Within-family genomic selection in rubber tree (
Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat marke...
Ausführliche Beschreibung
Autor*in: |
Cros, David [verfasserIn] Mbo-Nkoulou, Luther [verfasserIn] Bell, Joseph Martin [verfasserIn] Oum, Jean [verfasserIn] Masson, Aurélien [verfasserIn] Soumahoro, Mouman [verfasserIn] Tran, Dinh Minh [verfasserIn] Achour, Zeineb [verfasserIn] Le Guen, Vincent [verfasserIn] Clement-Demange, André [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Industrial crops and products - New York, NY [u.a.] : Elsevier, 1992, 138 |
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Übergeordnetes Werk: |
volume:138 |
DOI / URN: |
10.1016/j.indcrop.2019.111464 |
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Katalog-ID: |
ELV002736284 |
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520 | |a Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. | ||
650 | 4 | |a Marker assisted selection | |
650 | 4 | |a Genomic predictions | |
650 | 4 | |a Selection response | |
650 | 4 | |a Clonal varieties | |
700 | 1 | |a Mbo-Nkoulou, Luther |e verfasserin |4 aut | |
700 | 1 | |a Bell, Joseph Martin |e verfasserin |4 aut | |
700 | 1 | |a Oum, Jean |e verfasserin |4 aut | |
700 | 1 | |a Masson, Aurélien |e verfasserin |4 aut | |
700 | 1 | |a Soumahoro, Mouman |e verfasserin |4 aut | |
700 | 1 | |a Tran, Dinh Minh |e verfasserin |4 aut | |
700 | 1 | |a Achour, Zeineb |e verfasserin |4 aut | |
700 | 1 | |a Le Guen, Vincent |e verfasserin |0 (orcid)0000-0002-8791-9778 |4 aut | |
700 | 1 | |a Clement-Demange, André |e verfasserin |4 aut | |
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10.1016/j.indcrop.2019.111464 doi (DE-627)ELV002736284 (ELSEVIER)S0926-6690(19)30473-X DE-627 ger DE-627 rda eng 630 640 DE-600 48.30 bkl Cros, David verfasserin (orcid)0000-0002-8601-7991 aut Within-family genomic selection in rubber tree ( 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. Marker assisted selection Genomic predictions Selection response Clonal varieties Mbo-Nkoulou, Luther verfasserin aut Bell, Joseph Martin verfasserin aut Oum, Jean verfasserin aut Masson, Aurélien verfasserin aut Soumahoro, Mouman verfasserin aut Tran, Dinh Minh verfasserin aut Achour, Zeineb verfasserin aut Le Guen, Vincent verfasserin (orcid)0000-0002-8791-9778 aut Clement-Demange, André verfasserin aut Enthalten in Industrial crops and products New York, NY [u.a.] : Elsevier, 1992 138 Online-Ressource (DE-627)300894678 (DE-600)1483245-8 (DE-576)259270792 1872-633X nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.30 Natürliche Ressourcen Land- und Forstwirtschaft AR 138 |
spelling |
10.1016/j.indcrop.2019.111464 doi (DE-627)ELV002736284 (ELSEVIER)S0926-6690(19)30473-X DE-627 ger DE-627 rda eng 630 640 DE-600 48.30 bkl Cros, David verfasserin (orcid)0000-0002-8601-7991 aut Within-family genomic selection in rubber tree ( 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. Marker assisted selection Genomic predictions Selection response Clonal varieties Mbo-Nkoulou, Luther verfasserin aut Bell, Joseph Martin verfasserin aut Oum, Jean verfasserin aut Masson, Aurélien verfasserin aut Soumahoro, Mouman verfasserin aut Tran, Dinh Minh verfasserin aut Achour, Zeineb verfasserin aut Le Guen, Vincent verfasserin (orcid)0000-0002-8791-9778 aut Clement-Demange, André verfasserin aut Enthalten in Industrial crops and products New York, NY [u.a.] : Elsevier, 1992 138 Online-Ressource (DE-627)300894678 (DE-600)1483245-8 (DE-576)259270792 1872-633X nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.30 Natürliche Ressourcen Land- und Forstwirtschaft AR 138 |
allfields_unstemmed |
