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Title: Accelerating genetic improvement with SNP chips and DNA sequencing

Author
item Van Tassell, Curtis - Curt
item Vanraden, Paul
item Wiggans, George
item MATUKUMALLI, L - GEORGE MASON
item Schroeder, Steven - Steve
item O'CONNELL, J - UNIVERSITY OF MARYLAND
item SCHNABEL, R - UNIVERSITY OF MARYLAND
item TAYLOR, J - UNIVERSITY OF MISSOURI
item POLLAK, E - CORNELL UNIVERSITY
item MUNSON, M - ILLUMINA
item BAILEY, D - ILLUMINA
item Sonstegard, Tad

Submitted to: Joint Abstracts of the American Dairy Science and Society of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 2/28/2009
Publication Date: 7/11/2009
Citation: Van Tassell, C.P., Van Raden, P.M., Wiggans, G.R., Matukumalli, L.K., Schroeder, S.G., O'Connell, J., Schnabel, R.D., Taylor, J.F., Pollak, E.J., Munson, M., Bailey, D., Sonstegard, T.S. 2009. Accelerating genetic improvement with SNP chips and DNA sequencing. Joint Abstracts of the American Dairy Science and Society of Animal Science.

Interpretive Summary:

Technical Abstract: The development of high-density single nucleotide polymorphism (SNP) assays is expected to have a profound impact on genetic progress in the U.S. dairy industry. In the 16 months since its initial availability, the Illumina BovineSNP50 BeadChip has been used to genotype nearly 20,000 Holsteins. These genomic data were included for the first time in the national dairy cattle genetic evaluation published by the USDA in January 2009. Substantial increases in genetic improvement have been predicted through the implementation of genome enabled selection. Currently, however, validation results are available only from the analysis of historic data, where populations have somewhat arbitrarily been divided into past and “future” populations. Availability of low-density but targeted SNP data could also dramatically impact genetic improvement. Low-density SNP data could be used to validate reported parentage and correct pedigree errors if comprehensive genotyping were conducted. These data could also be used to discover parentage and to more accurately characterize the degree of relatedness among animals in the population using genomics-based relationship coefficients. By accurately charactering the fractions of the genome inherited from each grandparent, genetic similarity that is currently described by statistical averaging using the pedigree could be refined to more accurately predict genetic merit early in life. Finally, individual animal genome sequencing is on the scientific horizon. Availability of such data could have implications beyond genetic improvement, and result in deeper understandings of basic biology, consequences of selection, and even animal and human health. Our ability to fully utilize these data will present enormous statistical and computational challenges.