Skip to main content
SpringerLink
Log in

QTL mapping and breeding value estimation through pedigree-based analysis of fruit size and weight in four diverse peach breeding programs

  • Original Article
  • Published:
Tree Genetics & Genomes Aims and scope Submit manuscript

Abstract

The narrow genetic base of peach (Prunus persica L. Batsch) challenges efforts to accurately dissect the genetic architecture of complex traits. Standardized phenotypic assessment of pedigree-linked breeding germplasm and new molecular strategies and analytical approaches developed and conducted during the RosBREED project for enabling marker-assisted breeding (MAB) in Rosaceae crops has overcome several aspects of this challenge. The genetic underpinnings of fruit size (fruit equatorial diameter (FD)) and weight (fresh weight (FW)), two most important components of yield, were investigated using the pedigree-based analysis (PBA) approach under a Bayesian framework which has emerged as an alternative strategy to study the genetics of quantitative traits within diverse breeding germplasm across breeding programs. In this study, a complex pedigree with the common founder “Orange Cling” was identified and FD and FW data from 2011 and 2012 analyzed. A genetic model including genetic additive and dominance effects was considered, and its robustness was evaluated by using various prior and initial values in the Markov chain Monte Carlo procedure. Five QTLs were identified which accounted for up to 29 and 17 % of the phenotypic variation for FD and FW, respectively. Additionally, genomic breeding values were obtained for both traits, with accuracies >85 %. This approach serves as a model study for performing PBA across diverse pedigrees. By incorporating multiple breeding programs, the method and results presented support and highlight the ability of this strategy to identify genomic resources as targets for DNA marker development and subsequent MAB within each program.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abascal E, García Lautre I, Landaluce MI (2006) Multiple factor analysis of mixed tables of metric and categorical data. In: Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences. Chapman and Hall/CRC, pp 351–367. doi:10.1201/9781420011319.ch15

  • American Pomological Society, Ragan WH (1899) Revised catalogue of fruits recommended for cultivation in the various sections of the United States and the British provinces by the American pomological society. U.S. Dept. of agriculture. Division of Pomology. Bulletin ;no. 8, vol 63 p. Govt. print. off., Washington

  • Aranzana MJ, Abbassi EK, Howad W, Arus P (2010) Genetic variation, population structure and linkage disequilibrium in peach commercial varieties. BMC Genet 11:69. doi:10.1186/1471-2156-11-69

    Article  PubMed  PubMed Central  Google Scholar 

  • Bai XF, Wu B, Xing YZ (2012) Yield-related QTLs and their applications in rice genetic improvement. J Integr Plant Biol 54:300–311. doi:10.1111/J.1744-7909.2012.01117.X

    Article  PubMed  Google Scholar 

  • Bink MCAM, Uimari P, Sillanpaa MJ, Janss LLG, Jansen RC (2002) Multiple QTL mapping in related plant populations via a pedigree-analysis approach. Theor Appl Genet 104:751–762. doi:10.1007/s00122-001-0796-x

    Article  CAS  PubMed  Google Scholar 

  • Bink MCAM, Boer MP, Braak CJF, Jansen J, Voorrips RE, de Weg WE (2008) Bayesian analysis of complex traits in pedigreed plant populations. Euphytica 161:85–96

    Article  Google Scholar 

  • Bink MCAM, Totir LR, ter Braak CJF, Winkler CR, Boer MP, Smith OS (2012) QTL linkage analysis of connected populations using ancestral marker and pedigree information. Theor Appl Genet 124:1097–1113. doi:10.1007/S00122-011-1772-8

    Article  PubMed  PubMed Central  Google Scholar 

  • Bink MCAM et al (2014) Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor Appl Genet 127:1073–1090. doi:10.1007/s00122-014-2281-3

    CAS  PubMed  Google Scholar 

  • Byrne DH, Bassols-Raseira M, Bassi D, Piagnani MC, Gasic K, Reighard GL, Moreno MA, Pérez S (2012) Peach. In: Badenes ML, Byrne DH (eds) Handbook of plant breeding, vol 5. Springer, New York, pp 505–569

    Google Scholar 

  • Cabrera A, Kozik A, Howad W, Arus P, Iezzoni AF, van der Knaap E (2009) Development and bin mapping of a Rosaceae conserved ortholog set (COS) of markers. BMC Genomics 10 doi:Artn 562. doi:10.1186/1471-2164-10-562

