Genetic architecture of key traits for Prunus crop improvement: an overview of 25 years of curated genomic and breeding data

Abstract

The extensive accumulation of genetic, genomic, expression, and breeding data on Prunus species often results in valuable information being lost or difficult to access for breeding purposes. We report a recent effort to increase curation on Prunus data in the Genome Database for Rosaceae (GDR, rosaceae.org) and a case study that explores 25 years of curated data (from 1998 to 2023) to uncover the genetic architecture of key traits in Prunus species, provide actionable insights for breeding, and encourage the use of shared molecular data across Prunus species. The curated data includes 177 genetic maps, primarily for almond (19), apricot (21), peach (52), and sweet cherry (46). A total of 28 971 trait-associated loci were reported, with 72.4% derived from genome-wide association studies, 18.7% from quantitative trait loci (QTL), and 8.9% from Mendelian trait loci. Notably, 76.4% of these loci are associated with morphological and quality traits, reflecting breeders’ focus on consumer preferences. We identified 16 potential QTL hotspots linked to key traits such as morphology, phenology, fruit quality, and disease resistance. Additionally, we identified 17 high-priority syntenic regions among peach, sweet cherry, and almond. The colocalized markers and genes within the QTL hotspots and syntenic regions offer a valuable resource for tool development for Prunus breeding, especially for complex polyploid genomes and lesser studied species with limited genetic and genomic data.

Acknowledgements

This research was supported by the USDA National Research Support Project (NRSP10) and the SCRI-NIFA Award 2022-51181-38449.

Author contributions

S.J., M.I., K.G., and D.M. conceived the project. S.J. designed the alignment pipeline, data storage models, and performed alignments. P.Z. coded the alignment script and T.L. wrote the alignment and data loading script. M.I. compiled and analyzed data and wrote the manuscript with input from S.J. and K.G. All authors reviewed, edited, and agreed to the published version of the manuscript.

Data availability

All data presented in this work are available on the GDR website (ww.rosaceae.org).

Conflict of interest statement

The authors declare that there is no conflict of interest.

52. Yamamoto T, Yamaguchi M, Hayashi T. SSR, STS, AFLP RAPD .

. 2005;74:204-13

53. Decroocq V, Favé MG, Hagen L. et al. Development and transfer-ability of apricot and grape EST microsatellite markers across taxa. Theor Appl Genet. 2003;106:912-22

54. Salazar JA, Ruiz D, Campoy JA. et al. Inheritance of reproductive phenology traits and related QTL identification in apricot. Tree Genet Genomes. 2016;12:71

55. Joobeur T, Viruel MA, De Vicente MC. et al. Construction of a saturated linkage map for Prunus using an almond x peach F2 progeny. Theor Appl Genet. 1998;97:1034-41

56. Kale SM, Jaganathan D, Ruperao P. et al. Prioritization of can-didate genes in “QTL-hotspot” region for drought tolerance in chickpea (Cicer arietinum L.). Sci Rep. 2015;15:1-14

57. Boopathi NM, Tiwari GJ, Jena SN. et al. Identification of stable and multiple environment interaction QTLs and candidate genes for fiber productive traits under irrigated and water stress condi-tions using intraspecific RILs of Gossypium hirsutum var. MCU5 X TCH1218. Frontiers in Plant Science. 2022;13:851504

58. Gasic K, Saski C. Advances in fruit genetics. In: G. A. Lang (Ed.), Achieving Sustainable Cultivation of Temperate Zone Tree Fruits and Berries. Burleigh Dodds Science Publishing Limited, London, 2019, 135-80

59. Zhang X, Sun J, Zhang Y. et al. Hotspot regions of quantitative trait loci and candidate genes for ear-related traits in maize: a literature review. Genes. 2024;15:15

60. Li X, Meng X, Jia H. et al. Peach genetic resources: diversity, population structure and linkage disequilibrium. BMC Genet. 2023;14:1-16

