Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels

M. Di Guardo , B. Farneti , I. Khomenko , G. Modica , A. Mosca , G. Distefano , L. Bianco , M. Troggio , F. Sottile , S. La Malfa , F. Biasioli , A. Gentile

Horticulture Research ›› 2021, Vol. 8 ›› Issue (1) : 27

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Horticulture Research ›› 2021, Vol. 8 ›› Issue (1) :27 DOI: 10.1038/s41438-021-00465-7
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Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels
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Abstract

Almond is appreciated for its nutraceutical value and for the aromatic profile of the kernels. In this work, an almond collection composed of 96 Sicilian accessions complemented with 10 widely cultivated cultivars was phenotyped for the production of volatile organic compounds using a proton-transfer time-of-flight mass spectrometer and genotyped using the Illumina Infinium®18 K Peach SNP array. The profiling of the aroma was carried out on fresh and roasted kernels enabling the detection of 150 mass peaks. Sixty eight, for the most related with sulfur compounds, furan containing compounds, and aldehydes formed by Strecker degradation, significantly increased during roasting, while the concentration of fifty-four mass peaks, for the most belonging to alcohols and terpenes, significantly decreased. Four hundred and seventy-one robust SNPs were selected and employed for population genetic studies. Structure analysis detected three subpopulations with the Sicilian accessions characterized by a different genetic stratification compared to those collected in Apulia (South Italy) and the International cultivars. The linkage-disequilibrium (LD) decay across the genome was equal to r2 = 0.083. Furthermore, a high level of collinearity (r2 = 0.96) between almond and peach was registered confirming the high synteny between the two genomes. A preliminary application of a genome-wide association analysis allowed the detection of significant marker-trait associations for 31 fresh and 33 roasted almond mass peaks respectively. An accurate genetic and phenotypic characterization of novel germplasm can represent a valuable tool for the set-up of marker-assisted selection of novel cultivars with an enhanced aromatic profile.

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M. Di Guardo, B. Farneti, I. Khomenko, G. Modica, A. Mosca, G. Distefano, L. Bianco, M. Troggio, F. Sottile, S. La Malfa, F. Biasioli, A. Gentile. Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels. Horticulture Research, 2021, 8 (1) : 27 DOI:10.1038/s41438-021-00465-7

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References

[1]

FAOstat. Agriculture Data. 2018.

[2]

Zohary, D. & Hopf, M. Domestication of plants in the Old World: the origin and spread of cultivated plants in West Asia, Europe and the Nile Valley. (Oxford University Press, Oxford, UK, 2000).

[3]

Klein, A. M. et al. Wild pollination services to California almond rely on semi-natural habitat. J. Appl. Ecol. 49, 723-732 (2012).

[4]

Kodad, O. et al. Oil content, fatty acid composition and tocopherol concentration in the Spanish almond genebank collection. Sci. Hortic. 177, 99-107 (2014).

[5]

Currò, S. et al. Analysis of S-allele genetic diversity in sicilian almond germplasm comparing different molecular methods. Plant Breed. 134, 713-718 (2015).

[6]

Tamura, M. et al. Identification of self-incompatibility genotypes of almond by allele-specific PCR analysis. Theor. Appl. Genet. 101, 344-349 (2000).

[7]

Channuntapipat, C. et al. Identification of incompatibility genotypes in almond (Prunus dulcis Mill.) using specific primers based on the introns of the S-alleles . Plant Breed. 122, 164-168 (2003).

[8]

El Hadi, M. A. M., Zhang, F. J., Wu, F. F., Zhou, C. H. & Tao, J. Advances in fruit aroma volatile research. Molecules 18, 8200-8229 (2013).

[9]

Klee, H. J. Improving the flavor of fresh fruits: genomics, biochemistry, and biotechnology. N. Phytol. 187, 44-56 (2010).

[10]

Klee, H. J. & Tieman, D. M. The genetics of fruit flavour preferences. Nat. Rev. Genet. 19, 347-356 (2018).

[11]

Farneti, B. et al. Exploring blueberry aroma complexity by chromatographic and direct-injection spectrometric techniques. Front. Plant Sci. 8, 1-19 (2017).