  • Calboli FCF, Sampson J, Fretwell N, Balding DJ (2008) Population structure and inbreeding from pedigree analysis of purebred dogs. Genetics 179:593–601. doi:10.1534/Genetics.107.084954

    Article  PubMed  PubMed Central  Google Scholar 

  • da Silva Linge C, Bassi D, Bianco L, Pacheco I, Pirona R, Rossini L (2015) Genetic dissection of fruit weight and size in an F2 peach (Prunus persica (L.) Batsch) progeny. Mol Breed 35:1–19. doi:10.1007/s11032-015-0271-z

    Article  Google Scholar 

  • De Franceschi P et al (2013) Cell number regulator genes in Prunus provide candidate genes for the control of fruit size in sweet and sour cherry. Mol Breed 32:311–326. doi:10.1007/S11032-013-9872-6

    Article  PubMed  PubMed Central  Google Scholar 

  • de Souza VAB, Byrne DH, Taylor JF (1998a) Heritability, genetic and phenotypic correlations, and predicted selection response of quantitative traits in peach: I. An analysis of several reproductive traits. J Am Soc Hortic Sci 123 (4):598–603

  • de Souza VAB, Byrne DH, Taylor JF (1998b) Heritability, genetic and phenotypic correlations, and predicted selection response of quantitative traits in peach: II. An analysis of several fruit traits. J Am Soc Hortic Sci 123 (4):604–611

  • de Souza VAB, Byrne DH, Taylor JF (2000) Predicted breeding values for nine plant and fruit characteristics of 28 peach genotypes. J Am Soc Hortic Sci Science 125 (4):460–465

  • DeStefano AL, Hoeschele I (1992) Utilization of dominance variance through mate allocation strategies. J Dairy Sci 75:1680–1690. doi:10.3168/jds.S0022-0302(92)77925-9

    Article  Google Scholar 

  • Eduardo I, Pacheco I, Chietera G, Bassi D, Pozzi C, Vecchietti A, Rossini L (2011) QTL analysis of fruit quality traits in two peach intraspecific populations and importance of maturity date pleiotropic effect. Tree Genet Genomes 7:323–335. doi:10.1007/S11295-010-0334-6

    Article  Google Scholar 

  • Esch E, Horn R (2008) Variability of recombination rates in higher plants. In: Lüttge U, Beyschlag W, Murata J (eds) Progress in botany, vol 69, Progress in botany. Springer, Berlin, pp 37–60. doi:10.1007/978-3-540-72954-9_2

    Chapter  Google Scholar 

  • Etienne C et al (2002) Candidate genes and QTLs for sugar and organic acid content in peach [Prunus persica (L.) Batsch]. Theor Appl Genet 105:145–159. doi:10.1007/S00122-001-0841-9

    Article  CAS  PubMed  Google Scholar 

  • Fan S, Bielenberg DG, Zhebentyayeva TN, Reighard GL, Okie WR, Holland D, Abbott AG (2010) Mapping quantitative trait loci associated with chilling requirement, heat requirement and bloom date in peach (Prunus persica). New Phytol 185:917–930. doi:10.1111/J.1469-8137.2009.03119.X

    Article  PubMed  Google Scholar 

  • Fanizza G, Lamaj F, Costantini L, Chaabane R, Grando MS (2005) QTL analysis for fruit yield components in table grapes (Vitis vinifera). Theor Appl Genet 111:658–664. doi:10.1007/S00122-005-2016-6

    Article  CAS  PubMed  Google Scholar 

  • Fernández i Martí A, Font i Forcada C, Socias i Company R (2013) Genetic analysis for physical nut traits in almond. Tree Genet Genomes 9:455–465. doi:10.1007/S11295-012-0566-8

  • Font i Forcada C, Oraguzie N, Igartua E, Moreno MÁ, Gogorcena Y (2012) Population structure and marker–trait associations for pomological traits in peach and nectarine cultivars. Tree Genet Genomes 9:331–349. doi:10.1007/s11295-012-0553-0

    Article  Google Scholar 

  • Fresnedo-Ramírez J, Crisosto CH, Gradziel TM, Famula TR (2015a) Pedigree correction and estimation of breeding values for peach genetic improvement. Acta Hortic 1084:249–256. doi:10.17660/ActaHortic.2015.1084.35

    Article  Google Scholar 

  • Fresnedo-Ramírez J, Bink MCAM, van de Weg E, Famula TR, Crisosto CH, Frett TJ, Gasic K, Peace CP, Gradziel TM (2015b) QTL mapping of pomological traits in peach and related species breeding germplasm. Mol Breed 35(8):166. doi:10.1007/s11032-015-0357-7

