BIOINFORMATICS TO IMPLEMENT GENOMIC SELECTION (BIGS)

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: NRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 0216777
• Grant No.: 2009-35205-05100
• Proposal No.: 2008-03924
• Project Start Date: Jan 1, 2009
• Project End Date: Dec 31, 2012
• Grant Year: 2009
• Program Code: [43.0]- Animal Genome

Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011

Performing Department
ANIMAL SCIENCE

Non Technical Summary
Conventional selection is based on the collection and analysis of pedigree and performance records. Predicted merit of selection candidates cannot be particularly accurate until information is available on the selection candidate, itself, or its offspring. This occurs because of crossing over and chance sampling of chromosomes at meiosis that limit the prediction of each parents contribution to an average value represented by half the merit of the parent. Delays in selection age beyond puberty that are required to measure the performance of the candidate or its offspring slow down the annual rate of genetic improvement relative to a selection program that could accurately determine genetic merit by puberty. Genomic prediction is an approach that uses genomic DNA sequence to determine whether particular parental contributions are above or below average. Genomic prediction would be accurate if genotypes were available on the set of causal polymorphisms responsible for variation in a particular trait. This project uses statistical techniques to analyze genomic data, along with phenotypic or progeny performance information, in order to identify the genomic regions that have predictive value. The analyses are complicated by the fact that the analysis is overparameterized, with some 50,000 genotypes available on each animal, and perhaps no more than a few thousand animals with records. Accordingly, many genotypes will be predictive in the training data as a result of spurious or chance associations and have nothing to do with causative mechanisms. This proposal will develop methods that allow researchers to analyze genomic information in concert with various kinds of biological data, including categorical and maternally-influenced traits via a web-based system. Further, it will allow the merit of new animals with genotypic but not performance records to be predicted from previous training analyses.

Animal Health Component

20%

Research Effort Categories

Basic

20%

Applied

20%

Developmental

60%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
303	3399	1080	100%

Knowledge Area
303 - Genetic Improvement of Animals;

Subject Of Investigation
3399 - Beef cattle, general/other;

Field Of Science
1080 - Genetics;

Keywords

genomic prediction

bayesian analysis

haplotyping

web-based access

improved selection accuracy

beef cattle improvement

Goals / Objectives
This research and development project will develop analytical software for Bayesian analysis of genomic information and deliver it within an integrated bioinformatics infrastructure that will enable genomic evaluation using high-throughput SNP genotyping technology in livestock. We will implement these methodologies across a range of economic traits in beef cattle, using genomic and phenotypic records from the U.S. Meat Animal Research Center.

Project Methods
This proposal will develop bioinformatics infrastructure for beef cattle through three complementary objectives. Objective 1 is to develop and successively improve an accessible, web-based system for input and secure storage of genotypic and phenotypic information, linked to training and prediction analyses, and reporting. Objective 2 is focused on the use of both rule-based and likelihood-based procedures for pre-processing of genotypic data. Processing genotypes prior to analysis can be helpful as it allows genotyping, pedigree errors or other errors associated with misidentification of samples to be detected. Analytical procedures for inferring genotypes and haplotypes from a single locus to a multiple locus system will be inferred by peeling on haplotypes spanning a number of loci. The result will be a probability distribution of haplotype pairs for each individual in the pedigree. Overlapping sets of loci will be used in adjacent steps to tie haplotype blocks together. The multiple locus algorithm will likely be important in the short-term to exploit low-density genotype panels used on some animals (e.g., non parents) to reduce genotyping costs. In the longer term, it will likely be important to exploit very high-density information, perhaps even sequence information, that will become available on widely-used sires. This will also provide opportunities for the Bayesian analysis tools to fit haplotypic rather than allelic effects. Objective 3 will extend the scope of Bayesian training and prediction software beyond the single-trait additive effects model routinely assumed for genomic prediction and account for maternal effects, multiple trait analysis and categorical traits.

