Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits

Abstract

Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.

References

[1] Padmanabhan P, Cheema A, Paliyath G . Solanaceous Fruits Including Tomato, Eggplant, and Peppers. In: Caballero B, Finglas PM, Toldrá F, eds. Encyclopedia of Food and Health.Elsevier: Oxford, 2016, 24-32.

[2] Bebeli PJ, Mazzucato A . The Solanaceae-a review of recent research on genetic resources and advances in the breeding of tomato, pepper and eggplant. Eur J Plant Sci Biotechnol. 2008; 2: 3-30.

[3] Tripodi P, Rabanus-Wallace MT, Barchi L et al. Global range expansion history of pepper (capsicum spp.) revealed by over 10,000 genebank accessions. Proc Natl Acad Sci U S A. 2021; 118: e2104315118.

[4] Wang L, Li J, Zhao J et al. Evolutionary developmental genetics of fruit morphological variation within the solanaceae. Front Plant Sci. 2015; 6: 248.

[5] van der Knaap E, Chakrabarti M, Chu YH et al. What lies beyond the eye: the molecular mechanisms regulating tomato fruit weight and shape. Front Plant Sci. 2014; 5: 227.

[6] Lin T, Zhu G, Zhang J et al. Genomic analyses provide insights into the history of tomato breeding. Nat Genet. 2014; 46: 1220-6.

[7] Zhu G, Wang S, Huang Z et al. Rewiring of the fruit Metabolome in tomato breeding. Cell. 2018; 172: 249-61.

[8] Denise T, Zhu G, Lin T et al. A chemical genetic roadmap to improved tomato flavor. Science. 2017; 355: 391-4.

[9] Zhao J, Sauvage C, Zhao J et al. Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor. Nat Commun. 2019; 10: 1534.

[10] Tong H, Nikoloski Z . Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data. J Plant Physiol. 2021; 257: 153354.

[11] Uffelmann E, Huang QQ, Munung NS et al. Genome-wide association studies. Nat Rev Methods Prim. 2021; 1: 59.

[12] Tripodi P, Soler S, Campanelli G et al. Genome wide association mapping for agronomic, fruit quality, and root architectural traits in tomato under organic farming conditions. BMC Plant Biol. 2021; 21.

[13] Tripodi P, Greco B . Large scale phenotyping provides insight into the diversity of vegetative and reproductive organs in a wide collection of wild and domesticated peppers (capsicum spp.). Plan Theory. 2018; 7: 103.

[14] International Plant Genetic Resources Institute (IPGRI) . Descriptors for tomato (Lycopersicon spp.) International Plant Genetic Resources Institute: Rome, 1996. https://www.bioversityinternational.org/e-library/publications/detail/descriptors-for-tomato-lycopersicon-spp/.

[15] Figàs MR, Prohens J, Raigon MD et al. Characterization of a collection of local varieties of tomato (Solanum lycopersicum L.) using conventional descriptors and the high-throughput phenomics tool tomato Analyzer. Genet Resour Crop Evol. 2015; 62: 189-204.

[16] Brewer MT, Lang L, Fujimura K et al. Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species. Plant Physiol. 2006; 141: 15-25.

[17] Darrigues A, Hall J, van der Knaap E et al. Tomato analyzer-color test: a new tool for efficient digital phenotyping. J Am Soc Hortic Sci. 2008; 133: 579-86.

[18] Adhikari P, McNellie J, Panthee DR . Detection of quantitative trait loci (QTL) associated with the fruit morphology of tomato. Genes. 2020; 11: 1117.

[19] Marefatzadeh-Khameneh M, Fabriki-Ourang S, Sorkhilalehloo B et al. Genetic diversity in tomato (Solanum lycopersicum L.) germplasm using fruit variation implemented by tomato analyzer software based on high throughput phenotyping. Genet Resour Crop Evol. 2021; 68: 2611-25.

[20] Nankar AN, Tringovska I, Grozeva S et al. Tomato phenotypic diversity determined by combined approaches of conventional and high-throughput tomato Analyzer Phenotyping. Plan Theory. 2020; 9: 197.

[21] Hurtado M, Vilanova S, Plazas M et al. Phenomics of fruit shape in eggplant (Solanum melongena L.) using tomato Analyzer software. Sci Hortic. 2013; 164: 625-32.

[22] Colonna V, D’Agostino N, Garrison E et al. Genomic diversity and novel genome-wide association with fruit morphology in capsicum, from 746k polymorphic sites. Sci Rep. 2019; 9: 10067.