[12]

Farneti, B. et al. Genome-wide association study unravels the genetic control of the apple volatilome and its interplay with fruit texture. J. Exp. Bot. 68, 1467-1478 (2017).

[13]

Acierno, V., Fasciani, G., Kiani, S., Caligiani, A. & van Ruth, S. PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins. Food Chem. 289, 591-602 (2019).

[14]

Yener, S. et al. Monitoring single coffee bean roasting by direct volatile compound analysis with proton transfer reaction time-of-flight mass spectrometry. J. Mass Spectrom. 51, 690-697 (2016).

[15]

Carbone, F. et al. Development of molecular and biochemical tools to investigate fruit quality traits in strawberry elite genotypes. Mol. Breed. 18, 127-142 (2006).

[16]

Costa, F. et al. QTL validation and stability for volatile organic compounds (VOCs) in apple. Plant Sci. 211, 1-7 (2013).

[17]

Zargar, S. M. et al. Recent advances in molecular marker techniques: Insight into QTL mapping, GWAS and genomic selection in plants. J. Crop Sci. Biotechnol. 18, 293-308 (2015).

[18]

Rasheed, A. et al. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol. Plant 10, 1047-1064 (2017).

[19]

Aranzana, M. J. et al. Prunus genetics and applications after de novo genome sequencing: achievements and prospects . Hortic. Res. https://doi.org/10.1038/s41438-019-0140-8 (2019).

[20]

Sánchez-Pérez, R., Howad, W., Dicenta, F., Arús, P. & Martínez-Gómez, P. Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breed. 126, 310-318 (2007).

[21]

Font i Forcada CF, i Martí ÀF, I Company RS . Mapping quantitative trait loci for kernel composition in almond. BMC Genet. https://doi.org/10.1186/1471-2156-13-47 (2012).

[22]

Fernández i Martí, A., Font i Forcada, C. & Socias i Company, R. Genetic analysis for physical nut traits in almond. Tree Genet. Genomes 9, 455-465 (2013).

[23]

Font i Forcada, C., Velasco, L., Socias i Company, R. & Fernández i Martí, Á. Association mapping for kernel phytosterol content in almond. Front. Plant Sci. 6, 1-12 (2015).

[24]

Cantin, C. M., Wang, X. W., Almira, M., Arús, P. & Eduardo, I. Inheritance and QTL analysis of chilling and heat requirements for flowering in an interspecific almond x peach (Texas x Earlygold) F2 population. Euphytica https://doi.org/10.1007/s10681-020-02588-9 (2020).

[25]

Baró-Montel, N. et al. Exploring sources of resistance to brown rot in an interspecific almond × peach population. J. Sci. Food Agric. 99, 4105-4113 (2019).

[26]

Fresnedo-Ramírez, J. et al. QTL mapping of pomological traits in peach and related species breeding germplasm. Mol. Breed. 35, 166 (2015).

[27]

Rafalski, J. A. Association genetics in crop improvement. Curr. Opin. Plant Biol. 13, 174-180 (2010).

[28]

Mackay, I. & Powell, W. Methods for linkage disequilibrium mapping in crops. Trends Plant Sci. 12, 57-63 (2007).

[29]

Distefano, G. et al. Genetic diversity and relationships among Italian and foreign almond germplasm as revealed by microsatellite markers. Sci. Hortic. 162, 305-312 (2013).

[30]

Distefano, G. et al. HRM analysis of chloroplast and mitochondrial DNA revealed additional genetic variability in Prunus . Sci. Hortic. 197, 124-129 (2015).

[31]

Gasic, K. et al. Development and Evaluation of a 9 K SNP Addition to the Peach Ipsc 9 K SNP Array v1. In: American Society for Horticultural Science, annual conference (2019).

[32]

Xiao, L. et al. HS-SPME GC/MS characterization of volatiles in raw and dry-roasted almonds (Prunus dulcis) . Food Chem. 151, 31-39 (2014).

[33]

Erten, E. S. & Cadwallader, K. R. Identification of predominant aroma components of raw, dry roasted and oil roasted almonds. Food Chem. 217, 244-253 (2017).

[34]

Bernal, J., Manzano, P., Diego, J. C., Bernal, J. L. & Nozal, M. J. Comprehensive two-dimensional gas chromatography coupled with static headspace sampling to analyze volatile compounds: application to almonds. J. Sep. Sci. 37, 675-683 (2014).

