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

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

Data Analysis Drop Number of variants per distribution
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
  1. 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
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