10.1016/j.indcrop.2019.111464 doi (DE-627)ELV002736284 (ELSEVIER)S0926-6690(19)30473-X DE-627 ger DE-627 rda eng 630 640 DE-600 48.30 bkl Cros, David verfasserin (orcid)0000-0002-8601-7991 aut Within-family genomic selection in rubber tree ( 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. Marker assisted selection Genomic predictions Selection response Clonal varieties Mbo-Nkoulou, Luther verfasserin aut Bell, Joseph Martin verfasserin aut Oum, Jean verfasserin aut Masson, Aurélien verfasserin aut Soumahoro, Mouman verfasserin aut Tran, Dinh Minh verfasserin aut Achour, Zeineb verfasserin aut Le Guen, Vincent verfasserin (orcid)0000-0002-8791-9778 aut Clement-Demange, André verfasserin aut Enthalten in Industrial crops and products New York, NY [u.a.] : Elsevier, 1992 138 Online-Ressource (DE-627)300894678 (DE-600)1483245-8 (DE-576)259270792 1872-633X nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.30 Natürliche Ressourcen Land- und Forstwirtschaft AR 138 |
allfieldsGer |
10.1016/j.indcrop.2019.111464 doi (DE-627)ELV002736284 (ELSEVIER)S0926-6690(19)30473-X DE-627 ger DE-627 rda eng 630 640 DE-600 48.30 bkl Cros, David verfasserin (orcid)0000-0002-8601-7991 aut Within-family genomic selection in rubber tree ( 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. Marker assisted selection Genomic predictions Selection response Clonal varieties Mbo-Nkoulou, Luther verfasserin aut Bell, Joseph Martin verfasserin aut Oum, Jean verfasserin aut Masson, Aurélien verfasserin aut Soumahoro, Mouman verfasserin aut Tran, Dinh Minh verfasserin aut Achour, Zeineb verfasserin aut Le Guen, Vincent verfasserin (orcid)0000-0002-8791-9778 aut Clement-Demange, André verfasserin aut Enthalten in Industrial crops and products New York, NY [u.a.] : Elsevier, 1992 138 Online-Ressource (DE-627)300894678 (DE-600)1483245-8 (DE-576)259270792 1872-633X nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.30 Natürliche Ressourcen Land- und Forstwirtschaft AR 138 |
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10.1016/j.indcrop.2019.111464 doi (DE-627)ELV002736284 (ELSEVIER)S0926-6690(19)30473-X DE-627 ger DE-627 rda eng 630 640 DE-600 48.30 bkl Cros, David verfasserin (orcid)0000-0002-8601-7991 aut Within-family genomic selection in rubber tree ( 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. Marker assisted selection Genomic predictions Selection response Clonal varieties Mbo-Nkoulou, Luther verfasserin aut Bell, Joseph Martin verfasserin aut Oum, Jean verfasserin aut Masson, Aurélien verfasserin aut Soumahoro, Mouman verfasserin aut Tran, Dinh Minh verfasserin aut Achour, Zeineb verfasserin aut Le Guen, Vincent verfasserin (orcid)0000-0002-8791-9778 aut Clement-Demange, André verfasserin aut Enthalten in Industrial crops and products New York, NY [u.a.] : Elsevier, 1992 138 Online-Ressource (DE-627)300894678 (DE-600)1483245-8 (DE-576)259270792 1872-633X nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.30 Natürliche Ressourcen Land- und Forstwirtschaft AR 138 |
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630 640 DE-600 48.30 bkl Within-family genomic selection in rubber tree ( Marker assisted selection Genomic predictions Selection response Clonal varieties |
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Within-family genomic selection in rubber tree ( |
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Within-family genomic selection in rubber tree ( |
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Cros, David Mbo-Nkoulou, Luther Bell, Joseph Martin Oum, Jean Masson, Aurélien Soumahoro, Mouman Tran, Dinh Minh Achour, Zeineb Le Guen, Vincent Clement-Demange, André |
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within-family genomic selection in rubber tree ( |
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Within-family genomic selection in rubber tree ( |
abstract |
Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. |
abstractGer |
Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. |
abstract_unstemmed |
Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required. |
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