[35]

Franklin, L. M. et al. Chemical and sensory characterization of oxidative changes In roasted almonds undergoing accelerated shelf life. J. Agric. Food Chem. 65, 2549-2563 (2017).

[36]

Oliveira, I., Malheiro, R., Meyer, A. S., Pereira, J. A. & Gonçalves, B. Application of chemometric tools for the comparison of volatile profile from raw and roasted regional and foreign almond cultivars (Prunus dulcis) . J. Food Sci. Technol. 56, 3764-3776 (2019).

[37]

Yeretzian, C., Jordan, A., Brevard, H. & Lindinger, W. On-Line Monitoring of Coffee Roasting by Proton-Transfer-Reaction Mass-Spectrometry. In: Flavor Release. 10-112 (American Chemical Society, 2000).

[38]

Wieland, F. et al. Online monitoring of coffee roasting by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS): towards a real-time process control for a consistent roast profile. Anal. Bioanal. Chem. 402, 2531-2543 (2012).

[39]

Gloess, A. N. et al. Evidence of different flavour formation dynamics by roasting coffee from different origins: on-line analysis with PTR-ToF-MS. Int. J. Mass Spectrom. 365-366, 324-337 (2014).

[40]

Whitfield, F. B. Volatiles from interactions of Maillard reactions and lipids. Crit. Rev. Food Sci. Nutr. 31, 1-58 (1992).

[41]

Batool, Z. et al. Determination of furan and its derivatives in preserved dried fruits and roasted nuts marketed in China using an optimized HS-SPME GC/MS method. Eur. Food. Res. Technol. https://doi.org/10.1007/s00217-020-03556-2 (2020).

[42]

Hao, J., Xu, X. L., Jin, F., Regenstein, J. M. & Wang, F. J. HS-SPME GC-MS characterization of volatiles in processed walnuts and their oxidative stability. J. Food Sci. Technol. 57, 2693-2704 (2020).

[43]

Gong, Y., Kerrihard, A. L. & Pegg, R. B. Characterization of the volatile compounds in raw and roasted Georgia pecans by HS-SPME-GC-MS. J. Food Sci. 83, 2753-2760 (2018).

[44]

Wang, S., Adhikari, K. & Hung, Y. C. Acceptability and preference drivers of freshly roasted peanuts. J. Food Sci. 82, 174-184 (2017).

[45]

Kwak, J. et al. Volatile organic compounds released by enzymatic reactions in raw nonpareil almond kernel. Eur. Food Res. Technol. 241, 441-446 (2015).

[46]

Yang, J. et al. Shelf-life of infrared dry-roasted almonds. Food Chem. 138, 671-678 (2013).

[47]

Esfahlan, A. J., Jamei, R. & Esfahlan, R. J. The importance of almond (Prunus amygdalus L.) and its by-products . Food Chem. 120, 349-360 (2010).

[48]

Mexis, S. F., Badeka, A. V. & Kontominas, M. G. Quality evaluation of raw ground almond kernels (Prunus dulcis): effect of active and modified atmosphere packaging, container oxygen barrier and storage conditions . Innov. Food Sci. Emerg. Technol. 10, 580-589 (2009).

[49]

Lee, J., Xiao, L., Zhang, G., Ebeler, S. E. & Mitchell, A. E. Influence of storage on volatile profiles in roasted almonds (Prunus dulcis) . J. Agric. Food Chem. 62, 11236-11245 (2014).

[50]

Valdés, A. et al. Monitoring the oxidative stability and volatiles in blanched, roasted and fried almonds under normal and accelerated storage conditions by DSC, thermogravimetric analysis and ATR-FTIR. Eur. J. Lipid Sci. Technol. 117, 1199-1213 (2015).

[51]

Agila, A. & Barringer, S. Effect of roasting conditions on color and volatile profile including HMF level in sweet almonds (Prunus dulcis) . J. Food Sci. https://doi.org/10.1111/j.1750-3841.2012.02629.x (2012).

[52]

Farneti, B. et al. Development of a novel phenotypic roadmap to improve blueberry quality and storability. Front. Plant Sci. 11, 1-21 (2020).

