Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

Table 4 Average number of variants per distribution over the number of iterations in the simulated dataset

Data	Analysis	Drop	Number of variants per distribution
Data	Analysis	Drop	0 \(\sigma_{g}^{2}\)	0.0001 \(\sigma_{g}^{2}\)	0.001 \(\sigma_{g}^{2}\)	0.01 \(\sigma_{g}^{2}\)
HD	FULL	0.0	592,931	3286	898	21
S	FULL	0.0	914,767	5053	666	48
		0.7	369,322	2464	471	43
		0.9	172,007	1350	407	42
S	CHR	0.0	915,665	4279	519	71
		0.7	371,643	2279	396	65
		0.9	171,554	1350	358	61
S	KEPT	0.7	298,238	2118	499	44
		0.9	98,494	908	390	45
S + HD	KEPT + HD	0.7	759,835	4459	663	44
		0.9	650,252	3813	616	45

HD = HD genotypes used for prediction, S = sequence variants used for prediction, FULL = all variants analysed together, CHR = all variants analysed per chromosome, KEPT = variants selected by CHR reanalysed with all chromosomes together, KEPT + HD = variants selected by CHR and all HD variants reanalysed with all chromosomes together, dropProp = proportion of variants that is dropped after 10,000 MCMC iterations, \(\sigma_{g}^{2}\) = additive genetic variance

ISSN: 1297-9686