Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Table 2 Estimated parameters for FDC

	Model 1	Model 1-u^a	Model 1-f^b	Model 2	Model 3	Model 4
f ²	0.14	0.14	N/A	0.07	0.06	0.03
	(0.03)	(0.03)		(0.03)	(0.02)	(0.02)
c ²	0.02	0.02	0.03	0.02	0.02	0.01
	(0.03)	(0.03)	(0.03)	(0.02)	(0.02)	(0.01)
u ²	0.00	N/A	0.29	N/A	N/A	N/A
	(0)		(0.06)
g ²	N/A	N/A	N/A	0.25	0.18	0.10
				(0.06)	(0.06)	(0.05)
α ²	N/A	N/A	N/A	N/A	0.12	0.21
					(0.13)	(0.22)
δ ²	N/A	N/A	N/A	N/A	N/A	0.27
						(0.23)
Log L	1621.30	1621.30	1619.24	1633.91	1650.56^c	1695.96^d
					(3.55)	(10.17)
Parameters	f, c, u	f, c	c, u	f, c, g	f, c, g, α	f, c, g, α, δ
AIC^e	-3236.60	-3238.60	-3234.48	-3261.82	-3288.02	-3365.58

Proportion of total phenotypic variance due to family (f), cage (c), and polygenic effects based on pedigree (u), realized relationships (g), and specific additive and dominance SNP effects (α and δ) when using model 1, 2, 3 and 4 for FDC
^aModel 1 without the term u, ^bModel 1 without the term f, ^cThe averaged log-likelihood during MCMC process (the averaged number of parameters due to additive SNP in the model), ^dThe averaged likelihood during MCMC process (the averaged number of parameters due to additive and dominance SNP in the model), ^eAIC = 2 * number of parameters - 2 * log likelihood

ISSN: 1297-9686