Progress 01/01/09 to 12/31/12

Outputs
OUTPUTS: The BIGS research project has generated a product in the form of a web-based system (bigs.ansc.iastate.edu) for the analysis of bioinformatics data for whole genome analysis. The product has gone through successive iterations over the period 1 Jan 2010 to 31 Dec 2012 with additional features being added in each iteration. Events including workshops explaining the underlying technology and oral presentations communicating the use of the website has increased exposure of the product to national and international audiences of possible users. Potential participants have been able to register on the website and gain access to the analytical system. Registered users have been involved in activities that comprise the analysis of their data. Services to this user group have included consulting and tutoring on the use of the system and the interpretation of the results. The project has contributed to training at undergraduate, graduate and post-doctoral levels. Post-doctoral Fellows: Dr. David Habier (method development and programming), Dr. Kadir Kizilkaya (categorical methods and estimation of degrees of freedom, scale and pi), Dr. Joseph Abraham (programming and software testing), Dr. Mahdi Saatchi (applications to beef cattle). See publications for more details. It has been the basis for teaching genomic prediction courses. Graduates, post-doctoral fellows and researchers have been trained in genomic prediction methodology through the web interface to this software, and through taught courses capped at 40-70 students including; 1-10 June 2009, Ames, IA, Dekkers, Fernando and Garrick; 1 - 5 February 2010, Armidale, Australia, Fernando and Garrick; 14-18 June 2010, Ames, IA, Dekkers, Fernando and Garrick; 20-24 June 2011, Davos, Switzerland, Fernando and Garrick; 27 June- 1 July 2011, Wageningen, The Netherlands, Fernando and Garrick; 8-13 July 2012, Phoenix, Dekkers, Fernando and Garrick; 27-30 December 2012, Irene, South Africa, Garrick. PARTICIPANTS: Post-doctoral Fellows: Dr. David Habier (method development and programming), Dr. Kadir Kizilkaya (categorical methods and estimation of degrees of freedom, scale and pi), Dr. Joseph Abraham (programming and software testing), Dr. Mahdi Saatchi (applications to beef cattle). See publications for more details. Undergraduates: Rex Fernando. Analytical methods developed in this project have supported USDA-CSREES-NRI projects with Dr. Milt Thomas (2008- 2011: Identification of molecular markers to improve fertility in beef cattle), Dr. Diane Spurlock (2008-2011: Genetic regulation and genomic selection of energy balance traits in dairy cattle), Dr. Curt Van Tassell (2009-65205-05635: Implementation of whole genome selection in the US dairy and beef cattle industries), Dr. Joan Lunney (2009-2012: Characterization of host factors that contribute to PRRS disease resistance and susceptibility) and USDA-NIFA Dr. Jack Dekkers (2010-2012: Use of high-density SNP genotyping for genetic improvement of livestock), Dr. Nick Gabler (2010- 2013: The physiological basis of differences in efficiency, metabolism and energy partitioning between lines of pigs selected for residual feed intake), Dr. Jerry Taylor (2011-680004-30214: National program for genetic improvement of feed efficiency in beef cattle). The web interface has also supported analyses for many other USDA projects at various institutions without formal collaboration (e.g. Balschek et al, JAS, and other publications under review). Further, these software have supported Pfizer Animal Genetics and Merial Igenity in their development of genomic prediction services for beef cattle. The web system and software has also been used by Aviagen, Hy-Line, KeyGene, Livestock Improvement Corporation, Newsham, Pioneer Hi-Bred, PIC, and other animal and plant seedstock companies. Graduates, post-doctoral fellows and researchers have been trained in genomic prediction methodology through the web interface to this software, and through taught courses capped at 40-70 students including; 1-10 June 2009, Ames, IA, Dekkers, Fernando and Garrick; 1 - 5 February 2010, Armidale, Australia, Fernando and Garrick; 14-18 June 2010, Ames, IA, Dekkers, Fernando and Garrick; 20-24 June 2011, Davos, Switzerland, Fernando and Garrick; 27 June- 1 July 2011, Wageningen, The Netherlands, Fernando and Garrick; 8-13 July 2012, Phoenix, Dekkers, Fernando and Garrick; 27-30 December 2012, Irene, South Africa, Garrick. TARGET AUDIENCES: Target audiences include any individuals with access to phenotypic and genomic data, and an interest in genomic prediction or genome-wide association studies. These target audiences were introduced to the nature and scope of the project through summer courses and scientific publications that used the project deliverables, as well as word of mouth. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
The bioinformatics system is used for two main reasons. First, to obtain whole genome estimates of merit. Second, to detect genomic regions that harbor quantitative trait loci (OTL) that influence the trait being analyzed. Outcomes and impacts of the research vary according to these two reasons for using the system. Change in knowledge has occurred from determining the accuracy of whole genome predictions and from identifying the location and size of OTL. Changes in action have been stimulated by knowledge of the accuracy of genomic predictions. This project has led to the implementation of Bayesian methodologies across a range of traits in beef cattle that led to the commercialization of genomic technologies in the beef industry by Pfizer and Merial, in addition to supporting analysis for research collaborators not funded by this grant. The web system has over 360 users that represent a variety of animal, plant and human researchers. It has led to numerous new collaborations and forms the analytical backbone for other NIFA-funded projects such as Feed Efficiency. Analytical methods developed in this project have supported USDA-CSREES-NRI projects with Dr. Milt Thomas (2008-2011: Identification of molecular markers to improve fertility in beef cattle), Dr. Diane Spurlock (2008-2011: Genetic regulation and genomic selection of energy balance traits in dairy cattle), Dr. Curt Van Tassell (2009-65205-05635: Implementation of whole genome selection in the US dairy and beef cattle industries), Dr. Joan Lunney (2009-2012: Characterization of host factors that contribute to PRRS disease resistance and susceptibility) and USDA-NIFA Dr. Jack Dekkers (2010-2012: Use of high-density SNP genotyping for genetic improvement of livestock), Dr. Nick Gabler (2010-2013: The physiological basis of differences in efficiency, metabolism and energy partitioning between lines of pigs selected for residual feed intake), Dr. Jerry Taylor (2011- 680004-30214: National program for genetic improvement of feed efficiency in beef cattle). The web interface has also supported analyses for many other USDA projects at various institutions without formal collaboration (e.g. Balschek et al, JAS, and other publications under review). The web system and software has also been used by Aviagen, Hy-Line, KeyGene, Livestock Improvement Corporation, Newsham, Pioneer Hi-Bred, PIC, and other animal and plant seedstock companies.