61. Devos KM, Moore G, Gale MD. Conservation of marker synteny during evolution. Euphytica. 1995;85:367-72

62. Duran C, Edwards D, Batley J. Genetic maps and the use of synteny. Methods Mol Biol. 2009;513:41-55

63. Kang YJ, Lee T, Lee J. et al. Translational genomics for plant breeding with the genome sequence explosion. Plant Biotechnol J.2016;14:1057-69

64. Schmidt R. Synteny: recent advances and future prospects. Curr Opin Plant Biol. 2000;3:97-102

65. Jung S, Cestaro A, Troggio M. et al. Whole genome comparisons of Fragaria, Prunus and Malus reveal different modes of evo-lution between Rosaceous subfamilies. BMC Genomics. 2012;13: 1-12

66. Mancini MC, Cardoso-Silva CB, Sforça DA. et al. “Targeted sequencing by gene synteny,” a new strategy for polyploid species: sequencing and physical structure of a complex sug-arcane region. Front Plant Sci. 2018;9:332370

67. Wang J, Liu W, Zhu D. et al. Chromosome-scale genome assembly of sweet cherry (Prunus avium L.) cv. Tieton obtained using long-read and Hi-C sequencing. Hort Res. 2020;7: 122

68. Le Dantec L, Girollet N, Gouzy J. et al. An improved assembly of the diploid ‘Regina’ sweet cherry genome. PO1193. Plant & Animal Genome Conference XXVII Abstracts & Workshops (p. 232). San Diego, CA, USA, 2019;

69. Sánchez-Pérez R, Pavan S, Mazzeo R. et al. Mutation of a bHLH transcription factor allowed almond domestication. Sci-ence. 2019;364:1095-8

70. Wang Y, Tang H, Debarry JD. et al. MCScanX: a toolkit for detec-tion and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40:e49-9

71. Cao K,Wang B,Fang W.et al. Combined nature and human selec-tions reshaped peach fruit metabolome. Genome Biol. 2022;23: 1-25

72. Cao K, Zhou Z, Wang Q. et al. Genome-wide association study of 12 agronomic traits in peach. Nat Commun. 2016;17: 1-10

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1] Joosen RVL, Ligterink W, Hilhorst HWM. et al. Advances in genet-ical genomics of plants. Curr Genomics. 2009; 10:540-9

[2] Prins P, Smant G, Jansen RC. Genetical genomics for evolutionary studies. Methods Mol Biol. 2012; 856:469-85

[3] Jung S, Lee T, Cheng CH. et al.15 years of GDR: new data and functionality in the genome database for Rosaceae. Nucleic Acids Res. 2019;47:D1137-45

[4] Salazar JA, Ruiz D, Campoy JA. et al. Quantitative trait loci (QTL) and Mendelian trait loci (MTL) analysis in Prunus: a breeding perspective and beyond. Plant Mol Biol Report. 2014; 32:1-18

[5] Cici SZH, Van Acker RC. Gene flow in Prunus species in the context of novel trait risk assessment. Environ Biosaf Res. 2010; 9: 75-85

[6] FAOSTAT.http://www.fao.org/faostat/es/#data/QC 2022(accessed on 28 May 2024).

[7] Cai L, Voorrips RE, van de Weg E. et al. Genetic structure of a QTL hotspot on chromosome 2 in sweet cherry indicates positive selection for favorable haplotypes. Mol Breed. 2017; 37:1-10

[8] Dirlewanger E, Boudehri K, Renaud C. et al. QTL anlysis of characters involved in fruit quality in Prunus persica and a related species and co-location with candidate genes in the European Project ISAFRUIT. 2009; 2 p

[9] Dirlewanger E, Cosson P, Boudehri K. et al. Development of a second-generation genetic linkage map for peach [Prunus persica (L.) Batsch] and characterization of morphological traits affecting flower and fruit. Tree Genet Genomes. 2006; 3:1-13

[10] Lambert P, Campoy JA, Pacheco I. et al. Identifying SNP markers tightly associated with six major genes in peach [Prunus persica (L.) Batsch] using a high-density SNP array with an objective of marker-assisted selection (MAS). Tree Genet Genomes. 2016; 12: 1-21

[11] da Silva Linge C, Cai L, Fu W. et al. Multi-locus genome-wide asso-ciation studies reveal fruit quality hotspots in peach genome. Front Plant Sci. 2021; 12:644799

