QTL mapping of pomological traits in peach and related species breeding germplasm
- 935 Downloads
Peach is an economically important fruit tree crop that exhibits high phenotypic variability yet suffers from diversity-limited gene pool. Genetic introgression of novel alleles from related species is being pursued to expand genetic diversity. This process is, however, challenging and requires the incorporation of innovative genomic and statistical tools to facilitate efficient transfer of these exotic alleles across the multiple generations required for introgression. In this study, pedigree-based analysis (PBA) in a Bayesian QTL mapping framework was applied to a diverse peach pedigree introgressed with almond and other related Prunus species. The aim was to investigate the genetic control of eight commercially important fruit productivity and fruit quality traits over two subsequent years. Fifty-two QTLs with at least positive evidence explaining up to 98 % of the phenotypic variance across all trait/year combinations were mapped separately per trait and year. Several QTLs exhibited variable association with traits between years. By using the peach genome sequence as a reference, the intrachromosomal positions for several QTLs were shown to differ from those previously reported in peach. The inclusion of introgressed germplasm and the explicit declaration of the genetic structure of the pedigree as covariate in PBA enhanced the mapping and interpretation of QTLs. This study serves as a model study for PBA in a diverse peach breeding program, and the results highlight the ability of this strategy to identify genomic resources for direct utilization in marker-assisted breeding.
KeywordsPrunus persica (L.) Batsch Germplasm introgression Bayesian SNPs Pedigree correction Genetic structure
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 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 the University of California, Davis. The contributions of M.C.A.M. Bink & E.W. van de Weg were also supported by the EU-FruitBreedomics project funded by the Commission of the European Communities (Contract FP7-KBBE-2010-265582). Thanks to Dr. Pedro J. Martínez-García for his helpful comments about the organization of the manuscript. Special thanks for their valuable help during the phenotyping of the accessions to the field crew of the Processing Peach Breeding Program at UC Davis, supervised by Mary Ann Thorpe, Sabrina Marchand, Helen Chan and Rachel Williams. Likewise, thanks to Palma Lower, writing specialist at UC Davis, for her valuable comments and corrections during early drafts of the manuscript.
The genotypic and phenotypic datasets of the UC Davis pedigree-connected germplasm can be accessed through the Breeders Toolbox available at the Genome Database for Rosaceae (http://www.rosaceae.org/breeders_toolbox). The QTL information is accessible through the Trait Loci search tool at GDR. (http://www.rosaceae.org/search/qtl).
J.F.R. carried out the analyzes and drafted the manuscript, M.C.A.M.B. and E.V.W. provided support for implementation and performing of PBA as well as for the interpretation of the results, also helped in drafting the manuscript, T.R.F. helped to perform pedigree pruning and determination of genetic structure, C.H.C. provided support for phenotypic evaluation and analyzes, T.J.F., K.G. and C.P.P. developed the SNP genotyping and database for the peach set in RosBREED and helped in drafting the manuscript, and T.M.G. provided the genetic materials, coordinated the study and elaborated on manuscripts. All authors read and approved the final and reviewed manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that there are no conflicts of interest.
- 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
- Arús P, Yamamoto T, Dirlewanger E, Abbott AG (2010) Synteny in the Rosaceae. In: Plant breeding reviews. Wiley, New York, pp 175–211. doi: 10.1002/9780470650349.ch4
- Bink MCAM, Jansen J, Madduri M, Voorrips RE, Durel CE, Kouassi AB, Laurens F, Mathis F, Gessler C, Gobbin D, Rezzonico F, Patocchi A, Kellerhals M, Boudichevskaia A, Dunemann F, Peil A, Nowicka A, Lata B, Stankiewicz-Kosyl M, Jeziorek K, Pitera E, Soska A, Tomala K, Evans KM, Fernández-Fernández F, Guerra W, Korbin M, Keller S, Lewandowski M, Plocharski W, Rutkowski K, Zurawicz E, Costa F, Sansavini S, Tartarini S, Komjanc M, Mott D, Antofie A, Lateur M, Rondia A, Gianfranceschi L, van de Weg WE (2014) Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor Appl Genet 127(5):1073–1090. doi: 10.1007/s00122-014-2281-3 PubMedGoogle Scholar
- Boudehri K, Bendahmane A, Cardinet G, Troadec C, Moing A, Dirlewanger E (2009) Phenotypic and fine genetic characterization of the D locus controlling fruit acidity in peach. BMC Plant Biol 9 (Artn 59)Google Scholar
- Bulmer MG (1980) The mathematical theory of quantitative genetics. Oxford University Press, OxfordGoogle Scholar
- Coster A (2012) pedigree: pedigree functions, 1.4 edn. http://CRAN.R-project.org/package=pedigree
