Location: Citrus and Other Subtropical Products Research
Title: Rapid volatile metabolomics and genomics in large strawberry populations segregating for aromaAuthor
BARBEY, C - University Of Florida | |
FOLTA, K - University Of Florida | |
WHITAKER, V - University Of Florida | |
VERNA, S - University Of Florida | |
Bai, Jinhe |
Submitted to: Acta horticulturae
Publication Type: Proceedings Publication Acceptance Date: 2/1/2017 Publication Date: 4/1/2017 Citation: Barbey, C.R., Folta, K.M., Whitaker, V.M., Verna, S., Bai, J. 2017. Rapid volatile metabolomics and genomics in large strawberry populations segregating for aroma. Acta Horticulturae. 1156:695-702. doi:10.17660/ActaHortic.2017.1156.102. Interpretive Summary: Three volatile organic compound (VOC)-related segregating phenotypes were discovered using a non-targeted, ion-wise alignment approach. This method enabled a rapid and accurate depiction of the population-wide VOC metabolome in strawberry. The breadth of this analysis allowed facile generation of agronomically useful descriptive data about the population, including the covariance of volatile sensory compounds and their abundances with respect to time and the environment. Technical Abstract: Volatile organic compounds (VOCs) in strawberry (Fragaria spp.) represent a large portion of the fruit secondary metabolome, and contribute significantly to aroma, flavor, disease resistance, pest resistance and overall fruit quality. Understanding the basis for volatile compound biosynthesis and its regulation is of great importance for the genetic improvement of cultivated varieties. Due to the complexity of the autoallooctoploid strawberry genome and the large influence of environmental factors over volatile expression, genetic studies on strawberry volatile biosynthesis typically require large segregating populations that are continually resampled for volatile expression. This data complexity frequently limits discovery to a small number of volatiles of ideal behavior and simple genetics. To address these problems and others, we demonstrate the ability to rapidly generate and describe the population-wide strawberry volatile metabolome through spectral reconstruction of unsupervised mass-aligned peaks. This approach generates unbiased, populationwide quantitative data suited for identifying segregating compounds as well as multivariate statistical tests and network analyses. Using this approach, we determined a number of quantitatively segregating volatiles which were used subsequently in quantitative trait locus (QTL) analysis. We further demonstrate the application of a single nucleotide polymorphism (SNP) genotyping array with the power to resolve recombination events in the autoallooctoploid background. |