[23] Nankar AN, Tringovska I, Grozeva S et al. Application of high-throughput phenotyping tool tomato Analyzer to characterize Balkan capsicum fruit diversity. Sci Hortic. 2020; 260: 108862.

[24] Collard BCY, Mackill DJ . Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Phil Trans R Soc B. 2008; 363: 557-72.

[25] Jannink JL, Lorenz AJ, Iwata H . Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics. 2010; 9: 166-77.

[26] Beyene Y, Gowda M, Perez-Rodriguez P et al. Application of genomic selection at the early stage of breeding pipeline in tropical maize. Front Plant Sci. 2021; 12: 685488.

[27] Xu Y, Ma K, Zhao Y et al. Genomic selection: a breakthrough technology in rice breeding. Crop J. 2021; 9: 669-77.

[28] Cappetta E, Andolfo G, Matteo AD et al. Accelerating tomato breeding by exploiting genomic selection approaches. Plan Theory. 2020; 9: 1236.

[29] Hong JP, Ro N, Lee HY et al. Genomic selection for prediction of fruit-related traits in pepper (capsicum spp.). Front Plant Sci. 2020; 11: 570871.

[30] Nankar AN, Todorova V, Tringovska I et al. A step towards Balkan Capsicum annuum L. core collection: phenotypic and biochemical characterization of 180 accessions for agronomic, fruit quality, and virus resistance traits. PLoS One. 2020; 15: e0237741.

[31] Goode EL . In: Schwab M, ed. Encyclopedia of Cancer . Springer: Berlin, Heidelberg, 2011, 2043-8.

[32] Liang Z, Gupta SK, Yeh CT et al. Phenotypic data from inbred parents can improve genomic prediction in pearl millet hybrids. G3 (Bethesda). 2018; 8: 2513-22.

[33] Brinton J, Simmonds J, Wingen L et al. A haplotype-led approach to increase the precision of wheat breeding. Commun Biol. 2020; 3: 712.

[34] Won S, Park JE, Son JH et al. Genomic prediction accuracy using haplotypes defined by size and hierarchical clustering based on linkage disequilibrium. Front Genet. 2020; 11: 134.

[35] Zhao C, Zhang Y, Du J et al. Crop phenomics: current status and perspectives. Front Plant Sci. 2019; 10: 714.

[36] Jia Y, Jannink JL . Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics. 2012; 192: 1513-22.

[37] Fernandes SB, Dias KOG, Ferreira DF et al. Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. Theor Appl Genet. 2018; 131: 747-55.

[38] Strecker J, Rodríguez G, Njanji I et al. Tomato Analyzer Color Test User Manual Version 3. 2010. https://vanderknaaplab.uga.edu/files/Color_Test_3.0_Manual.pdf.

[39] Rodríguez G, Strecker J, Brewer M et al. Tomato Analyzer user manual version 3. 2010. https://vanderknaaplab.uga.edu/files/Tomato_Analyzer_3.0_Manual.pdf.

[40] Rodríguez GR, Moyseenko JB, Robbins MD et al. Tomato analyzer: a useful software application to collect accurate and detailed morphological and colorimetric data from two-dimensional objects. J Vis Exp. 2010; 37: e1856.

[41] Bates D, Mächler M, Bolker B et al. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015; 67: 1-48.

[42] Browning BL, Browning SR . Genotype imputation with millions of reference samples. Am J Hum Genet. 2016; 98: 116-26.

[43] Kumar S, Stecher G, Li M et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018; 35: 1547-9.

[44] Letunic I, Bork P . Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021; 49: W293-6.

[45] Liu G, Wang Y, Wong L . FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium. BMC Bioinformatics. 2010; 11: 66.

[46] Meuwissen THE, Hayes BJ, Goddard ME . Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001; 157: 1819-29.

[47] Endelman JB . Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome. 2011; 4: 250-5.

[48] Covarrubias-Pazaran G. Software update: moving the R package sommer to multivariate mixed models for genome-assisted prediction. bioRxiv 354639, 2018.

[49] Mayer CD, Lorent J, Horgan GW . Exploratory analysis of multiple omics datasets using the adjusted RV coefficient. Stat Appl Genet Mol Biol. 2011; 10: 14.

[50] Kuhn M . Building predictive models in R using the caret package. J Stat Softw. 2008; 28: 1-26.

[51] Chawla NV, Bowyer KW, Hall LO et al. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002; 16: 321-57.