Effect of genotyping strategies on the sustained benefit of single-step genomic BLUP over multiple generations

Milagros Sánchez-Mayor, Valentina Riggio, Pau Navarro, Beatriz Gutiérrez-Gil, Chris S Haley, Luis Fernando de la Fuente, Juan-José Arranz, Ricardo Pong-Wong

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Single-step genomic best linear unbiased prediction (ssGBLUP) allows the inclusion of information from genotyped and ungenotyped individuals in a single analysis. This avoids the need to genotype all candidates with the potential benefit of reducing overall costs. The aim of this study was to assess the effect of genotyping strategies, the proportion of genotyped candidates and the genotyping criterion to rank candidates to be genotyped, when using ssGBLUP evaluation. A simulation study was carried out assuming selection over several discrete generations where a proportion of the candidates were genotyped and evaluation was done using ssGBLUP. The scenarios compared were: (i) three genotyping strategies defined by their protocol for choosing candidates to be genotyped (RANDOM: candidates were chosen at random; TOP: candidates with the best genotyping criterion were genotyped; and EXTREME: candidates with the best and worse criterion were genotyped); (ii) eight proportions of genotyped candidates (p); and (iii) two genotyping criteria to rank candidates to be genotyped (candidates’ own phenotype or estimated breeding values). The criteria of the comparison were the cumulated gain and reliability of the genomic estimated breeding values (GEBV).
Original languageEnglish
Article number23
JournalGenetics Selection Evolution
Issue number1
Early online date18 Mar 2022
Publication statusPublished - 18 Mar 2022


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