    Article  Google Scholar 

  • Frett TJ, Gasic K, Clark JR, Byrne D, Gradziel T, Crisosto C (2012) Standardized phenotyping for fruit quality in peach [Prunus persica (L.) Batsch]. J Am Pomol Soc 66:214–219

    Google Scholar 

  • Gradziel TM (2012) Traditional genetics and breeding. In: Kole C, Abbott AG (eds) Genetics, genomics and breeding of crop plants. CRC Press, Boca Raton, pp 22–54

    Google Scholar 

  • Gradziel TM, Beres W, Pelletreau K (1993) Inbreeding in California canning clingstone peach cultivars. Fruit Varieties J 47:160–168

    Google Scholar 

  • Guo M, Simmons CR (2011) Cell number counts—the fw2.2 and CNR genes and implications for controlling plant fruit and organ size. Plant Sci 181:1–7. doi:10.1016/J.Plantsci.2011.03.010

    Article  CAS  PubMed  Google Scholar 

  • Hallander J, Waldmann P (2007) The effect of non-additive genetic interactions on selection in multi-locus genetic models. Heredity 98:349–359. doi:10.1038/Sj.Hdy.6800946

    CAS  PubMed  Google Scholar 

  • Hallander J, Waldmann P (2009) Optimum contribution selection in large general tree breeding populations with an application to Scots pine. Theor Appl Genet 118:1133–1142. doi:10.1007/S00122-009-0968-7

    Article  PubMed  Google Scholar 

  • Hansche PE (1986a) Heritability of juvenility in peach. HortScience 21:1197–1198

    Google Scholar 

  • Hansche PE (1986b) Heritability of fruit-quality traits in peach and nectarine breeding stocks dwarfed by the Dw gene. HortScience 21:1193–1195

    Google Scholar 

  • Hansche PE (1988) Two genes that induce brachytic dwarfism in peach. HortScience 23:604–606

    Google Scholar 

  • Hansche PE, Boynton B (1986) Heritability of enzymatic browning in peaches. HortScience 21:1195–1197

    CAS  Google Scholar 

  • Hansche PE, Beres V, Hesse CO (1972) Estimates of genetic and environmental effects on several traits in peach. J Am Soc Hortic Sci 97:76

    Google Scholar 

  • Hedrick UP, Howe GH, Taylor OM, Tubergen CB, New York State Agricultural Experiment Station., New York (U.S.: state). Department of Agriculture. (1917) The peaches of New York : report of the New York Agricultural Experiment Station for the year 1916. Annual Report / New York, U.S.: state, Department of Agriculture, vol 24 2 pt II. J.B. Lyon Co. printers, Albany

  • Howad W et al (2005) Mapping with a few plants: using selective mapping for microsatellite saturation of the Prunus reference map. Genetics 171:1305–1309. doi:10.1534/Genetics.105.043661

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Iezzoni A (2010) RosBREED: enabling marker-assisted breeding in the Rosaceae. HortScience 45:S27–S28

    Google Scholar 

  • Iezzoni A, Peace C, Bassil N, Coe M, Finn C, Gasic K, Luby JJ, Main D, McFerson J, Norelli J, Olmstead M, Whitaker VM, Chengyan Y (2015) RosBREED: Combining disease resistance with horticultural quality in new Rosaceous cultivars. XXIII Plant and Animal Genome Conference, San Diego 2015

  • Jannink JL, Bink MCAM, Jansen RC (2001) Using complex plant pedigrees to map valuable genes. Trends Plant Sci 6:337–342

    Article  CAS  PubMed  Google Scholar 

  • Jelenkovic G, Harrington E (1972) Morphology of the pachytene chromosomes in Prunus persica. Can J Genet Cytol 14:317–324. doi:10.1139/g72-039

    Article  Google Scholar 

  • Jung S, Staton M, Lee T, Blenda A, Svancara R, Abbott A, Main D (2008) GDR (Genome Database for Rosaceae): integrated web-database for Rosaceae genomics and genetics data. Nucleic Acids Res 36:D1034–D1040. doi:10.1093/nar/gkm803

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jung S et al (2014) The Genome Database for Rosaceae (GDR): year 10 update. Nucleic Acids Res 42:D1237–D1244. doi:10.1093/nar/gkt1012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kass RE, Raftery AE (1995) Bayes Factors. J Am Stat Assoc 90:773–795. doi:10.2307/2291091