Publications

• Garrick, D.J., L.H. Baumgard, and H.L. Neibergs. 2012. Genomic analysis of data from physiological studies. Invited review. Journal of Dairy Science, 95:499-507.
• Wolc, A., J. Arango, P. Settar, J.E. Fulton, N.P. OSullivan, R. Preisinger, D. Habier, R.L. Fernando, D.J. Garrick, W.G. Hill, and J.C.M. Dekkers. 2012. Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens. Animal Genetics, doi 10.1111/j.1365-2052.2012.02381.x.
• Resende, M.F.R., P. Munoz, M.D.V. Resende, D.J. Garrick, R.L. Fernando, J.M. Davis, E.J. Jokela, T.A. Martin, G.F. Peter, M. Kirst. 2012. Accuracy of genomic selection models in Loblolly pine. Genetics, doi: 10.1534/genetics.111.137026.
• Duan, Q., R.G. Tait, M.S. Mayes, D.J. Garrick, Q. Liu, A.L. Van Eenennaam, R.G. Mateescu, D.L. Van Overbeke, A.J. Garmyn, D.C. Beitz, and J.M. Reecy. 2012. Genetic polymorphisms in bovine transferrin receptor 2 (TFR2) and solute carrier family 40 (iron-regulated transporter) member 1 (SLC40A1) gene and their associations with beef iron content. Animal Genetics. Doi:10.1111/j.1365-2052.2011.02224.x.
• Peters, S.O., K. Kizilkaya, D.J. Garrick, R.L. Fernando, J.M. Reecy, R.L. Weaber, G.A. Silver, and M.G. Thomas. 2012. Bayesian genome-wide association analyses of growth and yearling ultrasound measures of carcass traits in Brangus heifers. Journal of Animal Science, doi:10.2527/jas.2011-4507.
• Weber, K.L., D.J. Drake, J.F. Taylor, D.J. Garrick, L.A. Kuehn, R.M. Thallman, R.D. Schnabel, W.M. Snelling, E.J. Pollak, and A.L. Van Eenennaam. 2012. The accuracies of DNA-based estimates of genetic merit derived from Angus- or multi-breed beef cattle training populations. Journal of Animal Science doi:10.2527/jas.2011-5020.
• Saatchi, M., R.D. Schnabel, M.M. Rolf, J.F. Taylor, and D.J. Garrick. 2012. Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genetics Selection Evolution, 44:38.
• Peters, S.O., K. Kizilkaya, D.J. Garrick, R.L. Fernando, J.M. Reecy, R.L. Weaber, G.A. Silver, and M.G. Thomas. 2012. Heritability and Bayesian genome-wide association study of first service conception and pregnancy in Brangus heifers. Journal of Animal Science, doi:10.2527/jas.2012-5580.
• Schurink, A., A. Wolc, B.J. Ducro, K. Frankena, D.J. Garrick, J.C.M. Dekkers, J.A.M van Arendonk. 2012. Genome-wide association study of insect bite hypersensitivity in two horse populations in the Netherlands. Genetics Selection Evolution 2012, 44:31.
• Sun, X., L. Qu, R.L. Fernando, D.J. Garrick, and J.C.M Dekkers. 2012. A fast EM algorithm for BayesA-like prediction of genomic breeding values. PLoS One 10.1371/journal.pone.0049157

