Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations

BMC Bioinformatics

Table 2 Variance components and LogLikelihood for models with or without the segment

Seg-chromosome	6	3	5
SNP − log₁₀(p-value)	8.02	5.94	3.78
Lk_m1	−1227.938	−1227.938	−1227.938
Lk_m2	−1210.800	−1223.178	−1224.540
LRT	34.28	9.52	6.80
p-value _LRT	1.1 × 10⁻⁹	6.5 × 10⁻⁴	3.1 × 10⁻³
VarE_m1	3.70	3.70	3.70
VarA_m1	2.68	2.68	2.68
VarE_m2	3.73	3.67	3.69
VarA_m2	1.95	2.42	2.55
segmVA	0.70	0.63	0.15
%segmVA	0.11	0.09	0.02

Seg-chromosome = Number of chromosome where segment is located, m1 = model(2a) without the segment: y = Xβ + a + e, m2 = model (8) with the segment y = X β + a₁ + a₂ + e, SNP − log₁₀(p-value) = −Logarithm in base 10 of the SNP p-value selected to create a segment, Lk_m1 = −LogLikelihood for m1, Lk_m2 = −LogLikelihood for m2, LRT = Likelihood Ratio Test for m1 and m2, p-value_LRT = p-value for LRT, VarE_m1 = Error variance $(σ_{e}^{2})$ of m1, VarA_m1 = Additive variance $(σ_{A}^{2})$ of m1, VarE_m2 = Error variance $(σ_{e}^{2})$ of m2, VarA_m2 = Additive variance $(σ_{A}^{2})$ of m2, segmVA = Additive variance segment $(σ_{A_{1}}^{2})$ of m2, %segmVa = Proportion in% of the total variance explained by the segment.

ISSN: 1471-2105