    Article  Google Scholar 

  • Kinghorn B (1987) On computing strategies for mate allocation. J Anim Breed Genet 104:12–22. doi:10.1111/j.1439-0388.1987.tb00104.x

    Article  Google Scholar 

  • Knox MR, Ellis THN (2002) Excess heterozygosity contributes to genetic map expansion in pea recombinant inbred populations. Genetics 162:861–873

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lacape JM et al (2013) Mapping QTLs for traits related to phenology, morphology and yield components in an inter-specific Gossypium hirsutum x G. barbadense cotton RIL population. Field Crop Res 144:256–267. doi:10.1016/J.Fcr.2013.01.001

    Article  Google Scholar 

  • Le S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25:1–18

    Article  Google Scholar 

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

  • Lippman Z, Tanksley SD (2001) Dissecting the genetic pathway to extreme fruit size in tomato using a cross between the small-fruited wild species Lycopersicon pimpinellifolium and L. esculentum var. giant heirloom. Genetics 158:413–422

    CAS  PubMed  PubMed Central  Google Scholar 

  • Nishio M, Satoh M (2014) Including dominance effects in the genomic BLUP method for genomic evaluation. Plos One 9:e85792. doi:10.1371/journal.pone.0085792

    Article  PubMed  PubMed Central  Google Scholar 

  • Olmstead JW, Lezzoni AF, Whiting MD (2007) Genotypic differences in sweet cherry fruit size are primarily a function of cell number. J Am Soc Hortic Sci 132:697–703

    Google Scholar 

  • Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD (1988) Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335:721–726. doi:10.1038/335721a0

    Article  CAS  PubMed  Google Scholar 

  • Pea G, Aung HH, Frascaroli E, Landi P, Pe ME (2013) Extensive genomic characterization of a set of near-isogenic lines for heterotic QTL in maize (Zea mays L.). BMC Genomics 14:61

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peace CP, Crisosto CH, Gradziel TM (2005) Endopolygalacturonase: a candidate gene for freestone and melting flesh in peach. Mol Breed 16(1):21–31. doi:10.1007/s11032-005-0828-3

    Article  CAS  Google Scholar 

  • Peace CP, Luby JJ, van de Weg WE, Bink MCAM, Iezzoni AF (2014) A strategy for developing representative germplasm sets for systematic QTL validation, demonstrated for apple, peach, and sweet cherry. Tree Genet Genomes 10(6):1679–1694. doi:10.1007/s11295-014-0788-z

    Article  Google Scholar 

  • Peng B et al (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320. doi:10.1007/S00122-011-1532-9

    Article  PubMed  Google Scholar 

  • Portis E et al (2014) QTL mapping in eggplant reveals clusters of yield-related loci and orthology with the tomato genome. Plos One 9:e89499. doi:10.1371/journal.pone.0089499

    Article  PubMed  PubMed Central  Google Scholar 

  • Quarrie SA et al (2006) Dissecting a wheat QTL for yield present in a range of environments: from the QTL to candidate genes. J Exp Bot 57:2627–2637. doi:10.1093/Jxb/Erl026

    Article  CAS  PubMed  Google Scholar 

  • Quilot B, Wu BH, Kervella J, Genard M, Foulongne M, Moreau K (2004) QTL analysis of quality traits in an advanced backcross between Prunus persica cultivars and the wild relative species P. davidiana. Theor Appl Genet 109:884–897. doi:10.1007/S00122-004-1703-Z

    Article  CAS  PubMed  Google Scholar 

  • R Core Team (2013) R: a language and environment for statistical computing, 3.0.1 edn. R Foundation for Statistical Computing, Vienna, Austria

  • Rosyara UR et al (2013) Fruit size QTL identification and the prediction of parental QTL genotypes and breeding values in multiple pedigreed populations of sweet cherry. Mol Breed 32:875–887. doi:10.1007/S11032-013-9916-Y

    Article  Google Scholar 

  • Scorza R, Sherman BW (1996) Peaches. In: Janick J, Moore JN (eds) Fruit breeding. Wiley, New York, pp 325–440

    Google Scholar 

  • Scorza R, Mehlenbacher SA, Lightner GW (1985) Inbreeding and coancestry of freestone peach cultivars of the eastern United States and implications for peach germplasm improvement. J Am Soc Hortic Sci 110:547–552