Progress 01/01/11 to 12/31/11

Outputs
OUTPUTS: This research has developed the software products available at bigs.ansci.iastate.edu for genomic analyses. It has contributed to training at undergraduate, graduate and post-doctoral levels. Post-doctoral Fellows: Dr. David Habier (method development and programming), Dr. Kadir Kizilkaya (categorical methods and estimation of degrees of freedom, scale and pi), Dr. Joseph Abraham (programming and software testing), Dr. Mahdi Saatchi (applications to beef cattle). See publications for more details. It has been the basis for teaching genomic prediction courses. Graduates, post-doctoral fellows and researchers have been trained in genomic prediction methodology through the web interface to this software, and through taught courses capped at 40-70 students including; 1-10 June 2009. Ames, Iowa. Dekkers, Fernando and Garrick; 1 - 5 February 2010, Armidale, Australia. Fernando and Garrick; 14-18 June 2010, Ames, Iowa. Dekkers, Fernando and Garrick; 20-24 June 2011, Davos Switzerland. Fernando and Garrick; 27 June- 1 July 2011, Wageningen, The Netherlands. Fernando and Garrick. PARTICIPANTS: Post-doctoral Fellows: Dr. David Habier (method development and programming), Dr. Kadir Kizilkaya (categorical methods and estimation of degrees of freedom, scale and pi), Dr. Joseph Abraham (programming and software testing), Dr. Mahdi Saatchi (applications to beef cattle). See publications for more details. Undergraduates: Rex Fernando. Analytical methods developed in this project have supported USDA-CSREES-NRI projects with Dr. Milt Thomas (2008-2011: Identification of molecular markers to improve fertility in beef cattle), Dr. Diane Spurlock (2008-2011: Genetic regulation and genomic selection of energy balance traits in dairy cattle), Dr. Curt Van Tassell (2009-65205-05635: Implementation of whole genome selection in the US dairy and beef cattle industries), Dr. Joan Lunney (2009-2012: Characterization of host factors that contribute to PRRS disease resistance and susceptibility) and USDA-NIFA Dr. Jack Dekkers (2010-2012: Use of high-density SNP genotyping for genetic improvement of livestock), Dr. Nick Gabler (2010-2013: The physiological basis of differences in efficiency, metabolism and energy partitioning between lines of pigs selected for residual feed intake), Dr. Jerry Taylor (2011-680004-30214: National program for genetic improvement of feed efficiency in beef cattle). The web interface has also supported analyses for many other USDA projects at various institutions without formal collaboration (e.g. Balschek et al, JAS, and other publications under review). Further, these software have supported Pfizer Animal Genetics and Merial Igenity in their development of genomic prediction services for beef cattle. The web system and software has also been used by Aviagen, Hy-Line, KeyGene, Livestock Improvement Corporation, Newsham, Pioneer Hi-Bred, PIC, and other animal and plant seedstock companies. Graduates, post-doctoral fellows and researchers have been trained in genomic prediction methodology through the web interface to this software, and through taught courses capped at 40-70 students including 1-10 June 2009. Ames, Iowa. Dekkers, Fernando and Garrick. 1 - 5 February 2010, Armidale, Australia. Fernando and Garrick. 14-18 June 2010, Ames, Iowa. Dekkers, Fernando and Garrick. 20-24 June 2011, Davos Switzerland. Fernando and Garrick. 27 June- 1 July 2011, Wageningen, The Netherlands. Fernando and Garrick TARGET AUDIENCES: Target audiences include any individuals with access to phenotypic and genomic data, and an interest in genomic prediction or genome-wide association studies. These target audiences were introduced to the nature and scope of the project through summer courses and scientific publications that used the project deliverables, as well as word of mouth. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
This project has led to the implementation of Bayesian methodologies across a range of traits in beef cattle that led to the commercialization of genomic technologies in the beef industry by Pfizer and Merial, in addition to supporting analysis for research collaborators not funded by this grant. The web system has over 230 users that represent a variety of animal, plant and human researchers. It has led to numerous new collaborations and forms the analytical backbone for other NIFA-funded projects such as Feed Efficiency. Analytical methods developed in this project have supported USDA-CSREES-NRI projects with Dr. Milt Thomas (2008-2011: Identification of molecular markers to improve fertility in beef cattle), Dr. Diane Spurlock (2008-2011: Genetic regulation and genomic selection of energy balance traits in dairy cattle), Dr. Curt Van Tassell (2009-65205-05635: Implementation of whole genome selection in the US dairy and beef cattle industries), Dr. Joan Lunney (2009-2012: Characterization of host factors that contribute to PRRS disease resistance and susceptibility) and USDA-NIFA Dr. Jack Dekkers (2010-2012: Use of high-density SNP genotyping for genetic improvement of livestock), Dr. Nick Gabler (2010-2013: The physiological basis of differences in efficiency, metabolism and energy partitioning between lines of pigs selected for residual feed intake), Dr. Jerry Taylor (2011-680004-30214: National program for genetic improvement of feed efficiency in beef cattle). The web interface has also supported analyses for many other USDA projects at various institutions without formal collaboration (e.g. Balschek et al, JAS, and other publications under review). Further, these software have supported Pfizer Animal Genetics and Merial Igenity in their development of genomic prediction services for beef cattle. The web system and software has also been used by Aviagen, Hy-Line, KeyGene, Livestock Improvement Corporation, Newsham, Pioneer Hi-Bred, PIC, and other animal and plant seedstock companies.

