Performance of genomic prediction within and across generations in maritime pine

BMC Genomics

Table 1 List of genomic selection studies based on real data sets conducted on forest tree species. Studies are listed in chronological order of publication. This study is the last one listed

Species	Population				Genotyping		Traits analyzed	Models	Prediction accuracy	Reference
Species	Size	Family type	Family size	G	Method	Number of markers	Traits analyzed	Models	Prediction accuracy	Reference
Eucalyptus hybrids	738	43 FS	15 to 23	1	DArT array	3129	Growth, wood properties	RR-BLUP	0.54–0.6	[26]
Eucalyptus hybrids	920	51 FS	10 to 15	1	DArT array	3564	Growth, wood properties	RR-BLUP	0.38–0.55	[26]
Loblolly pine	790–840	61 FS	-	1	SNP array	4852	Growth	RR-BLUP	0.63–0.75	[51]
Loblolly pine	951	61 FS	15 ± 2.2	1	SNP array	4825	Growth, tree architecture, wood properties, disease resistance	RR-BLUP, Bayes A, Bayes Cπ, B-LASSO, RR-BLUP B	0.17–0.51	[52]
Loblolly pine	149	13 FS	1 to 34	1	SNP array	3406	Growth, wood properties	RR-BLUP	0.30–0.83	[75]
Loblolly pine	165	9 FS	3 to 37	1	SNP array	3461	Growth	ABLUP, GBLUP	0.37–0.74	[71]
White spruce	1694	214 HS	-	1	SNP array	6385	Growth, wood properties	ABLUP, B-RR, B-LASSO	0–0.44	[24]
White spruce	1748	59 FS	25 to 33	1	SNP array	6932	Growth, wood properties	ABLUP, B-RR, Combined	0.33–0.45	[60]
Loblolly pine	956	61 FS	15 ± 2.2	1	SNP array	4825	Growth, tree architecture	ABLUP, RR-BLUP	0.17–0.51	[57]
Maritime pine	661	191 HS	1 to 13	2	SNP array	2500	Growth, stem straightness	GBLUP, B-RR, B-LASSO	0.09–0.73	[25]
Interior spruce	1126	25 HS	<32	1	GBS	8868–62,198	Growth, wood properties	RR-BLUP, GRR	0.34–0.77	[61]
Interior spruce	769	25 HS	-	1	GBS	34,570–50,803	Growth	RR-BLUP, GRR, Bayes Cπ	0.04–0.55	[76]
Maritime pine	817	35 HS	13 to 34	3	SNP array	4332	Growth, stem straightness	ABLUP, GBLUP, B-LASSO	0.24–0.94	This study

FS full-sib family, HS half-sib family, G number of generations included in the study, GBS genotyping-by-sequencing method

ISSN: 1471-2164