- De Franceschi P, Stegmeir T, Cabrera A, van der Knaap E, Rosyara UR, Sebolt AM, Dondini L, Dirlewanger E, Quero-Garcia J, Campoy JA, Iezzoni AF (2013) Cell number regulator genes in Prunus provide candidate genes for the control of fruit size in sweet and sour cherry. Mol Breed 32(2):311–326. doi: 10.1007/S11032-013-9872-6 PubMedCentralCrossRefPubMedGoogle Scholar
- Etienne C, Rothan C, Moing A, Plomion C, Bodenes C, Svanella-Dumas L, Cosson P, Pronier V, Monet R, Dirlewanger E (2002) Candidate genes and QTLs for sugar and organic acid content in peach [Prunus persica (L.) Batsch]. Theor Appl Genet 105(1):145–159. doi: 10.1007/S00122-001-0841-9 CrossRefPubMedGoogle Scholar
- Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, HarlowGoogle 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(4):917–930. doi: 10.1111/J.1469-8137.2009.03119.X CrossRefPubMedGoogle 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(4):214–219Google Scholar
- Gradziel TM (2002) Almond species as sources of new genes for peach improvement. Acta Hortic 592:81–88Google Scholar
- Gradziel TM (2003) Interspecific hybridizations and subsequent gene introgression within Prunus subgenus Amygdalus. Acta Hortic 622:249–255Google Scholar
- Gradziel TM, Weinbaum SA (1999) High relative humidity reduces anther dehiscence in apricot, peach, and almond. HortScience 34(2):322–325Google Scholar
- Gradziel TM, Beres W, Pelletreau K (1993) Inbreeding in California canning clingstone peach cultivars. Fruit Var J 47(3):160–168Google Scholar
- Iezzoni A (2010) RosBREED: enabling marker-assisted breeding in the Rosaceae. HortScience 45(8):S27–S28Google Scholar
- Iezzoni A, Weebadde C, Luby J, Yue CY, van de Weg WE, Fazio G, Main D, Peace CP, Bassil NV, McFerson J (2010) RosBREED: enabling marker-assisted breeding in Rosaceae. Acta Horticulturae 859:389–394Google Scholar
- Jung S, Ficklin SP, Lee T, Cheng C-H, Blenda A, Zheng P, Yu J, Bombarely A, Cho I, Ru S, Evans K, Peace C, Abbott AG, Mueller LA, Olmstead MA, Main D (2014) The genome database for Rosaceae (GDR): year 10 update. Nucleic Acids Res 42(D1):D1237–D1244. doi: 10.1093/nar/gkt1012 PubMedCentralCrossRefPubMedGoogle Scholar
- Le S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25(1):1–18Google Scholar
- Okie WR, Bacon T, Bassi D (2008) Fresh market cultivar development. In: Layne D, Bassi D (eds) The peach: botany, production and uses. CABI, Wallinford, pp 37–60Google Scholar
- Olukolu BA, Kole C (2012) Molecular mapping of complex traits. In: Kole C, Abbott AG (eds) Genetics, genomics and breeding of crop plants. CRC Press, Boca Raton, pp 126–157Google Scholar
- Peace C, Bassil NV, Bink M, Brown SK, Byrne DH, Clark JR, Davis TM, Evans K, Fazio G, Finn CE, Gasic K, Gradziel T, Hancock JF, Luby J, Main D, Oraguzie N, van de Weg E, Wang DC, Xu KN, Iezzoni A (2010) RosBREED’s marker-assisted breeding Pipeline. HortScience 45(8):S54–S54Google Scholar
- Pirona R, Eduardo I, Pacheco I, Linge CD, Miculan M, Verde I, Tartarini S, Dondini L, Pea G, Bassi D, Rossini L (2013) Fine mapping and identification of a candidate gene for a major locus controlling maturity date in peach. BMC Plant Biol 13 (Artn 166)Google Scholar
- R Development Core Team (2012) R: a language and environment for statistical computing, 2.15th edn. Development Core Team R, ViennaGoogle Scholar
- Rosyara UR, Bink MCAM, van de Weg E, Zhang GR, Wang DC, Sebolt A, Dirlewanger E, Quero-Garcia J, Schuster M, Iezzoni AF (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(4):875–887. doi: 10.1007/S11032-013-9916-Y CrossRefGoogle 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(4):547–552Google Scholar
- Sorensen D, Gianola D (2002) Likelihood, Bayesian and MCMC methods in quantitative genetics. Statistics for biology and health. Springer, New YorkGoogle Scholar
- Tanksley SD, Ganal MW, Prince JP, de Vicente MC, Bonierbale MW, Broun P, Fulton TM, Giovannoni JJ, Grandillo S, Martin GB, Messeguer R, Miller JC, Miller L, Paterson AH, Pineda O, Roder MS, Wing RA, Wu W, Young ND (1992) High density molecular linkage maps of the tomato and potato genomes. Genetics 132(4):1141–1160PubMedCentralPubMedGoogle Scholar
- van de Weg WE, Voorrips RE, Finkers HJ, Kodde LP, Meulenbroek EJ, Jansen J, Bink MCAM (2005) Pedigree genotyping: a new pedigree-based approach of QTL identification and allele mining by exploiting breeding material. Acta Hortic 708:483–488Google Scholar
- Verde I, Bassil N, Scalabrin S, Gilmore B, Lawley CT, Gasic K, Micheletti D, Rosyara UR, Cattonaro F, Vendramin E, Main D, Aramini V, Blas AL, Mockler TC, Bryant DW, Wilhelm L, Troggio M, Sosinski B, Aranzana MJ, Arus P, Iezzoni A, Morgante M, Peace C (2012) Development and evaluation of a 9K SNP array for peach by internationally coordinated SNP detection and validation in breeding germplasm. PlosOne 7(4) (ARTN e35668). http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0035668
- 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(1):25–36. doi: 10.1007/s11295-009-0225-x CrossRefGoogle Scholar