Implications of SNP weighting on single‐step genomic predictions for different reference population sizes |
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| Authors: | D A L Lourenco B O Fragomeni H L Bradford I R Menezes J B S Ferraz I Aguilar S Tsuruta I Misztal |
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| Affiliation: | 1. Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA;2. FZEA, University of Sao Paulo, Pirassununga, SP, Brazil;3. Instituto Nacional de Investigacion Agropecuaria, Canelones, Uruguay |
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| Abstract: | We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single‐step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix ( G ). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome‐wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G . Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important. |
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| Keywords: | accuracy BayesB SNP weighting variable selection weighted ssGBLUP |
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