[53]

Di Guardo, M. et al. ASSIsT: an automatic SNP scoring tool for in- and out-breeding species. Bioinformatics 31, 3873-3874 (2015).

[54]

Alioto, T. et al. Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J. 101, 455-472 (2020).

[55]

Sánchez-Pérez, R. et al. Mutation of a bHLH transcription factor allowed almond domestication. Science 364, 1095-1098 (2019).

[56]

Verde, I. et al. The Peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity. BMC Genomics 18, 225 (2017).

[57]

Arús, P., Yamamoto, T., Dirlewanger, E. & Abbott A. G. Synteny in the Rosaceae https://doi.org/10.1002/9780470650349.ch4 (2010).

[58]

Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945-959 (2000).

[59]

Marchese, A., Bos, R. I., Road, N. & Malling, E. Short Communication The origin of the self-compatible almond ‘Supernova'. Plant Breed. 107, 105-107 (2008).

[60]

Aranzana, M. J., Abbassi, E. K., Howad, W. & Arús, P. Genetic variation, population structure and linkage disequilibrium in peach commercial varieties. BMC Genet. https://doi.org/10.1186/1471-2156-11-69 (2010).

[61]

Cao, K. et al. Genetic diversity, linkage disequilibrium, and association mapping analyses of peach (Prunus persica) landraces in China . Tree Genet. Genomes 8, 975-990 (2012).

[62]

Cao, K. et al. Comparative population genomics reveals the domestication history of the peach, Prunus persica, and human influences on perennial fruit crops. Genome Biol. 15, 1-15 (2014).

[63]

Li X wei, et al. Peach genetic resources: diversity, population structure and linkage disequilibrium. BMC Genet. https://doi.org/10.1186/1471-2156-14-84 (2013).

[64]

Liu, S. et al. Linkage disequilibrium in North China and Xingjiang apricot cultivars (Prunus armeniaca L.) . In: Acta Horticulturae. 269-284 (International Society for Horticultural Science (ISHS), Leuven, Belgium, 2018).

[65]

Campoy, J. A. et al. Genetic diversity, linkage disequilibrium, population structure and construction of a core collection of Prunus avium L. landraces and bred cultivars. BMC Plant Biol. 16, 1-15 (2016).

[66]

Flint-Garcia, S. A., Thornsberry, J. M. & Buckler, E. S. Structure of linkage disequilibrium in plants. Annu Rev. Plant Biol. 54, 357-374 (2003).

[67]

Omodei, F. Descrizione e caratterizzazione biometrica di cultivar Siciliane di mandorlo (P. Amygdalus L.) in conservazione ‘ex situ’ . 2007.

[68]

R Core Team . R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2016).

[69]

Zhao, S., Guo, Y., Sheng, Q. & Shyr, Y. Heatmap3: an improved heatmap package with more powerful and convenient features. BMC Bioinforma. 15, 15-16 (2014).

[70]

Galili, T. dendextend: An R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31, 3718-3720 (2015).

[71]

Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer-Verlag New York, 2016).

[72]

Rohart, F., Gautier, B., Singh, A. & Lê Cao, K. A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, 1-19 (2017).

[73]

Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull v. 19, 11 (1987).

[74]

Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611-2620 (2005).

[75]

Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359-361 (2012).

[76]

Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801-1806 (2007).

[77]

Di Guardo, M. et al. Genetic structure analysis and selection of a core collection for carob tree germplasm conservation and management. Tree Genet. Genomes 15, 41 (2019).

[78]

Endelman, J. B. Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. Plant Genome 4, 250-255 (2011).

[79]

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289-300 (1995).

[80]

Storey, J., Bass, A., Dabney, A. & Robinson, D. qvalue: Q-value estimation for false discovery rate control. R package version 2.20.0. http://github.com/jdstorey/qvalue (2020).

[81]

Laidò, G. et al. Linkage disequilibrium and genome-wide association mapping in tetraploid wheat (Triticum turgidum L.) . PLoS ONE https://doi.org/10.1371/journal.pone.0095211 (2014).

[82]

Breseghello, F. & Sorrells, M. E. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars . Genetics 172, 1165-1177 (2006).

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