Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.

Autor: Rodriguez Neira JD; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil. juan.diego@unesp.br., Peripolli E; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil., de Negreiros MPM; Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil., Espigolan R; Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil., López-Correa R; Departamento de Genética y Mejoramiento Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay., Aguilar I; Instituto Nacional de Investigación Agropecuaria (INIA), Montevideo, Uruguay., Lobo RB; Associação Nacional de Criadores e Pesquisadores (ANCP), Ribeirão Preto, Brazil., Baldi F; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil.
Jazyk: angličtina
Zdroj: Journal of applied genetics [J Appl Genet] 2022 May; Vol. 63 (2), pp. 389-400. Date of Electronic Publication: 2022 Feb 08.
DOI: 10.1007/s13353-022-00685-0
Abstrakt: This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.
(© 2022. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)
Databáze: MEDLINE
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