Publications

• Garrick, D.J. 2010. Milestones in genetic prediction for applied livestock improvement. Proc. N.Z. Soc. Anim. Prod. 70:104-112.
• Kizilkaya K, Tait RG, Garrick DJ, Fernando RL, Reecy JM. Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models. BMC Proc. 2011 Jun 3;5 Suppl 4:S22.
• Sun X, Habier D, Fernando RL, Garrick DJ, Dekkers JC. Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods. BMC Proc. 2011 May 27;5 Suppl 3:S13.
• Downey, ED, EC Conrad, KK Lintz, JF Ridpath, RG Tait, DJ Garrick, JM Reecy. 2011. BVDV titer level relationships with growth and body composition traits in Angus calves. J. Anim. Sci./J. Dairy Sci.
• Boddicker, N, DJ Garrick, JM Reecy, B Rowland, MF Rothschild, JP Steibel, JK Lunney, JCM Dekkers. 2011. Genetic parameters and markers associated with viremia and growth in pigs infected with porcine reproductive and respiratory virus. J. Anim. Sci./J. Dairy Sci.
• Sun, X, RL Fernando, DJ Garrick, JCM Dekkers. 2011. An iterative approach for efficient calculation of breeding values and genome-wide association analysis using weighted genomic BLUP. J. Anim. Sci./J. Dairy Sci.
• Pollak, E.J. 2011. Genomics and the global livestock industries. Proceedings of Applied Genomics for Sustainable Livestock Breeding. Sir Mark Oliphant Conferences, May 2-5, 2011, Melbourne, Australia. p. 9-10.
• Wolc, A, J Arango, P Settar, JE Fulton, NP OSullivan, R Preisinger, D Habier, R Fernando, DJ Garrick, SJ Lamont, JCM. Dekkers. 2011. Persistence of accuracy of estimated breeding values in layers using marker and pedigree based relationship matrices. J. Anim. Sci./J. Dairy Sci.
• Wang, C, D Habier, A Kranis, KA Watson, S Avendano, DJ Garrick, R Fernando, SJ Lamont, JCM Dekkers. 2011. Accuracy of genomic EBV using an evenly spaced, low-density SNP panel in broiler chickens. J. Anim. Sci./J. Dairy Sci.
• Reecy, JM, RG Tait, DL VanOverbeke, AJ Garmyn, RG Mateescu, AL Van Eenennaam, Q Duan, Q Liu, JP Schoonmaker, ME Drewnoski, DC Beitz, K Kizilkaya, RL Fernando, DJ Garrick. 2011. Use of genomics to improve the fatty acid composition of meat. J. Anim. Sci./J. Dairy Sci.
• Lunney, JK, N Boddicker, JCM Dekkers, DJ Garrick, S Abrams, JP Steibel, JM Reecy, E Fritz, M Rothschild, M Kerrigan, B Trible, RRR Rowland. 2011. PRRS host genetics consortium: Background and current progress. J. Anim. Sci./J. Dairy Sci.
• Boddicker, N, DJ Garrick, JM Reecy, R Rowland, MF Rothschild, JP Steibel, JK Lunney, JCM Dekkers. 2011. A major QTL for response to porcine reproductive and respiratory syndrome virus in pigs. J. Anim. Sci./J. Dairy Sci.
• Saatchi, M, DJ Garrick, J Ward. 2011. Accuracy of genomic breeding values in Hereford beef cattle using k-means clustering method for cross-validation. J. Anim. Sci./J. Dairy Sci.
• Kizilkaya, K, RL Fernando, D Garrick. 2011. Accuracy of genomic prediction using a threshold model to analyze categorical traits in purebred and crossbred beef cattle populations. J. Anim. Sci./J. Dairy Sci.
• Weber, KL, and AL Van Eenennaam. 2011. Independent assessment of commercial DNA tests for beef cattle production traits. J. Anim. Sci. Vol. 89, E-Suppl. 1/J. Dairy Sci. Vol. 94, E-Suppl. 1
• Weber, K., Bennett, G.L., Keele, J.W., Snelling, W.M., Thallman, R.M., Van Eenennaam, A., Kuehn, L.A. 2011. Genomic selection in beef cattle: Training and validation in multibreed populations. PAG-XIX(P514).
• Snelling, W.M., Cushman, R.A., Bennett, G.L., Keele, J.W., Kuehn, L.A., Mcdaneld, T.G., Thallman, R.M., Thomas, M.G. 2011. How SNP chips will advance our knowledge of factors controlling puberty and aid in selecting replacement females. J. Anim. Sci. 89(E-Supplement 1):494. Abstract # 534.

