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Title:   Author  item
                                            Vanraden, Paulitem
                                            O'CONNELL, JEFFREY - University Of Marylanditem
                                            Wiggans, Georgeitem
                                            WEIGEL, KENT - University Of Wisconsin
  Submitted to: Genetic Selection Evolution
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 3/2/2011
                                    Publication  Date: 3/2/2011
                                    Citation: Van Raden, P.M., O'Connell, J.R., Wiggans, G.R., Weigel, K.A. 2011. Genomic evaluations with many more genotypes. Genetic Selection Evolution. 43:10.Interpretive Summary: Genomic evaluations can include animals genotyped with more or fewer than the current 50,000 markers using new tools such as 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers were predicted using simulation, whereas strategies to use fewer markers were compared using subsets of actual genotypes. A higher density of 500,000 markers increased reliability slightly (1.6%) in simulation, whereas lower densities and imputation of the missing markers could allow breeders to apply cost-effective genomic selection to many more animals. New methods for combining information from multiple data sets improve genetic progress with less cost. More precise estimates of reliability allow breeders to properly balance benefits vs. costs of using different marker sets.
                                Technical Abstract: Background
 Genomic evaluations have quickly become more reliable over the last two years in many countries as more animals were genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. Overall cost of selection is reduced by genotyping most animals at less than the highest density and filling their missing genotypes using haplotypes. Algorithms to combine different densities must be efficient because numbers of genotyped animals and markers may continue to grow quickly.

Methods
Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations were compared using linear and nonlinear methods.

Results
Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations in 50,000-marker and 1.6% lower in 500,000-marker data.

Conclusions
Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can account for success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.


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