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dc.contributor.authorCARVALHO, H. F.
dc.contributor.authorFERRÃO, L. F. V.
dc.contributor.authorGALLI, G.
dc.contributor.authorNONATO, J. V. A.
dc.contributor.authorPADILHA, L.
dc.contributor.authorMALUF, M. P.
dc.contributor.authorRESENDE JR., M. F. R. de
dc.contributor.authorFRITSCHE-NETO, R.
dc.contributor.authorGUERREIRO-FILHO, O.
dc.date.accessioned2023-08-21T19:25:14Z-
dc.date.available2023-08-21T19:25:14Z-
dc.date.created2023-08-21
dc.date.issued2023
dc.identifier.citationTree Genetics & Genomes, v. 19, n. 1, 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1156023-
dc.descriptionObtaining 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.
dc.language.isoeng
dc.rightsopenAccess
dc.titleOn the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee.
dc.typeArtigo de periódico
dc.subject.nalthesaurusCoffea arabica var. arabica
dc.subject.nalthesaurusLeucoptera
dc.subject.nalthesaurusHemileia
dc.subject.nalthesaurusLeaf rust
dc.subject.nalthesaurusGenomics
dc.format.extent210 p.
riaa.ainfo.id1156023
riaa.ainfo.lastupdate2023-08-21
dc.identifier.doihttps://doi.org/10.1007/s11295-022-01581-8
dc.contributor.institutionHUMBERTO 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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