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Implications of SNP weighting on single‐step genomic predictions for different reference population sizes
Author(s) -
Lourenco D. A. L.,
Fragomeni B. O.,
Bradford H. L.,
Menezes I. R.,
Ferraz J. B. S.,
Aguilar I.,
Tsuruta S.,
Misztal I.
Publication year - 2017
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12288
Subject(s) - snp , biology , population , quantitative trait locus , genetics , single nucleotide polymorphism , genome wide association study , best linear unbiased prediction , weighting , genotype , selection (genetic algorithm) , demography , computer science , gene , medicine , radiology , artificial intelligence , sociology
Summary 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 (ss GBLUP ) and weighted ss GBLUP (Wss GBLUP ) were used to estimate genomic EBV using a genomic relationship matrix ( G ). The Wss GBLUP performed better in small genotyped populations; however, any advantage for Wss GBLUP was reduced or eliminated when more animals were genotyped. Wss GBLUP had greater resolution for genome‐wide association ( GWA ) as did increasing the number of genotyped animals. For few QTL , accuracy was greater for Wss GBLUP than ss GBLUP ; 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.