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KMID : 1132720140120040254
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Genomics & Informatics
2014 Volume.12 No. 4 p.254 ~ p.260
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The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study
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Lee Young-Sup
Kim Hyeon-Jeong
Cho Seo-Ae
Kim Hee-Bal
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Abstract
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Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.
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KEYWORD
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best linear unbiased estimation (BLUE), best linear unbiased prediction (BLUP), SNP genomic best linear unbiased prediction (SNP-GBLUP), SNP-SNP relationship matrix
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