Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods

BMC Proceedings

Table 2 Average number of SNPs (#SNP) fitted in the model, estimated variance components, and estimated heritability (Heritability).

Methods	#SNP	Estimated variance components					Heritability
		Marker	Polygenic	Genetic¹	Residual	Total
True value ²		–	–	51.76	51.76	103.52	0.500
P-BLUP	–	–	54.44	54.44	48.68	103.12	0.528
G-BLUP	10031
No Poly		44.54	–	44.54	54.84	99.38	0.448
Poly		38.53	12.09	50.62	49.04	99.66	0.508
BayesB, π = 0.75	2508
No Poly		44.28	–	44.28	54.08	98.36	0.450
Poly		39.05	11.06	50.11	48.32	98.43	0.509
BayesB, π = 0.95	502
No Poly		43.96	–	43.96	54.16	98.12	0.448
Poly		38.05	12.80	50.85	47.59	98.44	0.517
BayesB, π = 0.99	100
No Poly		43.44	–	43.44	54.58	98.02	0.443
Poly		37.43	12.35	49.78	48.30	98.09	0.508
BayesCπ
No Poly	124	45.68	–	45.68	53.63	99.31	0.460
Poly	80	40.21	10.33	50.54	48.58	99.12	0.510
BayesCπ gen 5 ³
No Poly	92	47.13	–	47.13	53.48	100.61	0.468

Results are based on training on the first three generations and validation on generation 4 using P-BLUP, G-BLUP, BayesB with different π’s, and BayesCπ, and without (No Poly) and with (Poly) polygenic effects.
¹Total genetic variance = marker variance + polygenic variance.
²Total QTL variance = residual variance = 51.76 in the QTLMAS2010 dataset.
³Training on the first 4 generations.

ISSN: 1753-6561