[12] da Silva Linge C, Fu W, Calle A. et al. Ppe.RPT/SSC-1: from QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach. Sci Rep. 2024, 2024; 14:1-13

[13] Breitling R, Li Y, Tesson BM. et al. Genetical genomics: spotlight on QTL hotspots. PLoS Genet. 2008; 4:e1000232

[14] Wu PY, Yang MH, Kao CH. A statistical framework for QTL hotspot detection. G3 (Bethesda). 2021;11:jkab056

[15] Falconer DS, Mackay TFC. Introduction to Quantitative Genetics.4th ed.Addison Wesley Longman, Harlow, 1996:

[16] Dirlewanger E, Graziano E, Joobeur T. et al. Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proc Natl Acad Sci USA. 2004; 101:9891-6

[17] Dondini L, Lain O, Geuna F. et al. Development of a new SSR-based linkage map in apricot and analysis of syn-teny with existing Prunus maps. Tree Genet Genomes. 2007; 3: 239-49

[18] Olmstead JW, Sebolt AM, Cabrera A. et al. Construction of an intra-specific sweet cherry (Prunus avium L.) genetic linkage map and synteny analysis with the Prunus reference map. Tree Genet Genomes. 2008; 4:897-910

[19] Vera Ruiz EM, Soriano JM, Romero C. et al. Narrowing down the apricot plum pox virus resistance locus and comparative analysis with the peach genome syntenic region. Mol Plant Pathol. 2011; 12:535-47

[20] Aranzana MJ, Decroocq V, Dirlewanger E. et al. Prunus genetics and applications after de novo genome sequencing: achieve-ments and prospects. Hortic Res. 2019; 6:58

[21] Arunyawat U, Capdeville G, Decroocq V. et al. Linkage disequi-librium in French wild cherry germplasm and worldwide sweet cherry germplasm. Tree Genet Genomes. 2012; 8:737-55

[22] Arús P, Verde I, Sosinski B. et al. The peach genome. Tree Genet Genomes. 2012; 8:531-47

[23] Yoon J, Liu D, Song W. et al. Genetic diversity and ecogeographical phylogenetic relationships among peach and nectarine cultivars based on simple sequence repeat (SSR) markers. JAmSocHortic Sci. 2006; 131:513-21

[24] Verde I, Abbott AG, Scalabrin S. et al. The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet. 2013, 2013; 45:487-94

[25] Serra O, Giné-Bordonaba J, Eduardo I. et al. Genetic analysis of the slow-melting flesh character in peach. Tree Genet Genomes. 2017; 13:1-13

[26] Rawandoozi ZJ, Hartmann TP, Carpenedo S. et al. Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach. BMC Genomics. 2020; 21:1-18

[27] Dirlewanger E, Cosson P, Tavaud M. et al. Development of microsatellite markers in peach [Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium L.). Theor Appl Genet. 2002; 105:127-38

[28] Verde I, Jenkins J, Dondini L. et al. The Peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity. BMC Genomics. 2017; 18:1-18

[29] Howad W, Yamamoto T, Dirlewanger E. et al. Mapping with a few plants: using selective mapping for microsatellite saturation of the Prunus reference map. Genetics. 2005; 171:1305-9

[30] Pacheco I, Bassi D, Eduardo I. et al. QTL mapping for brown rot (Monilinia fructigena) resistance in an intraspecific peach (Prunus persica L. Batsch) F1 progeny. Tree Genet Genomes. 2014; 10: 1223-42

[31] da Silva Linge C, Bassi D, Bianco L. et al. Genetic dissection of fruit weight and size in an F2 peach (Prunus persica (L.) Batsch) progeny. Mol Breed. 2015; 35:1-19

[32] Calle A, Cai L, Iezzoni A. et al. Genetic dissection of bloom time in low chilling sweet cherry (Prunus avium L.) using a multi-family QTL approach. Front Plant Sci. 2020; 10:470689

[33] Crump WW, Peace C, Zhang Z. et al. Detection of breeding-relevant fruit cracking and fruit firmness quantitative trait loci in sweet cherry via pedigree-based and genome-wide associa-tion approaches. Front Plant Sci. 2022; 13:823250