Progress 01/01/10 to 12/31/10

Outputs
OUTPUTS: The BIGS research project has generated a product in the form of a web-based system (bigs.ansci.iastate.edu) for the analysis of bioinformatics data for whole genome analysis. The product has gone through successive iterations over the period 1 Jan 2010 to 31 Dec 2010 with additional features being added in each iteration. Events including workshops explaining the underlying technology and oral presentations communicating the use of the website has increased exposure of the product to national and international audiences of possible users. Potential participants have been able to register on the website and gain access to the analytical system. Registered users have been involved in activities that comprise the analysis of their data. Services to this user group have included consulting and tutoring on the use of the system and the interpretation of the results. Most of the users have not yet published the results of their endeavors. The publication list includes some of the collaborators. PARTICIPANTS: Two week-long summer courses were taught by the PI and coPI(s). Garrick and Fernando taught one course in Australia, and Dekkers, Fernando and Garrick taught one course at Iowa State University. There are now 167 users that have registered to undertake analyses on the web site. These include a number of partner organizations that have been using the system to develop services for external or internal use within their respective livestock organizations. TARGET AUDIENCES: Target audiences for genomic prediction include beef cattle breed associations and genomics companies that market services to bull breeders. A symposium was held in December for the purposes of updating the beef cattle breed associations with the results to date and encouraging their use of the technology for national cattle evaluation. Target audiences for QTL location include researchers studying the genetics and genomics of particular traits. Summer short course and numerous oral and poster presentations at scientific conferences have been used to ensure widespread awareness of the products arising from this grant. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
The bioinformatics system is used for two main reasons. First, to obtain whole genome estimates of merit. Second, to detect genomic regions that harbor quantitative trait loci (QTL) that influence the trait being analyzed. Outcomes and impacts of the research vary according to these two reasons for using the system. Change in knowledge has occurred from determining the accuracy of whole genome predictions and from identifying the location and size of QTL. Changes in action have been stimulated by knowledge of the accuracy of genomic predictions. These actions primarily must be taken by the beef breed associations who are responsible for the commercial implementation of services to their breeders. The American Angus Association has now adopted routine genomic prediction, with the prediction equations being developed from use of the analytical methods developed in this project. The American Hereford Association is planning a change in action for 2011. Change in conditions have been required for the breed associations to implement genomic technologies, including infrastructural and database changes. Changes in actions have also resulted from scientists using the system in that the identification of QTL has led them to propose new or modified research activities.

Publications

• Thomas, MG, SO Peters, K Kizilkaya, DJ Garrick, RL Fernando, JM Reecy, Z-L Hu, FD Schilkey, RL Weaber, GA Silver. 2010. QTL Hypothalamic-transcriptome, and candidate gene query suggest bovine chromosome 2 influences heifer pregnancy. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P537.
• Peters, SO, K Kizilkaya, DJ Garrick, RL Fernando, JM Reecy, RL Weaber, GA Silver, MG Thomas. 2010. Bayesian QTL inference from whole genome growth and carcass analyses in Brangus heifers. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P558.
• Fan, Bin, SK Onteru, DJ Garrick, KJ Stalder, MF Rothschild. 2010. Whole-genome association analysis on body composition and feet and leg structure soundness traits in the pig. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P605.
• Onteru, SK, B Fan, DJ Garrick, KJ Stalder, MF Rothschild. 2010. Genome-wide analyses for pig reproductive traits using the porcineSNP60 beadchip. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P615.
• Gorbach, DM, W Cai, JCM Dekkers, JM Young, DJ Garrick, RL Fernando, MF Rothschild. 2010. Genetic analysis of residual feed intake and its components based on the porcineSNP60 beadchip. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P614.
• Rothschild, MF, DM Gorbach, B Fan, SK Onteru, Z-Q Du, DJ Garrick, RL Fernando, KJ Stalder, JCM Dekkers. 2010. Applications of new porcine genomic tools to trait discovery and understanding of genomic architecture. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, W614.
• Downey, ED, EC Conrad, RG Tait, DJ Garrick, JM Reecy. 2010. Whole genome analysis of response to BVDV2 vaccinations in Angus calves using Bayesian models. International Symposium on Animal Genomics for Animal Health, Paris, France, May 2010. 2-PA27.
• Kizilkaya, K, RG Tait, DJ Garrick, RL Fernando, JM Reecy. 2010. Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models. International Symposium on Animal Genomics for Animal Health, Paris, France, May 2010. 2-PA28.
• Cleveland, M., S. Forni, D. Garrick, N. Deeb. 2010. Prediction of genomic breeding values in a commercial pig population. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:266.
• Onteru, S., B. Fan, D. Garrick, K. Stalder, M Rothschild. 2010. Whole-genome association analyses for sow lifetime production, reproduction and structural soundness traits using the porcineSNP60 beadchip. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:273.
• Habier, D., R.L. Fernando, K. Kizilkaya, D.J. Garrick. 2010. Extension of the Bayesian alphabet for genomic selection. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:468.
• Garrick, D.J. 2010. Consequence of genomic prediction in beef cattle. Proceedings of the Interbull international workshop on genomic information in genetic evaluations. Bulletin No. 41, ISSN 1011-6079. http://www.interbull.org/index.phpoption=com_content&view=article&id =78:interbull-bulletin-41&catid=4:organization&Itemid=112
• Garrick, D.J. 2010. The nature scope and impact of some whole genome analyses in beef cattle. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:23.
• Gorbach, D., W. Cai, J. Dekkers, J. Young, D. Garrick, R. Fernando, M. Rothschild. 2010. Large-scale SNP association analyses of residual feed intake and its component traits in pigs.. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:265.
• M. MacNeil, S. Northcutt, R. Schnabel, D. Garrick, B. Woodward, J. Taylor. 2010. Genetic correlations between carcass traits and molecular breeding values in Angus cattle. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:482.
• Reecy, J., R. Tait, D. Van Overbeke, A. Garmyn, R. Mateescu, A. Van Eenennaam, Q. Duan, Q. Liu, J. Schoonmaker, M. Drewnowski, D. Beitz, K. Kizilkaya, R. Fernando, D. Garrick. 2010. Use of genomics to improve healthfulness and quality of meat. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:53.
• Wolc, A., C. Stricker, J. Arango, P. Settar, J. E. Fulton, N. OSullivan, D. Habier, R. L. Fernando, D. J. Garrick, S. J. Lamont, J. C. M. Dekkers. 2010. Breeding value prediction for production traits in layers using pedigree and marker based methods. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:552.
• Habier, D, R. Fernando, D. Garrick. 2010. A combined strategy to infer high-density SNP haplotypes in large pedigrees. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 9:915.
• Dekkers, JCM, C Stricker, RL Fernando, DJ Garrick, SJ Lamont, NP OSullivan, JE Fulton, J Arango, P Settar, A Kranis, J MacKay, A Koerhuis, R Preisinger. 2010. Implementation of genomic selection in egg-layer chickens. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, W493.
• Hu, Zhiliang, Rex Fernando, DJ Garrick, JM Reecy. 2010. SNPlotz: A genome plot tool for SNP association studies. Abstracts of the Plant & Animal Genome XVIII conference, San Diego, P851.
• Garrick, D.J. 2010. Genomic analysis of data from physiological studies. Symposium on bridging the gap between physiology and genomics. J. Anim. Sci. Vol. 88, E-suppl. 2/ J. Dairy Sci. Vol. 93, E-Suppl. Abstract 289.
• Woodward, BW, DJ Garrick, RL Fernando, S Northcutt, B Bowman, SW Bauck, RD Schnabel, JF Taylor. 2010. Development and validation of an Angus-specific IGENITY profile for marbling, backfat thickness, hot carcass weight, ribeye area, yearling weight, and heifer pregnancy based on a whole genome scan. J. Anim. Sci. Vol. 88, E-suppl. 2/ J. Dairy Sci. Vol. 93, E-Suppl. Abstract 788.
• Peters, SO, K Kizilkaya, DJ Garrick, RL Fernando, JM Reecy, Z-L Hu, RL Weaber, GA Silver, MG Thomas. 2010. Bayesian QTL inference and gene identification for first service conception rate in Brangus heifers. . J. Anim. Sci. Vol. 88, E-suppl. 2/ J. Dairy Sci. Vol. 93, E-Suppl. Abstract 924.

