The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study
Genomics & Informatics
;
: 254-260, 2014.
Article
in English
| WPRIM
| ID: wpr-113803
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Cohort Studies
/
Polymorphism, Single Nucleotide
Type of study:
Etiology study
/
Incidence study
/
Observational study
/
Prognostic study
/
Risk factors
Language:
English
Journal:
Genomics & Informatics
Year:
2014
Type:
Article
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