[34] Fan S, Bielenberg DG, Zhebentyayeva TN. et al. Mapping quan-titative trait loci associated with chilling requirement, heat requirement and bloom date in peach (Prunus persica). New Phytol. 2010; 185:917-30

[35] Zhebentyayeva TN, Fan S, Chandra A. et al. Dissection of chilling requirement and bloom date QTLs in peach using a whole genome sequencing of sibling trees from an F2 mapping pop-ulation. Tree Genet Genomes. 2014; 10:35-51

[36] Bielenberg DG, Rauh B, Fan S. et al. Genotyping by sequencing for SNP-based linkage map construction and QTL analysis of chilling requirement and bloom date in peach [Prunus persica (L.) Batsch]. PLoS One. 2015; 10:e0139406

[37] Calle A, Serradilla MJ, Wünsch A. QTL mapping of phenolic compounds and fruit colour in sweet cherry using a 6+9K SNP array genetic map. Sci Hortic. 2021; 280:109900

[38] Rawandoozi ZJ, Hartmann TP, Carpenedo S. et al. Mapping and characterization QTLs for phenological traits in seven pedigree-connected peach families. BMC Genomics. 2021; 22:1-16

[39] Nuñez-Lillo G, Cifuentes-Esquivel A, Troggio M. et al. Identifica-tion of candidate genes associated with mealiness and maturity date in peach [Prunus persica (L.) Batsch] using QTL analysis and deep sequencing. Tree Genet Genomes. 2015; 11:1-13

[40] Foulongne M, Pascal T, Pfeiffer F. et al. QTLs for powdery mildew resistance in peach x Prunus davidiana crosses: consistency across generations and environments. Mol Breed. 2003; 12:33-50

[41] Yang N, Reighard G, Ritchie D. et al. Mapping quantitative trait loci associated with resistance to bacterial spot (Xan-thomonas arboricola pv. Pruni) in peach. Tree Genet Genomes. 2012; 9: 573-86

[42] Cai L, Stegmeir T, Sebolt A. et al. Identification of bloom date QTLs and haplotype analysis in tetraploid sour cherry (Prunus cerasus). Tree Genet Genomes. 2018; 14:1-11

[43] Yang MH, Wu DH, Kao CH. A statistical procedure for genome-wide detection of QTL hotspots using public databases with application to rice. G3 (Bethesda). 2019; 9:439-52

[44] Viruel MA, Madur D, Dirlewanger E. et al. Mapping quantita-tive trait loci controlling peach leaf curl resistance. Acta Hortic. 1998; 465:79-87

[45] Etienne C, Rothan C, Moing A. et al. Candidate genes and QTLs for sugar and organic acid content in peach [Prunus persica (L.) Batsch]. Theor Appl Genet. 2002; 105:145-59

[46] Testolin R, Marrazzo T, Cipriani G. et al. Microsatellite DNA in peach (Prunus persica L. Batsch) and its use in fingerprinting and testing the genetic origin of cultivars. Genome. 2011; 43:512-20

[47] Sosinski B, Gannavarapu M, Hager LD. et al. Characterization of microsatellite markers in peach [Prunus persica (L.) Batsch]. Theor Appl Genet. 2000; 101:421-8

[48] Yamamoto T, Mochida K, Imai T. et al. Microsatellite markers in peach [Prunus persica (L.) Batsch] derived from an enriched genomic and cDNA libraries. Mol Ecol Notes. 2002; 2:298-301

[49] Lopes GC, Bonissoni L, Baratieri LN. et al. Identification of microsatellite loci in apricot. Mol Ecol Notes. 2002; 2:24-6

[50] Vendramin E, Dettori MT, Giovinazzi J. et al. A set of EST-SSRs iso-lated from peach fruit transcriptome and their transportability across Prunus species. Mol Ecol Notes. 2007; 7:307-10

[51] Messina R, Lain O, Marrazzo MT. et al. New set of microsatellite loci isolated in apricot. Mol Ecol Notes. 2004; 4:432-4