Progress 01/01/09 to 12/31/09

Outputs
OUTPUTS: Research activities have been undertaken in relation to the three research objectives. Objective 3 involves extending the methodology for Bayesian training and prediction beyond a single-trait with normally distributed genomic and residual effects, and efficiently implementing appropriate methodologies in computer software. GenSel software has been developed to implement models that fit arbitrary fixed effects representing covariates or class effects, genomic effects at each locus as random normally-distributed effects with unknown variance and uncorrelated normally-distributed residual effects with unknown variance. Assuming an inverse chi-squared prior for the variances of genomic effects, Bayes C0 estimates a common variance and Bayes A estimates a separate variance for every genomic effect. Mixture models that assume some known fraction pi of the genotypes have no influence on the trait have been implemented in the context of homogeneous (Bayes C) and heterogeneous (Bayes B) variances of genomic effects. These mixture models have been extended to simultaneously estimate the fraction pi from the data (e.g. Bayes Cpi). All the models have been extended to estimate from the data the scale parameter for the inverse chi-squared prior for variances of genomic effects. Preliminary research has been undertaken to extend these models to relax the assumption of residual effects or genomic effects conditional on their variance following students-t rather than normal distributions. The degrees of freedom for the students-t distribution are estimated from the data. Preliminary research has been undertaken to extend these analyses to ordered categorical traits by estimating underlying thresholds and corresponding liability scores. Objective 1 has been advanced through subcontract with CalPoly San Luis Obisbo and has led to the development of prototype and production web sites for online access to the software developed in Objective 3. The web interface includes facilities to request a password protected usercode, upload genotype and phenotype files, run genomic analyses, download and view results in tabular or graphical form. Events have been held to explain the underlying basis of the methods used in objective 3 and access to the web interface developed in objective 1. These have included a 2 week workshop at Iowa State University held in Summer 2009. The principal product developed from the research is the web interface to the analytical machinery and this can be accessed at bigs.ansci.iastate.edu PARTICIPANTS: Garrick - PI - Oversight of the project (all objectives) and methods researcher in objective 3. Fernando - co-PI - Provides technical oversight in the theoretical development and software implementation of the statistical methods in objective 3. Dekkers - co-PI - Assists in technical developments and interpretation of results. Kizilkaya - post-doc - Prototyping extensions to the statistical methods Habier - post-doc - Extending methods in objectives 2 and 3. US-MARC subcontract - no progress to date on objective 2. CalPoly San Luis Obispo subcontract - oversees the day-to-day activities in objective 1. TARGET AUDIENCES: Principal target audience includes those researchers with access to genomic information who can benefit from applying our statistical methods to the analysis of beef cattle data. This includes researchers involved in USDA and other federally-funded projects, researchers in land-grant universities, researchers in companies developing tests for industry. Efforts to attract and influence the target audience are through scientific publications, conference presentations and short courses, as well as through word of mouth. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
The project has led to change in knowledge with respect to the value of genomic information in predicting the genetic merit of individuals. This has come about from application of the web-interface product developed in objective 1 leveraging the research actions that enhanced analytical software in objective 3. Some of these results have been published and many have been communicated in a variety of forms. The beef cattle industry has begun to change its actions as a result of this project, because two companies marketing gene tests (Merial Igenity & Pfizer Animal Genetics) have initiated plans to market new genomic-based products and services that are a direct result of applying the software developed in objective 3. The American Angus Association has modified its national cattle evaluation system to include information on genomic tests in the national ranking of their cattle.

