Trait Diversity and Potential for Selection Indices Based on Variation Among Regionally Adapted Processing Tomato Germplasm

For many horticultural crops, selection is based on quality as well as yield. To investigate the distribution of trait variation and identify those attributes appropriate for developing selection indices, we collected and organized information related to fruit size, shape, color, soluble solids, acid, and yield traits for 143 processing tomato (Solanum lycopersicum L.) lines from North America. Evaluation of the germplasm panel was conducted in a multiyear, multilocation trial. Data were stored in a flat-file format and in a trait ontology database, providing a public archive. We estimated variance components and proportion of variance resulting from genetics for each trait. Genetic variance was low to moderate (range, 0.03–0.51) for most traits, indicating high environmental influence on trait expression and/or complex genetic architecture. Phenotypic values for each line were estimated across environments as best linear unbiased predictors (BLUPs). Principal components (PC) analysis using the trait BLUPs provided a means to assess which traits explained variation in the germplasm. The first two PCs explained 28.0% and 16.2% of the variance and were heavily weighted by measures of fruit shape and size. The third PC explained 12.9% of the phenotypic variance and was determined by fruit color and yield components. Trait BLUPs and the first three PCs were also used to explore the relationship between phenotypes and the origin of the accessions. We were able to differentiate germplasm for fruit size, fruit shape, yield, soluble solids, and color based on origin, indicating regional breeding programs provide a source of trait variation. These analyses suggest that multitrait selection indices could be established that encompass quality traits in addition to yield. However, such indices will need to balance trait correlations and be consistent with market valuation. Received for publication 3 May 2012. Accepted for publication 25 July 2012. This work was supported by an award from the U.S. Department of Agricultural National Institute of Food and Agriculture (2009-85606-05673) and through gifts from the Mid-American Food Processors Association. We thank the field staff at the Campbell Soup Company in Davis, CA, and at The Ohio State University North Central Agricultural Research Station in Fremont, OH, for assistance in the coordination of field experiments. We gratefully acknowledge the assistance of Troy Aldrich in organizing, distributing, and seeding the trials. We thank Dr. Stephanie Wedryk for providing critical feedback of an early manuscript draft. We also thank Steve Smith, Red Gold, Inc.; Charles Rivara, California Tomato Research Institute; Mike Montna, California Tomato Growers Association; and Al Krueger, Ontario Processing Vegetable Growers, for discussion and or provision of resources relevant to contract structures. Present address: Syngenta, 11055 Wayzata Boulevard, Minnetonka, MN 55305. Present address: BHN Research of Gargiulo Inc., 25675 Immokalee Road, Immokalee, FL 34142. Corresponding author. E-mail: [email protected]. J. AMER. SOC. HORT. SCI. 137(6):427–437. 2012. 427 Breeders of horticultural crops and agronomic crops have often adopted different strategies and systems of selection. Breeders of grain crops have a long tradition of quantitative approaches and of collecting objective data from large populations. This practice is facilitated in grain breeding by highdensity planting, stability of the grain in the field, and the mechanization of harvest. In contrast, many horticultural crops require a labor-intensive harvest of a perishable commodity. Evaluation is often based on attributes beyond yield with appearance and quality receiving significant attention during selection. Both cost and time constrain the collection of objective data in horticultural crops, including tomato, and breeding often defaults to a qualitative decision. Thus, differences between commodities have affected approaches to selection, and the challenge remains for breeding programs targeting horticultural crops to develop the capacity to collect, store, and analyze objective trait data across multiple environments and generations in a high-throughput manner. Plant breeders are beginning to consider estimated breeding value, the merit of an individual as determined by the performance of its progeny rather than actual cultivar performance as criteria for selection (Heffner et al., 2009). Estimates of