02068naa a2200337 a 450000100080000000500110000800800410001902200330006002400250009310000210011824502700013926000090040950003530041852006030077165300090137465300110138365300260139465300220142065300240144265300140146665300080148065300150148870000180150370000210152170000180154270000170156070000220157770000160159970000140161577301010162910642772023-07-28       2023    bl uuuu     u00u1 u    #d  a1836-0939; eISSN: 1836-5787.7 a10.1071/AN215812DOI1 aRODRÍGUEZ, J.D.  aEffect of minor allele frequency and density of single nucleotide polymorphism marker arrays on imputation performance and prediction ability using the single-step genomic Best Linear Unbiased Prediction in a simulated beef cattle population.h[electronic resource]  c2023  aArticle history: Submitted 1 December 2021, Accepted 1 March 2023,  Published 4 April 2023. -- Correspondence to: Juan Diego Rodríguez,  Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrarias e Veterinárias, Departamento de Zootecnia, Jaboticabal, 14884-900, Brazil. Email: juan.diego@unesp.br  -- Handling Editor: Kim Bunter. --  aContext: In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims: This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. © 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing  aBias  aBovine  aCustomised SNP arrays  aGenomic selection  aImputation accuracy  aInflation  aMAF  aSimulation1 aPERIPOLLI, E.1 aLONDOÑO-GIL, M.1 aESPIGOLAN, R.1 aLÔBO, R. B.1 aLÓPEZ-CORREA, R.1 aAGUILAR, I.1 aBALDI, F.  tAnimal Production Science. 2023, volume 63, issue 9, p. 844-852. https://doi.org/10.1071/AN21581