Abstract

Chilean fruit export industry, which includes many species of fruit such as peaches and nectarines, is the largest in the southern hemisphere for these fruit tree species. Consumers of fresh fruit demand highquality products that have multiple attributes. However, satisfying consumers is a continuous challenge since our target markets are located very far away from Chile. In particular, exporting peaches and nectarines represents a challenge since these fruits are highly perishable. For this reason, fruits must be placed in cold storage (CS) for a long time. However, depending on the temperature and time duration, CS can induce a physiological disorder known as chilling injury (CI). The primary CI symptom is mealiness, which is defined as a lack of juice in the flesh. Mealiness impacts significantly fruit quality and the competitiveness of the peach industry (low prices). As a consequence, strategies such as the Chilean peach-breeding program have been focused on developing new peach varieties that are resistant to mealiness. The problem is that the breeding process to obtain a new variety is costly and time consuming (at least 12 years). For this reason, the development of biotechnology tools to speed up or maximize breeding results is extremely important to solve the mealiness problem as soon as possible. Quantitative Trait Loci (QTL) analyses have been used successfully to identify molecular markers associated with Mendelian traits or quantitative traits controlled for a major gene in peaches. The mealiness phenotype is a complex trait that is controlled by several genes, and the environment has a significant effect. Although QTL analyses and “omics” approaches have been performed in order to select candidate genes involved in mealiness, none of these approaches alone has had success. Recent advances in “omics” technologies, such as methylome sequencing, metabolome detecting, and genotyping profiling, have made it possible to dissect the genetics of complex traits represented by molecular phenotypes (gene expression, metabolite profiles, and methylation level, among others). The metabolome reflects the interaction between an organism’s genome and its environment, which is an excellent probe of its phenotype (metabolite traits; mTraits). On the other hand, there is increasing evidence that epigenetic markers such as DNA methylation contribute to phenotypic variation by regulating gene transcription, developmental plasticity, and interactions with the environment (methylation level traits; meTraits). Furthermore, changes in molecular phenotypes (particularly metabolome and methylome) for cold stress similar to conditions of fruit storage and shipping have been broadly described. Considering this background, we hypothesized that the co-localization on a genetic map among QTL, metabolite Quantitative Trait Loci (mQTL), and methylation Quantitative Trait Loci (meQTL) will make it possible to identify reliable biomarkers and candidate genes associated with mealiness in peaches. The primary goal of this proposal is to identify candidate genes and biomarkers associated with mealiness in peaches using QTL, mQTL, and meQTL. In order to accomplish this objective, we will phenotype (juice content and other cues) a segregating population of 151 contrasting siblings ('Venus' selfing) for mealiness over three seasons. Additionally, we will measure the mealiness in commercial varieties in the last season of the project for validating the proposed biomarkers. In parallel, we will saturate a genetic linkage map of this population using genotyping by sequencing in order to improve the density of the available markers. Furthermore, we will identify and quantify the metabolome (GC-MS untargeted metabolomics analysis of polar compounds) of fruit samples of 50 siblings of the mapping population contrasting for mealiness over two seasons. Metabolites with significant differences of redundancy between contrasting phenotypes will be considered to be mTraits. We will sequence the methylome (whole bisulfite sequencing using a Hiseq2500) from fruits of the same 50 siblings noted above after storage for one season. The differentially methylated regions will be identified, and they will be used as meTraits. We will perform QTL analyses using conventional phenotypes and all of the molecular phenotype data generated in this proposal (mTraits, meTraits) plus others molecular phenotypes developed by our group (gene expression traits) in order to identify candidate genes involved in mealiness and identify and validate biomarkers associated with this trait for supporting the breeding process. Therefore, our results will be: 1) A database of phenotypic data (three seasons) of peach segregating population, 2) an ultra-high density genetic linkage map, 3) QTL, mQTL, and meQTL for mealiness, and 4) candidate genes and biomarkers associated with mealiness identified and validated. A full integration of the molecular phenotypes combined with genotypes will allow us to elucidate the network of genetics factors underlying the phenotypic variation of mealiness.