|POMP, DANIEL - University Of North Carolina|
|Van Tassell, Curtis - Curt|
Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/17/2015
Publication Date: 7/12/2015
Citation: Wiggans, G.R., Cooper, T.A., Van Raden, P.M., Pomp, D., Van Tassell, C.P., Bickhart, D.M., Sonstegard, T.S. 2015. Effect of increasing the number of single-nucleotide polymorphisms from 60,000 to 85,000 in genomic evaluation of Holsteins. Journal of Dairy Science. 98(Suppl. 2)/Journal of Animal Science 93(Suppl. 3):578(abstr. 537).
Technical Abstract: The periodic need to restock reagent pools for genotyping chips provides an opportunity to increase the number of single-nucleotide polymorphisms (SNP) on a chip at no increase in cost. A high-density chip with >140,000 SNP has been developed by GeneSeek Inc. (Lincoln, NE) to increase accuracy of genomic evaluation of dairy cattle. The chip includes all SNP from the GeneSeek Genomic Profiler HD, all other SNP currently used in genomic evaluations computed by the Council on Dairy Cattle Breeding (Reynoldsburg, OH), SNP with lower minor allele frequencies to allow better tracking of causative variants, and SNP that are possible functional mutations based on sequence data. Most added SNP were selected from the Illumina BovineHD Genotyping BeadChip based on the magnitude of effects on 31 yield and fitness traits. Genotypes already available from other chips were used to impute and evaluate SNP expected to be included on the new chip to forecast its potential benefits. Effects for 134,511 usable SNP were estimated for all breed-trait combinations; SNP with the largest absolute values for effects were selected (5,000 for Holsteins, 1,000 for Jerseys, and 500 each for Brown Swiss and Ayrshires for each trait), which resulted in 78,032 SNP after removing duplicates. An additional 9,130 SNP with many parent-progeny conflicts after imputation were removed, which resulted in 72,843 SNP. Of those, 38,515 were among the 60,671 SNP currently used in genomic evaluation. To minimize possible accuracy loss, 12,094 of the SNP currently used but not already selected and with the largest effects were added for a total of 84,937 SNP. Three cutoff studies were done with 60,671, 84,937, and 134,511 SNP to determine gain in reliability over parent average when evaluations based on data from August 2011 were used to predict genetic merit from December 2014. Across all traits, mean gains were 32.5, 33.4, and 32.0 percentage points, respectively. The lowest gain from the highest number of SNP was likely because of imputation error. The gain of 0.9 percentage points from adding nearly 25,000 SNP justifies the extra computation time needed. However, the gain may be overestimated because data used to select the most informative SNP were also the data used to determine gain.