple CRS and supplementing individuals included 154 cultivars and parental selections as well as 313 seedlings of families chosen to provide efficient allelic representation of important breeding parents for a total of 467 related individuals. Subsets of the RosBREED CRS were grown at the UMN Horticultural Research Center near Chaska, MN, at the WSU Tree Fruit Research & Extension Center in Wenatchee, WA, and at the CU New York State Agricultural Experiment Station in Geneva, NY. Evaluation of a reference Fig. 1. Apple equatorial slice demonstrating MDT-1 fruit texture measures described by Evans et al. (2010) and Mohr and Mohr (2000). R1 is the outer area of the apple directly below the skin, R2 is the main edible portion of the fruit, and R3 contains the core. Red lines indicate regions in which traits are determined. Fig. 2. Proportions of variance attributable to year, individual, sampling and year 3 individual for fruit texture measures at three locations at harvest. Analysis of variance was used. Data from University of Minnesota (UMN), Washington State University (WSU), and Cornell University (CU) are shown in shades of blue, purple, and green, respectively. Abbreviations are as follows: A1, A2 = average pressure regions 1 and 2, respectively (N); C0 = creep at boundary between regions 1 and 2 (cm); Cn = crispness measurement (derived value); E2 = pressure at core boundary (N); M1, M2 = maximum pressure regions 1 and 2, respectively (N); OAH = overall average hardness (N); OMH = overall maximum hardness (N); QF = quality factor (derived value). The sensory measures of crispness, firmness, and juiciness were assessed on a 5-point scale. HORTSCIENCE VOL. 48(3) MARCH 2013 297 | BREEDING, CULTIVARS, ROOTSTOCKS, AND GERMPLASM RESOURCES

The USDA - Specialty Crop Research Initiative-funded RosBREED project is focused on enabling marker-assisted breeding in the Rosaceae. New molecular tools for selection need to be developed before this technology will be widely accepted and applied to apple breeding programs. As well as detailed genotypic data of inter-related progenies, parents and ancestor cultivars, fully descriptive phenotypic data also need to be collected. For apple, fruit phenotyping begins at harvest, followed by 10 and 20 weeks regular storage, each followed by 7 days shelf life at room temperature. The standardized phenotyping protocols agreed by breeding teams in Washington, Minnesota and New York states will be presented in this paper.

Sweetness is one of the most important fruit quality traits in breeding programs, determining the overall quality and flavor perception of apples. Selecting for this trait using conventional breeding methods is challenging due to the complexity of its genetic control. In order to improve the efficiency of trait selection via DNA-based markers, extensive studies focused on the detection of quantitative trait loci (QTL) and the development of DNA-based markers associated with QTL regions for traits of interest. Newly discovered QTLs detected in multiple apple breeding populations are presented here for individual sugars (fructose, glucose, sucrose, and sorbitol) and soluble solids content (SSC) at harvest, after 10, and 20 weeks of refrigerated storage followed by 1 week at room temperature in two successive years. A total of 1416 polymorphic SNPs were filtered from the RosBreed Apple SNP Infinium® array for QTL analysis using FlexQTL™ software. QTLs for individual sugars were identified on linkage groups (LG) 1, 2, 3, 4, 5, 9, 11, 12, 13, 15, and 16, and QTLs for SSC were found on LGs 2, 3, 12, 13, and 15. One QTL region on LG 1 was consistently identified for both fructose and sucrose from harvest through storage in both years, which accounted for 34–67 and 13–41 % of total phenotypic variation, respectively. These stable QTLs with high explained phenotypic variation on LG 1 for fructose content indicate a promising genomic region for DNA-based marker development to enable marker-assisted breeding for sweetness selection in apple breeding programs.