breeding value can be derived from phenotypic data and pedigree information or genome-wide selection models (GWSs) that combine phenotype and genotype (Crossa et al., 2010; de los Campos et al., 2009). Estimating a breeding value or building robust GWS models requires integrating pedigree, genotypic, and phenotypic data for large populations. Phenotypes are recorded over multiple generations, locations, and years, often with unbalanced experimental designs. To account for spatial variation between environments, unbalanced data, and pedigree relationships, best linear unbiased predictors of phenotypes are used in place of arithmetic means. Phenotypic data on the scale and scope required to estimate breeding values have recently been summarized for several agronomic crop species, including barley [Hordeum vulgare L. (Lorenz et al., 2010; Wang et al., 2012)], maize [Zea mays L. (Kump et al., 2011; Riedelsheimer et al., 2012; Tian et al., 2011)], and soft wheat [Triticum aestivum L. (Souza et al., 2012)]. Trends in breeding are also affecting how trait data are managed. Historically, traits were organized based on categorical descriptors. For example, tomato fruit shape is often classified based on categories described by the International Union for the Protection of New Varieties of Plants (2001) and the International Plant Genetic Resources Institute (1996). These systems retain some use and overlay well with objective measures but are not entirely consistent with each other nor amenable to quantitative analysis (Rodriguez et al., 2011). The use of ontology terms has been suggested as a way to organize phenotypes in a standardized and quantitative format that is also amenable to storage in databases (Brewer et al., 2006; Jung et al., 2011; Milc et al., 2011). In addition, organizing traits into a standardized format with a quantitative scale allows comparative queries across experiments. Archives of phenotypic data are the biological complement to open access genomic data. The process of measuring traits should be reliable, consistent, and objective if genotypic differences are to be detected and selection optimized. Digital phenotyping has emerged as one method to accomplish these goals (Hartmann et al., 2011). Such methods are helping to drive the transition from categorical to quantitative phenotyping that links ontology terms and trait descriptors. Tomato Analyzer software has emerged as a tool to quantify fruit size, shape, and color in a semiautomated fashion (Brewer et al., 2007; Darrigues et al., 2008; Gonzalo et al., 2009; Gonzalo and van der Knaap, 2008). When applied to a structured breeding population, the precise phenotypic quantification of color increased the proportion of variance that could be ascribed to genetic factors (Darrigues et al., 2008). Plant breeders may therefore realize increased gain under selection from efforts to collect and store quantitative phenotypic data. All crop improvement, whether marker-assisted, genomewide, or phenotype-based, is grounded on our ability to accurately partition trait variance into environmental and genetic components. To address a lack of baseline data for important tomato traits, we collected extensive data for a diverse collection of processing tomato breeding lines from North America in a multiyear, multilocation trial. Our specific objectives were to examine the range of variation for important traits, to estimate the genetic contribution to these traits, examine correlations between traits, determine how variation is distributed within and between subpopulations within the germplasm, and integrate this information to begin developing multitrait selection indices. Phenotypes were collected using standardized, quantitative methods: analysis of digital images using Tomato Analyzer (Brewer et al., 2006), chemical tests of fruit quality, and components of yield. Traits were classified into Solanaceae Phenotype Ontology terms (Jung et al., 2011; Menda et al., 2008) and stored in the Sol Genomics Network (2012) database. We determined that significant variation exists for economically important traits and that regionally adapted germplasm may serve as a reservoir for trait variation. Materials and Methods PLANT MATERIALS. A panel of 143 processing tomato lines (genotypes) representing breeding germplasm in North America was assembled by the Solanaceae Coordinated Agricultural Project (SolCAP) (Table 1). Ninety-five lines originated from breeding programs in the Great Lakes region of North America (midwestern United States and Ontario, Canada) and were considered ‘‘humid’’-adapted. Twenty-six lines were derived from germplasm adapted to Oregon or California and were included as ‘‘arid’’-adapted. The arid-adapted germplasm included 14 lines from the Cornell University breeding program that were developed from California-adapted germplasm with selection in alternate generations in California or Sinaloa, Mexico. Germplasm adapted to the production environments in the Great Lakes region or west coast of North America represent distinct genetic subpopulations (Sim et al., 2011). Pedigree records were not available for 22 lines; adaptation for these lines was reported as ‘‘undetermined.’’ Seedlings were grown inside a greenhouse and transplanted to the field 6 to 8 weeks after sowing. Transplants were spaced 0.3 m apart on raised beds with 1.54 m between beds. Ohio trials were conducted at The Ohio State University North Central Agricultural Research Station in Fremont, OH, which is located in an area of commercial tomato production. Production practices were as recommended for commercial growers. California trials were conducted at the Campbell’s Soup Company research station in Davis, CA, also using standard procedures for commercial growers. EXPERIMENT DESIGN. Field trials were conducted with an unbalanced design across three years. Control cultivars were 428 J. AMER. SOC. HORT. SCI. 137(6):427–437. 2012. Table 1. Processing tomato germplasm panel, including accession identification, donor institution, and regional adaptation. Donor no./cultivar name Donor Adaptation Donor no./cultivar name Donor Adaptation 2K1-1439 Ohio State Humid OH05-8179 Ohio State Humid 2K1-2019 Ohio State Humid OH05-8181 Ohio State Humid 2K1-2029 Ohio State Humid OH05-8184 Ohio State Humid 2K1-2054 Ohio State Humid OH05-8185 Ohio State Humid CULBPT04-1 Cornell Arid OH05-8186 Ohio State Humid CULBPT04-2 Cornell Arid OH05-8187 Ohio State Humid CULBPT04-3 Cornell Arid OH05-8188 Ohio State Humid CULBPT04-4 Cornell Arid OH05-8193 Ohio State Humid CULBPT04-5 Cornell Arid OH05-8197 Ohio State Humid CULBPT-05-10 Cornell Arid OH05-8206 Ohio State Humid CULBPT-05-11 Cornell Arid OH05-8210 Ohio State Humid CULBPT-05-15 Cornell Arid OH05-8214 Ohio State Humid CULBPT-05-18 Cornell Arid OH08-5201 Ohio State Und. CULBPT-05-20 Cornell Arid OH08-5202 Ohio State Und. CULBPT-05-21 Cornell Arid OH08-5203 Ohio State Und. CULBPT-05-22 Cornell Arid OH08-5204 Ohio State Und. CULBPT-05-9 Cornell Arid OH08-5205 Ohio State Und. CULBPT-A46-2 Cornell Arid OH08-5206 Ohio State Und. E3259 Ohio State Humid OH08-5207 Ohio State Und. E6203/LA4024 Ohio State Arid OH08-5210 Ohio State Und. F02-7530 Ohio State Humid OH08-5211 Ohio State Und. F03-6331 Ohio State Humid OH08-5213 Ohio State Und. F03-7463 Ohio State Humid OH08-5215 Ohio State Und. F06-1013-1 Ohio State Humid OH08-5216 Ohio State Und. F06-1014-1 Ohio State Humid OH08-7438 Ohio State Und. F06-2041 Ohio State Humid OH08-7439 Ohio State Und. F06-2054 Ohio State Humid OH08-7454 Ohio State Und. F06-2058 Ohio State Humid OH08-7457 Ohio State Und. FG02-188 Ohio State Humid OH08-7458 Ohio State Und. ‘Heinz 1706’/LA4345 TGRC Humid OH08-7459 Ohio State Und. ‘Hunt 100’/LA3144 TGRC Arid OH08-7460 Ohio State Und. ‘M82’/LA3475 TGRC Arid OH08-7466 Ohio State Und. OH03-6439 Ohio State Humid OH08-7469 Ohio State Und. OH05-8018 Ohio State Humid OH08-7470 Ohio State Und. OH05-8022 Ohio State Humid OH2641 Ohio State Humid OH05-8025 Ohio State Humid OH3614 Ohio State Humid OH05-8027 Ohio State Humid OH5-8127 Ohio State Humid OH05-8028 Ohio State Humid OH5-8157 Ohio State Humid OH05-8030 Ohio State Humid OH5-8164 Ohio State Humid OH05-8192 Ohio State Humid OH7814 Ohio State Humid OH05-8036 Ohio State Humid OH7870 Ohio State Humid OH05-8040 Ohio State Humid OH7983 Ohio State Humid OH05-8044 Ohio State Humid OH8243/PI 601423 Ohio State Humid OH05-8046 Ohio State Humid OH8245 Ohio State Humid OH05-8048 Ohio State Humid OH832 Ohio State Humid OH05-8053 Ohio State Humid OH8446 Ohio State Humid OH05-8059 Ohio State Humid OH8556 Ohio State Humid OH05-8062 Ohio State Humid OH86120 Ohio State Humid OH05-8064 Ohio State Humid OH8614-1 Ohio State Humid OH05-8068 Ohio State Humid OH87160 Ohio State Humid OH05-8069 Ohio State Humid OH88119 Ohio State Humid OH05-8070 Ohio State Humid OH9241 Ohio State Humid OH05-8072 Ohio State Humid OH9242 Ohio State Humid OH05-8074 Ohio State Humid OH981049 Ohio State Humid OH05-8078 Ohio State Humid OH981067 Ohio State Humid OH05-8079 Ohio State Humid OH981136 Ohio State Arid OH05-8087 Ohio State Humid OH981205 Ohio State Humid OH05-8090 Ohio State Humid OH987034 Ohio State Humid