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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
21/08/2023 |
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Data da última atualização: |
21/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARVALHO, H. F.; FERRÃO, L. F. V.; GALLI, G.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR., M. F. R. de; FRITSCHE-NETO, R.; GUERREIRO-FILHO, O. |
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Afiliação: |
HUMBERTO FANELLI CARVALHO, INSTITUTO AGRONÔMICO DE CAMPINAS; LUÍS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORIDA; GIOVANNI GALLI, LOUISIANA STATE UNIVERSITY; JULIANA VIEIRA ALMEIDA NONATO, INSTITUTO AGRONÔMICO DE CAMPINAS; LILIAN PADILHA, CNPCa; MIRIAN PEREZ MALUF, CNPCa; MÁRCIO FERNANDO RIBEIRO DE RESENDE JR., UNIVERSITY OF FLORIDA; ROBERTO FRITSCHE-NETO, LOUISIANA STATE UNIVERSITY; OLIVEIRO GUERREIRO-FILHO, INSTITUTO AGRONÔMICO DE CAMPINAS. |
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Título: |
On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 19, n. 1, 2023. |
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Páginas: |
10 p. |
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DOI: |
https://doi.org/10.1007/s11295-022-01581-8 |
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Idioma: |
Inglês |
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Conteúdo: |
Obtaining resistance cultivars for leaf miner and leaf rust are the main important strategy of Brazil?s national coffee breeding program. The narrow genetic basis, and founder effect consequences, lead to challenges in quantifying and detecting genetic diversity for these traits. Biotechnology tools allied with classical breeding strategies are powerful in detecting variability and deploying a precision selection. The selection based on the genetic merit of an individual obtained from thousands of single nucleotide polymorphism effects is known as genomic selection. The ordinal scale principally makes the resistance evaluation of the leaf rust and leaf miner of the score, categorizing the phenotypes following the discrete (ordinal) distribution. Hence, this distribution can be better analyzed by threshold models. Our goals were to optimize genomic prediction models for coffee resistance to leaf rust and leaf miner via threshold models and compare pedigree and genomic relationship matrices to underlying prediction models. We have observed that the genomic model with the genomic relationship matrix performed better for all scenarios. For the traits with at least five degrees of scores, the threshold models performed better, whereas for a trait with ten degrees of scores, we see no advantage to using a threshold model for genomic prediction. |
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Thesaurus Nal: |
Coffea arabica var. arabica; Genomics; Hemileia; Leaf rust; Leucoptera. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1156023/1/On-the-accuracy-of-threshold-genomic-prediction.pdf
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Marc: |
LEADER 02259naa a2200301 a 4500
001 2156023
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008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11295-022-01581-8$2DOI
100 1 $aCARVALHO, H. F.
245 $aOn the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee.$h[electronic resource]
260 $c2023
300 $a10 p.
520 $aObtaining resistance cultivars for leaf miner and leaf rust are the main important strategy of Brazil?s national coffee breeding program. The narrow genetic basis, and founder effect consequences, lead to challenges in quantifying and detecting genetic diversity for these traits. Biotechnology tools allied with classical breeding strategies are powerful in detecting variability and deploying a precision selection. The selection based on the genetic merit of an individual obtained from thousands of single nucleotide polymorphism effects is known as genomic selection. The ordinal scale principally makes the resistance evaluation of the leaf rust and leaf miner of the score, categorizing the phenotypes following the discrete (ordinal) distribution. Hence, this distribution can be better analyzed by threshold models. Our goals were to optimize genomic prediction models for coffee resistance to leaf rust and leaf miner via threshold models and compare pedigree and genomic relationship matrices to underlying prediction models. We have observed that the genomic model with the genomic relationship matrix performed better for all scenarios. For the traits with at least five degrees of scores, the threshold models performed better, whereas for a trait with ten degrees of scores, we see no advantage to using a threshold model for genomic prediction.
650 $aCoffea arabica var. arabica
650 $aGenomics
650 $aHemileia
650 $aLeaf rust
650 $aLeucoptera
700 1 $aFERRÃO, L. F. V.
700 1 $aGALLI, G.
700 1 $aNONATO, J. V. A.
700 1 $aPADILHA, L.
700 1 $aMALUF, M. P.
700 1 $aRESENDE JR., M. F. R. de
700 1 $aFRITSCHE-NETO, R.
700 1 $aGUERREIRO-FILHO, O.
773 $tTree Genetics & Genomes$gv. 19, n. 1, 2023.
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Registro original: |
Embrapa Café (CNPCa) |
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| 1. |  | CARVALHO, H. F.; FERRÃO, L. F. V.; GALLI, G.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR., M. F. R. de; FRITSCHE-NETO, R.; GUERREIRO-FILHO, O. On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee. Tree Genetics & Genomes, v. 19, n. 1, 2023. 10 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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