AUTHOR=Alvarenga Amanda B. , Veroneze Renata , Oliveira Hinayah R. , Marques Daniele B. D. , Lopes Paulo S. , Silva Fabyano F. , Brito Luiz F. 

TITLE=Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

JOURNAL=Frontiers in Genetics

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00263

DOI=10.3389/fgene.2020.00263

ISSN=1664-8021

ABSTRACT=<p>As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (<bold>G</bold><sup>−1</sup>) and pedigree (<inline-formula><mml:math id="M1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mstyle mathvariant="bold"><mml:mtext>A</mml:mtext></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle mathvariant="bold"><mml:mn>22</mml:mn></mml:mstyle></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71–0.99). The scenario mimicking a completely polygenic trait (<inline-formula><mml:math id="M2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mi>Q</mml:mi><mml:mi>T</mml:mi><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo></mml:math></inline-formula> 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and <inline-formula><mml:math id="M3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mi>Q</mml:mi><mml:mi>T</mml:mi><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine <bold>G</bold><sup>−1</sup> and <inline-formula><mml:math id="M4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mstyle mathvariant="bold"><mml:mtext>A</mml:mtext></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle mathvariant="bold"><mml:mn>22</mml:mn></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle mathvariant="bold"><mml:mo>-</mml:mo></mml:mstyle><mml:mn>1</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula> had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71–0.99) genetic correlations between purebred and crossbred populations].</p>