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- W4308961911 abstract "Abstract Background Whole-genome sequence (WGS) data harbor causative variants that may not be present in the regular SNP chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k imputed sequenced animals per line. Methods Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1,365 to 1,491 in maternal lines and 381 to 1,865 in terminal lines. Imputation occurred within each line, and the number of animals imputed to sequence ranged from 66k to 76k in maternal lines and 29k to 104k in terminal lines. Two preselected SNP sets were generated based on genome-wide association study (GWAS). Top40k included the SNP with the lowest p-value in each of 40k genomic windows; ChipPlusSign included significant variants integrated into the regular porcine SNP chip. Single-step genomic predictions with equal or different SNP variances using those SNP sets were compared to the regular porcine SNP chip. Results In maternal lines, ChipPlusSign, and Top40k showed, on average, 0.62%, and 4.9% increased accuracy compared to the regular porcine SNP chip. The greatest changes were for fertility traits with Top40k, where the initial accuracy based on the SNP chip was low. However, for terminal lines, Top40k resulted in a loss of accuracy of 1% on average. Only ChipPlusSign provided a positive, albeit small, gain (0.85%). Assigning different variances for SNP slightly improved accuracies when using variances obtained from BayesR; however, the increase was inconsistent across the lines and traits. Conclusions The benefit of using sequence data depends on the line, size of the genotyped population, and how the WGS variants are preselected. When WGS is available on hundreds of thousands of animals, the advantage of sequence data is present but limited in maternal and terminal pig lines." @default.
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- W4308961911 date "2022-11-14" @default.
- W4308961911 modified "2023-10-18" @default.
- W4308961911 title "Using large-scale whole-genome sequence data for single-step genomic predictions in maternal and terminal pig lines" @default.
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- W4308961911 doi "https://doi.org/10.1101/2022.11.11.516229" @default.
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