Table 1.

Means of posterior distributions of genetic variances explained by each marker set

Traits	All accessions (N = 738)			Indica accessions (N = 451)
	${\sigma }_S^2$	${\sigma }_M^2$	${\sigma }_R^2$	${\sigma }_S^2$	${\sigma }_M^2$	${\sigma }_R^2$
Culm diameter	0.16	0.17*	0.16	0.13	0.17*	0.15
Culm strength	0.10	0.25*	0.16	0.11	0.19*	0.14
Flag leaf angle	0.22*	0.14	0.15	0.24*	0.14	0.14
Grain length	0.48*	0.11	0.11	0.41*	0.11	0.13
Grain width	0.49*	0.11	0.12	0.42*	0.11	0.14
Leaf length	0.26*	0.16	0.19	0.22*	0.16	0.19
Leaf senescence	0.12	0.25*	0.18	0.14	0.21*	0.16
Grain weight	0.40*	0.11	0.13	0.31*	0.12	0.13
Salt injury	0.10	0.11	0.12*	0.09	0.11*	0.11*
Time to flowering	0.45*	0.12	0.13	0.39*	0.13	0.13
Pan. threshability	0.11	0.13*	0.10	0.11	0.15*	0.11

${\sigma }_S^2$: genetic variance explained by SNPs

${\sigma }_M^2$: genetic variance explained by DNA transposon markers (MITE/DTX)

${\sigma }_R^2$: genetic variance explained by retrotransposons (RLX/RIX)

Traits are scaled such that phenotypic variances are 1

*Best strategy