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...
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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]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Marker assisted selection Genomic predictions Selection response Clonal varieties

Übergeordnetes Werk:	Enthalten in: Industrial crops and products - New York, NY [u.a.] : Elsevier, 1992, 138
Übergeordnetes Werk:	volume:138

DOI / URN:	10.1016/j.indcrop.2019.111464

Katalog-ID:	ELV002736284

Internformat


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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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912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
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912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
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936	b	k	\|a 48.30 \|j Natürliche Ressourcen \|x Land- und Forstwirtschaft
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allfields	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
allfieldsSound	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
language	English
source	Enthalten in Industrial crops and products 138 volume:138
sourceStr	Enthalten in Industrial crops and products 138 volume:138
format_phy_str_mv	Article
bklname	Natürliche Ressourcen
institution	findex.gbv.de
topic_facet	Marker assisted selection Genomic predictions Selection response Clonal varieties
dewey-raw	630
isfreeaccess_bool	false
container_title	Industrial crops and products
authorswithroles_txt_mv	Cros, David @@aut@@ Mbo-Nkoulou, Luther @@aut@@ Bell, Joseph Martin @@aut@@ Oum, Jean @@aut@@ Masson, Aurélien @@aut@@ Soumahoro, Mouman @@aut@@ Tran, Dinh Minh @@aut@@ Achour, Zeineb @@aut@@ Le Guen, Vincent @@aut@@ Clement-Demange, André @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	300894678
dewey-sort	3630
id	ELV002736284
language_de	englisch
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author	Cros, David
spellingShingle	Cros, David ddc 630 bkl 48.30 misc Marker assisted selection misc Genomic predictions misc Selection response misc Clonal varieties Within-family genomic selection in rubber tree (
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topic_title	630 640 DE-600 48.30 bkl Within-family genomic selection in rubber tree ( Marker assisted selection Genomic predictions Selection response Clonal varieties
topic	ddc 630 bkl 48.30 misc Marker assisted selection misc Genomic predictions misc Selection response misc Clonal varieties
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title	Within-family genomic selection in rubber tree (
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title_full	Within-family genomic selection in rubber tree (
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author_browse	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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title_sort	within-family genomic selection in rubber tree (
title_auth	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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title_short	Within-family genomic selection in rubber tree (
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up_date	2024-07-06T17:16:41.019Z
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Within-family genomic selection in rubber tree (

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