The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance

2020 ◽  
Vol 138 (1) ◽  
pp. 14-22
Author(s):  
Viktor Milkevych ◽  
Per Madsen ◽  
Hongding Gao ◽  
Just Jensen
Keyword(s):  
Bayesian Analysis ◽  
Linear Model ◽  
Mixed Linear Model ◽  
Relative Effect ◽  
Genomic Information ◽  
Unknown Variance

A bayesian analysis of the mixed linear model

1990 ◽  
Vol 19 (3) ◽  
pp. 987-1002 ◽  
Author(s):  
Peyton Cook ◽  
Lyle D. Broemeling ◽  
Mohammad Gharaff
Keyword(s):  
Bayesian Analysis ◽  
Linear Model ◽  
Mixed Linear Model

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

1999 ◽  
Vol 99 (7-8) ◽  
pp. 1255-1264 ◽  
Author(s):  
D. L. Wang ◽  
J. Zhu ◽  
Z. K. L. Li ◽  
A. H. Paterson
Keyword(s):  
Linear Model ◽  
Mixed Linear Model ◽  
Epistatic Effects

Detecting dominance QTL in poultry pedigrees using variance component methodology

2007 ◽  
Vol 2007 ◽  
pp. 2-2
Author(s):  
S. J. Rowe ◽  
R. Pong-Wong ◽  
C.S. Haley ◽  
S.A. Knott ◽  
D.J. de Koning
Keyword(s):  
Linear Model ◽  
Variance Component ◽  
Mixed Linear Model ◽  
Variance Component Analysis ◽  
Order Effects ◽  
Empirical Power ◽  
Commercial Broiler ◽  
Underlying Mechanisms ◽  
Higher Order Effects ◽  
Qtl Effects

Methods that detect QTL within commercial populations circumvent the need for expensive experimental populations and facilitate direct application of results through marker assisted selection. Variance component analysis (VCA) uses phenotypic, pedigree and marker information within a mixed linear model to simultaneously detect QTL and estimate breeding values. The inclusion of non-additive effects has potential for greater accuracy of selection and understanding of underlying mechanisms. The linear model can be extended to include higher order effects such as dominance, however, there is little information on empirical power. Here VCA was applied to real and simulated commercial broiler data to detect additive and dominant QTL effects.

scholarly journals Mixed linear model approach adapted for genome-wide association studies

2010 ◽  
Vol 42 (4) ◽  
pp. 355-360 ◽  
Author(s):  
Zhiwu Zhang ◽  
Elhan Ersoz ◽  
Chao-Qiang Lai ◽  
Rory J Todhunter ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Linear Model ◽  
Association Studies ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Model Approach
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