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.
Artigo
em Inglês
| 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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Estudos de Coortes
/
Polimorfismo de Nucleotídeo Único
Tipo de estudo:
Estudo de etiologia
/
Estudo de incidência
/
Estudo observacional
/
Estudo prognóstico
/
Fatores de risco
Idioma:
Inglês
Revista:
Genomics & Informatics
Ano de publicação:
2014
Tipo de documento:
Artigo
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