2016
DOI: 10.1016/j.plantsci.2015.08.006
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Genomic selection in maritime pine

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Cited by 103 publications
(131 citation statements)
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References 70 publications
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“…3). While similar results have been reported for animals [18,43] and crop species [9,36] across a number of traits, in forest trees prediction accuracies using genomic data have generally been similar or up to 10-30% lower than accuracies obtained using pedigree-estimated breeding values, including Eucalyptus [4], loblolly pine (Pinus taeda) [44], white spruce (Picea glauca) [45,46], interior spruce (Picea engelmannii × glauca) [47,48] and maritime pine (Pinus pinaster) [49]. Genomic predictions with lower accuracies than pedigree-based predictions could arise from insufficient marker density, such that not all casual variants are captured in the genomic estimate [41], or an overestimate of the pedigree-based prediction due to its inability of ascertaining the true genetic relationships in half-sib families [47].…”
Section: Genomic Data Corrected Pedigree Inconsistenciessupporting
confidence: 72%
See 1 more Smart Citation
“…3). While similar results have been reported for animals [18,43] and crop species [9,36] across a number of traits, in forest trees prediction accuracies using genomic data have generally been similar or up to 10-30% lower than accuracies obtained using pedigree-estimated breeding values, including Eucalyptus [4], loblolly pine (Pinus taeda) [44], white spruce (Picea glauca) [45,46], interior spruce (Picea engelmannii × glauca) [47,48] and maritime pine (Pinus pinaster) [49]. Genomic predictions with lower accuracies than pedigree-based predictions could arise from insufficient marker density, such that not all casual variants are captured in the genomic estimate [41], or an overestimate of the pedigree-based prediction due to its inability of ascertaining the true genetic relationships in half-sib families [47].…”
Section: Genomic Data Corrected Pedigree Inconsistenciessupporting
confidence: 72%
“…In loblolly pine, for example, the performance of rrBLUP and three Bayesian methods were only marginally different when compared across 17 traits with distinct heritabilities, with a small improvement seen for BayesA only for fusiform rust resistance where loci of relatively larger effect have been described [44]. Similar results were obtained for growth and wood traits in other forest trees showing no performance difference between rrBLUP and Bayesian methods [46,48,49]. This occurs despite simulation studies suggesting that Bayesian methods, like BL, should outperform univariate methods such as rrBLUP and GBLUP [6,52,53].…”
Section: Genomic Predictions Show That Traits Adequately Fit the Infimentioning
confidence: 54%
“…Genomic selection is being increasingly explored as a major tool for selection in forest tree breeding Isik et al 2011Isik et al , 2016Lexer and Stölting 2012;Ratcliffe et al 2015;Resende et al 2011Resende et al , 2012a. The major benefit of genomic selection is that selection can be undertaken well before the normal age of phenotypingaround age 8 in radiata pine.…”
Section: Genomics Will Help Tackle G×ementioning
confidence: 99%
“…1966). These 50 genotypes constitute the founders of a three-generation pedigreed population used to develop proof of concept for genomic selection in maritime pine (Isik et al 2015). The Portuguese population was also represented by two subsets of genotypes: (i) 19 trees sampled from two provenances in a provenance trial carried out at Mimizan (France: 44°20 0 N, 1°28 0 W).…”
Section: Populations Studied and Genetic Analysismentioning
confidence: 99%