Genomics Inform.  2014 Dec;12(4):254-260. 10.5808/GI.2014.12.4.254.

The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study

• Lee YS
• Kim HJ
• Cho S
• Kim H

• KMID: 2391505
• DOI: http://doi.org/10.5808/GI.2014.12.4.254

Abstract

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.

Keyword

best linear unbiased estimation (BLUE); best linear unbiased prediction (BLUP); SNP genomic best linear unbiased prediction (SNP-GBLUP); SNP-SNP relationship matrix

MeSH Terms

Cohort Studies*
Polymorphism, Single Nucleotide