Publications

• Garrick, D.J. 2009. The nature and scope of some whole genome analyses in U.S. beef cattle. Proceedings of the Beef Improvement Federation 41st Annual Research Symposium and Annual Meeting. 41:92-102.
• Garrick, D.J. Comment on review of herdtesting in New Zealand. 2009. New Zealand Herd improvement database review committee.
• Stricker, C., J. Moll, D.J. Garrick, and R.L. Fernando. 2009. First results on genome-wide genetic evaluation in Swiss dairy cattle. Abstract, EAAP 60th Annual Meeting, Barcelona http://www.eaap.org/Barcelona/Book_Abstracts.pdf
• Kizilkaya, K. R.L. Fernando and D.J. Garrick. 2009. Simulation of genomic selection in a crossbred beef cattle population. J. Anim. Sci. 87:E-suppl 3, abstract 38.
• Garrick, D.J. 2009. The nature and scope of some whole genome analyses in beef cattle. http://www.bifconference.com/bif2009/ab_g1_4_garrick.html
• Stranden, I., and D.J. Garrick. 2009. Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci. 92:2971-2975.
• Kizilkaya, K., R. L. Fernando, and D. J. Garrick. 2009. Genomic prediction of simulated multi-breed and purebred performance using observed 50k SNP genotypes. J. Anim. Sci. jas.2009-2064v1-20092064.
• Garrick, D.J., J.F. Taylor, and R.L. Fernando. 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution, 41:55.
• Garrick, D.J. 2009. New genomic advances in dairy and beef cattle improvement. Proceedings of the Ensminger School held in Costa Rica Feb 11-13, 2009, Retos y alternativas para la produccion animal tropical. Pp 89-105.
• Dekkers, J.C.M., C. Stricker, R. L. Fernando, D. J. Garrick, S. J. Lamont, N. P. O'Sullivan, J. E. Fulton, J. Arango, P. Settar, A. Kranis, J. McKay, A. Koerhuis, R. Preisinger. 2009. Implementation of genomic selection in egg layer chickens. Late breaking abstract of the joint ASAS/ADSA/CSAS meeting held in Montreal, Canada, 11 July, 2009.
• Nkrumah, J.D., D.J. Garrick, R.L. Fernando, S. Northcutt, B. Bowman, B.W. Woodward, S.W. Bauck, D. Vasco, T.M. Taxis, M.M. Rolf, J.E. Decker, J.W. Kim, M.C. McClure, S.D. McKay, R.D. Schnabel, J.F. Taylor. 2009. Alternative methods for selecting SNP panels to predict marbling in Angus cattle. . Late breaking abstract of the joint ASAS/ADSA/CSAS meeting held in Montreal, Canada, 11 July, 2009.
• Garrick, D.J. 2009. Bioinformatics requirements to apply whole-genome prediction in livestock. J. Anim. Sci. Vol. 87, E-Suppl. 2/J. Dairy Sci. Vol. 92, E-Suppl. 1, abstract 203.
• Garrick, D.J. 2009. Recent developments in genetic evaluation tools. J. Anim. Sci. Vol. 87, E-Suppl. 2/J. Dairy Sci. Vol. 92, E-Suppl. 1, abstract 624.
• Garrick, D.J. 2009. Bayesian analyses of whole-genome data. Australasian conference on statistical methods for genomic data analysis. http://genepi.qimr.edu.au/genomics/sept30_files/Page671.htm
• Onteru, SK, B Fan, D Garrick, K J Stalder and MF Rothschild. 2009. Whole-genome analyses for pig reproductive traits using the PorcineSNP60 BeadChip. Pig Genome III Conference held in Sanger Institute, UK, 2-4 November 2009.
• Fan, B, SK Onteru, D Garrick, K J Stalder and MF Rothschild. 2009. A genome-wide association study for pig production and feet and leg structure traits using the PorcineSNP60 BeadChip. Pig Genome III Conference held in Sanger Institute, UK, 2-4 November 2009.
• Gorbach, DM, W Cai, JCM Dekkers, JM Young, DJ Garrick, RL Fernando, and MF Rothschild. 2009. Whole-genome analyses for genes associated with residual feed intake and related traits utilizing the PorcineSNP60 BeadChip. Pig Genome III Conference held in Sanger Institute, UK, 2-4 November 2009.