Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar)

Table 2 Correlation^a between breeding values for SRS resistance phenotypes^b estimated with different models^c using data from 50 K SNP genotypes^d

Model	PBLUP	GBLUP	SNPBLUP	PSNPBLUP	BAYESC	PBAYESC	BLASSO	PBLASSO
PBLUP		0.79	0.81	0.95	0.77	0.85	0.77	0.84
ssGBLUP	0.79		0.95	0.91	1.00	0.99	1.00	1.00
BLUPSNP	0.78	1.00		0.94	0.96	0.96	0.96	0.96
PBLUPSNP	0.91	0.96	0.96		0.90	0.94	0.90	0.93
BAYESC	0.77	1.00	1.00	0.95		0.99	1.00	0.99
PBAYESC	0.90	0.97	0.97	1.00	0.96		0.99	1.00
BLASSO	0.76	1.00	1.00	0.95	1.00	0.96		0.99
PBLASSO	0.91	0.97	0.96	1.00	0.96	1.00	0.96

^aAverage Pearson correlation between breeding values estimated with different models a from five-fold cross validation scheme
^bSRS resistance phenotypes: Survival days (DAYS) below diagonal and binary survival (STATUS) above diagonal
^cModels with pedigree: pedigree based BLUP (PBLUP), genomic BLUP (GBLUP), marker-effects BLUP with polygenic pedigree (PSNPBLUP) and Bayesian estimation methods with marker-effects and polygenic pedigree (PBAYESC and PBLASSO); Models with only marker-effects: market-effects BLUP (SNPBLUP) and Bayesian estimation methods (BAYESC and BLASSO)
^dThe effective number of SNPs used was 49 684 from the 50 K SNP array

ISSN: 1471-2164