    Google Scholar 

  • Semel Y et al (2006) Overdominant quantitative trait loci for yield and fitness in tomato. Proc Natl Acad Sci U S A 103:12981–12986. doi:10.1073/Pnas.0604635103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shi J et al (2009) Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics 182:851–861. doi:10.1534/genetics.109.101642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shulaev V et al (2008) Multiple models for Rosaceae genomics. Plant Physiol 147:985–1003. doi:10.1104/pp.107.115618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sorensen D, Gianola D (2002) Likelihood, Bayesian and MCMC methods in quantitative genetics. Statistics for biology and health. Springer-Verlag, New York

    Book  Google Scholar 

  • Therneau T, Atkinson E, Sinnwell J, Schaid D, McDonnell S (2013) kinship2: Pedigree functions, 1.54 edn. http://CRAN.R-project.org/package=pedigree

  • van de Weg WE, Voorrips RE, Finkers R, Kodde LP, Jansen J, Bink MCAM (2004) Pedigree genotyping: a new approach of QTL identification and allele mining by exploiting breeding material. Acta Horticult 663:45–50

    Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S. Statistics and computing, 4th edn. Springer, New York

    Book  Google Scholar 

  • Verde I, Abbott AG, Scalabrin S, Jung S, Shu S, Marroni F, Zhebentyayeva T, Dettori MT, Grimwood J, Cattonaro F, Zuccolo A, Rossini L, Jenkins J, Vendramin E, Meisel LA, Decroocq V, Sosinski B, Prochnik S, Mitros T, Policriti A, Cipriani G, Dondini L, Ficklin S, Goodstein DM, Xuan P, Fabbro CD, Aramini V, Copetti D, Gonzalez S, Horner DS, Falchi R, Lucas S, Mica E, Maldonado J, Lazzari B, Bielenberg D, Pirona R, Miculan M, Barakat A, Testolin R, Stella A, Tartarini S, Tonutti P, Arus P, Orellana A, Wells C, Main D, Vizzotto G, Silva H, Salamini F, Schmutz J, Morgante M, Rokhsar DS (2013) The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet 45:487–U447. doi:10.1038/ng.2586

  • Verde I et al. (2012a) Development and Evaluation of a 9K SNP Array for Peach by Internationally Coordinated SNP Detection and Validation in Breeding Germplasm. Plos One 7 doi:ARTN e35668. doi:10.1371/journal.pone.0035668

  • Verde I, Gazza L, Aramini V, Taller DL, Dettori MT (2012b) A second generation peach linkage map using IPSC 9K SNP chip for advanced QTL identification. Paper presented at the RGC6 6th Rosaceous Genomics Conference, San Michele, Italy, June 2014 http://www.fruitbreedomics.com/index.php/the-results/posters/55-rgc6-6th-rosaceous-genomics-conference-2012-san-michele/77-verde-i-et-al-2012-a-second-generation-peach-linkage-map-using-the-ipsc-9k-chip-for-advanced-qtl-identification

  • Voorrips RE, Bink MCAM, van de Weg WE (2012) Pedimap: software for the visualization of genetic and phenotypic data in pedigrees. J Hered 103:903–907. doi:10.1093/Jhered/Ess060

    Article  PubMed  PubMed Central  Google Scholar 

  • Waldmann P, Hallander J, Hoti F, Sillanpaa MJ (2008) Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. Genetics 179:1101–1112. doi:10.1534/Genetics.107.084160

    Article  PubMed  PubMed Central  Google Scholar 

  • Wricke GN, Weber E (1986) Quantitative genetics and selection in plant breeding. W. de Gruyter, Berlin

    Book  Google Scholar 

  • Yamamoto T et al (2001) Characterization of morphological traits based on a genetic linkage map in peach. Breed Sci 51:271–278. doi:10.1270/jsbbs.51.271

    Article  CAS  Google Scholar 

  • Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17:155–160. doi:10.1016/j.copbio.2006.02.003

    Article  CAS  PubMed  Google Scholar 

  • Zdravkovic J, Zivoslav M, Mirjana M, Bogoljub Z, Milan Z (2000) Epistatic gene effects on the yield of the parents of F-1, F-2, BC1 and BC2 progeny. Acta Physiol Plant 22:261–265. doi:10.1007/S11738-000-0027-0

