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With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25[N.sub.e] markers/morgan using microsatellites and 8, 4, 2, and 1[N.sub.e] markers/morgan using SNP, where 1[N.sub.e] markers/morgan means 100 markers per morgan, if effective size ([N.sub.e]) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25[N.sub.e]/morgan to 2[N.sub.e]/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1[N.sub.e]/morgan to 8[N.sub.e]/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8[N.sub.e] SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and [N.sub.e] = 100, this implies that about ~24,000 SNP are needed. Key words: accuracy of selection, breeding value estimation, dense marker map, genome-wide selection, marker-assisted selection

Using genome-wide SNP data, we calculated genomic inbreeding coefficients ( FROH > 1 Mb, FROH > 2 Mb, FROH > 8 Mb and FROH > 16 Mb) derived from runs of homozygosity ( ROH) of different lengths (>1, >2, >8 and > 16 Mb) as well as from levels of homozygosity ( FHOM). We compared these values of inbreeding coefficients with those calculated from pedigrees ( FPED) of 1422 bulls comprising Brown Swiss (304), Fleckvieh (502), Norwegian Red (499) and Tyrol Grey (117) cattle breeds. For all four breeds, population inbreeding levels estimated by the genomic inbreeding coefficients FROH > 8 Mb and FROH > 16 Mb were similar to the levels estimated from pedigrees. The lowest values were obtained for Fleckvieh ( FPED = 0.014, FROH > 8 Mb = 0.019 and FROH > 16 Mb = 0.008); the highest, for Brown Swiss ( FPED = 0.048, FROH > 8 Mb = 0.074 and FROH > 16 Mb = 0.037). In contrast, inbreeding estimates based on the genomic coefficients FROH > 1 Mb and FROH > 2 Mb were considerably higher than pedigree-derived estimates. Standard deviations of genomic inbreeding coefficients were, on average, 1.3-1.7-fold higher than those obtained from pedigrees. Pearson correlations between genomic and pedigree inbreeding coefficients ranged from 0.50 to 0.62 in Norwegian Red (lowest correlations) and from 0.64 to 0.72 in Tyrol Grey (highest correlations). We conclude that the proportion of the genome present in ROH provides a good indication of inbreeding levels and that analysis based on ROH length can indicate the relative amounts of autozygosity due to recent and remote ancestors. [ABSTRACT FROM AUTHOR]