The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study.
- Author:
Young Sup LEE
1
;
Hyeon Jeong KIM
;
Seoae CHO
;
Heebal KIM
Author Information
- Publication Type:Original Article
- Keywords: best linear unbiased estimation (BLUE); best linear unbiased prediction (BLUP); SNP genomic best linear unbiased prediction (SNP-GBLUP); SNP-SNP relationship matrix
- MeSH: Cohort Studies*; Polymorphism, Single Nucleotide
- From:Genomics & Informatics 2014;12(4):254-260
- CountryRepublic of Korea
- Language:English
- 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.