    Article  Google Scholar 

  • Zhang GR, Sebolt AM, Sooriyapathirana SS, Wang DC, Bink MCAM, Olmstead JW, Iezzoni AF (2010) Fruit size QTL analysis of an F1 population derived from a cross between a domesticated sweet cherry cultivar and a wild forest sweet cherry. Tree Genet Genomes 6:25–36. doi:10.1007/s11295-009-0225-x

    Article  CAS  Google Scholar 

  • Zhebentyayeva TN et al (2008) A framework physical map for peach, a model Rosaceae species. Tree Genet Genomes 4:745–756. doi:10.1007/S11295-008-0147-Z

    Article  Google Scholar 

Download references

Acknowledgments

This study was funded by USDA’s National Institute of Food and Agriculture— Specialty Crop Research Initiative project, “RosBREED: enabling marker-assisted breeding in Rosaceae” (2009-51181-05808).

Jonathan Fresnedo Ramírez (the lead author) was supported by a CONACYT-UCMEXUS (Mexican Council of Science and Technology and University of California Institute for Mexico and the United States) doctoral fellowship at University of California, Davis.

Special thanks are given to Palma Lower, writing specialist at UC Davis, for her valuable comments and corrections during early stages of redaction of the manuscript.

Author information

Author notes

  1. Jonathan Fresnedo-Ramírez

    Present address: BRC Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853, USA

Authors and Affiliations

  1. Department of Plant Sciences, University of California, Davis, CA, 95616, USA

    Jonathan Fresnedo-Ramírez, Carlos H. Crisosto & Thomas M. Gradziel

  2. Department of Horticulture, University of Arkansas, Fayetteville, AR, 72701, USA

    Terrence J. Frett, Paul J. Sandefur, Alejandra Salgado-Rojas & John R. Clark

  3. Department of Agricultural and Environmental Sciences, Clemson University, Clemson, SC, 29634, USA

    Terrence J. Frett & Ksenija Gasic

  4. Department of Horticulture, Washington State University, Pullman, WA, 99164, USA

    Paul J. Sandefur & Cameron P. Peace

  5. Texas A&M University, College Station, TX, 77843, USA

    Natalie Anderson, Timothy P. Hartmann & David H. Byrne

  6. Biometris, Wageningen University and Research Centre, P.O. Box 16, 6700 AA, Wageningen, The Netherlands

    Marco C. A. M. Bink

  7. Wageningen UR Plant Breeding, Wageningen University and Research Centre, PO Box 386, 6700 AJ, Wageningen, The Netherlands

    Eric van de Weg

Authors
  1. Jonathan Fresnedo-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terrence J. Frett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul J. Sandefur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alejandra Salgado-Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John R. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ksenija Gasic
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cameron P. Peace
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Natalie Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Timothy P. Hartmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. David H. Byrne
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marco C. A. M. Bink
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eric van de Weg
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Carlos H. Crisosto
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Thomas M. Gradziel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas M. Gradziel.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest.

Authors’ contributions

JFR phenotyped the accessions from UC Davis, carried out the analysis, and led the drafting of the manuscript. KG provided initial marker analysis IPSC 9K peach SNP array. TJF, PJS, and ASR helped in the selection of markers; phenotyped the selections from Clemson University, University of Arkansas; and drafted the manuscript. NA and TPH phenotyped the selections from Texas A&M University. MCAMB and EVW provided support for implementation and performing of PBA as well as for the interpretation of the results. CHC provided support for phenotypic evaluation and analyses. DHB, JRC, KG, and TMG provided the genetic materials and helped draft the manuscript. TMG coordinated the study and elaborated on the manuscript. All authors read and approved the final and reviewed manuscript.

Data archiving statement

The QTL data and genomic breeding values (GBVs) reported in this manuscript will be made publicly available through the Genome Database for Rosaceae (www.rosaceae.org).

Additional information

Communicated by C. Dardick

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

Number of progenies, maximum generation coefficient and contributing founders from each peach breeding program. (PDF 107 kb)

Online Resource 2

Polynomial equations and supplemental figures and table. (PDF 396 kb)

Online Resource 3

Genomic breeding values (GBVs) per accession per trait per year, and additional statistics. (PDF 265 kb)

Online Resource 4

Black and white version of Table 5. (PDF 175 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fresnedo-Ramírez, J., Frett, T.J., Sandefur, P.J. et al. QTL mapping and breeding value estimation through pedigree-based analysis of fruit size and weight in four diverse peach breeding programs. Tree Genetics & Genomes 12, 25 (2016). https://doi.org/10.1007/s11295-016-0985-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11295-016-0985-z

Keywords

Search

Navigation