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| Registros recuperados : 329 | |
| Registros recuperados : 329 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Florestas. |
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Data corrente: |
17/09/2018 |
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Data da última atualização: |
17/09/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
RESENDE, R. T.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; MELO, L. C.; PEREIRA, H. S.; SOUZA, T. L. P. O. de; VALDISSER, P. A. M. R.; BRONDANI, C.; VIANELLO, R. P. |
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Afiliação: |
Rafael T. Resende, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Camila F. Azevedo, UFV; Fabyano Fonseca e Silva, UFV; LEONARDO CUNHA MELO, CNPAF; HELTON SANTOS PEREIRA, CNPAF; THIAGO LIVIO PESSOA OLIV DE SOUZA, CNPAF; PAULA ARIELLE M RIBEIRO VALDISSER, CNPAF; CLAUDIO BRONDANI, CNPAF; ROSANA PEREIRA VIANELLO, CNPAF. |
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Título: |
Genome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 8, p. 2841-2854, Aug. 2018. |
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Idioma: |
Inglês |
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Conteúdo: |
The availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection. MenosThe availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare alle... Mostrar Tudo |
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Palavras-Chave: |
DArTseq; GWAS QTL; Herdabilidade; RHM QTL. |
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Thesagro: |
Feijão; Melhoramento Genético Vegetal; Phaseolus Vulgaris. |
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Thesaurus NAL: |
Beans; Heritability; Lodging resistance; Plant architecture; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/183088/1/2018-M.Deon-G3-Genome-wide.pdf
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Marc: |
LEADER 02867naa a2200373 a 4500
001 2095838
005 2018-09-17
008 2018 bl uuuu u00u1 u #d
100 1 $aRESENDE, R. T.
245 $aGenome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris.$h[electronic resource]
260 $c2018
520 $aThe availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection.
650 $aBeans
650 $aHeritability
650 $aLodging resistance
650 $aPlant architecture
650 $aPlant breeding
650 $aFeijão
650 $aMelhoramento Genético Vegetal
650 $aPhaseolus Vulgaris
653 $aDArTseq
653 $aGWAS QTL
653 $aHerdabilidade
653 $aRHM QTL
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aSILVA, F. F. e
700 1 $aMELO, L. C.
700 1 $aPEREIRA, H. S.
700 1 $aSOUZA, T. L. P. O. de
700 1 $aVALDISSER, P. A. M. R.
700 1 $aBRONDANI, C.
700 1 $aVIANELLO, R. P.
773 $tG3: Genes, Genomes, Genetics$gv. 8, p. 2841-2854, Aug. 2018.
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Registro original: |
Embrapa Florestas (CNPF) |
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