Breeding and genetics symposium: Really big data: Processing and analysis of very large data sets

Journal of Animal Science

Volumn 90, Issue 3, 2012, Pages 723-733

  Cole, J B ^a   Newman, S ^b   Foertter, F ^b   Aguilar, I ^c   Coffey, M ^d  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

^b GENUS PLC   (United States)

^c INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA INIA   (Uruguay)

^d SCOTTISH AGRICULTURAL COLLEGE   (United Kingdom)

Author keywords

Computation; Genetic evaluation; Genomic selection; Phenotype; Visualization

Indexed keywords

ANIMAL; ANIMAL IDENTIFICATION; ARTICLE; BIOLOGY; BREEDING; CATTLE; DATA MINING; METHODOLOGY; PEDIGREE; STATISTICAL ANALYSIS;

ANIMAL IDENTIFICATION SYSTEMS; ANIMALS; BREEDING; CATTLE; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATA MINING; PEDIGREE;

EID: 84857325909     PISSN: 00218812     EISSN: 15253163     Source Type: Journal    
DOI: 10.2527/jas.2011-4584     Document Type: Article

References (61)

1. ACM. 2011. CERN experiments generating one petabyte of data every second. Accessed Aug. 10, 2011. http://cacm.acm.org/ news/110048-cern-experiments-generating-one-petabyte-ofdata-every-second/fulltext.
2. Aggarwal, C. C., and P. S. Yu. 1998. Mining large datasets for association rules. Data Eng. Bull. 21:23-31.
3. Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta, and T. J. Lawlor. 2010. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93:743-752.
4. Aguilar, I., I. Misztal, A. Legarra, and S. Tsuruta. 2011a. Efficient computations of the genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet. 128:422-428.
5. Aguilar, I., I. Misztal, S. Tsuruta, G. R. Wiggans, and T. J. Lawlor. 2011b. Multiple trait genomic evaluation of conception rate in Holsteins. J. Dairy Sci. 94:2621-2624.
6. Banos, G., and M. P. Coffey. 2010. Genetic association between body energy measured throughout lactation and fertility in dairy cattle. Animal 4:189-199.
7. Berger, P., G. Luecke, and J. Hoekstra. 1989. Iterative algorithms for solving mixed model equations. J. Dairy Sci. 72:514-522.
8. Berry, D. P., F. Buckley, P. Dillo, R. D. Evans, M. Rath, and R. F. Veerkamp. 2003. Genetic relationships among body condition score, body weight, milk yield, and fertility in dairy cows. J. Dairy Sci. 86:2193-2204.
9. Calus, M. P. L., J. J. Windig, and R. F. Veerkamp. 2005. Associations among descriptors of herd management and phenotypic and genetic levels of health and fertility. J. Dairy Sci. 88:2178-2189.
10. Caroli, A. M., S. Chessa, and G. J. Erhardt. 2009. Invited review: Milk protein polymorphisms in cattle: Effect on animal breeding and human nutrition. J. Dairy Sci. 92:5335-5352.
11. Christensen, O. F., and M. Lund. 2010. Genomic prediction when some animals are not genotyped. Genet. Sel. Evol. 42:2 doi:10.1186/1297-9686-42-2.
12. Cole, J. B., D. J. Null, and L. R. Bacheller. 2008. A data exchange format and national database for producer-recorded health event data from on-farm management software. J. Dairy Sci. 91(E-Suppl. 1):2-3. (Abstr.)
13. Cole, J. B., and P. M. VanRaden. 2010. Visualization of results from genomic evaluations. J. Dairy Sci. 93:2727-2740.
14. Cole, J. B., and P. M. VanRaden. 2011. Use of haplotypes to estimate Mendelian sampling effects and selection limits. J. Anim. Breed. Genet. 128:446-455.
15. Cook, R. D., and S. Weisberg. 1999. Applied Regression Including Computing and Graphics. John Wiley and Sons, New York, NY.
16. Dechow, C. D., H. D. Norman, N. R. Zwald, C. M. Cowan, and O. M. Meland. 2008. Relationship between individual herd-heritability estimates and sire misidentification rate. J. Dairy Sci. 91:1640-1647.
17. Edwards, J. L., and P. R. Tozer. 2004. Using activity and milk yields as predictors of fresh cow disorders. J. Dairy Sci. 87:524-531.
18. Egger-Danner, C., K. Stock, J. Cole, A. Bradley, J. Pryce, N. Gengler, L. Andrews, and E. Strandberg. 2011. Registration of health traits-Strategies of phenotyping, aspects of data quality and possible benefits. 37th ICAR Session, Bourg-en-Bresse, France.
19. Everitt, B. S., S. Landau, and M. Leese. 2001. Cluster Analysis. 4th ed. Arnold Publishers, London, UK.
20. Fortes, M. R. S., A. Reverter, S. H. Nagaraj, Y. Zhang, N. N. Jonsson, W. Barris, S. Lehnert, G. B. Boe-Hansen, and R. J. Hawken. 2011. A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. J. Anim. Sci. 89:1669-1683.
21. Garrick, D. J. 2011. The nature, scope and impact of genomic prediction in beef cattle in the United States. Genet. Sel. Evol. 43:17. doi:10.1186/1297-9686-43-17.
22. Hand, D. J. 1997. Construction and Assessment of Classification Rules. John Wiley and Sons, New York, NY.
23. Harris, B. L., and D. L. Johnson. 2010. Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. J. Dairy Sci. 93:1243-1252.
24. Hayes, B. J., P. J. Bowman, A. J. Chamberlain, and M. E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92:433-443.
25. Henderson, C. R. 1975. Rapid method for computing the inverse of a relationship matrix. J. Dairy Sci. 58:1727-1730.
26. Henderson, C. R. 1984. Application of Linear Models in Animal Breeding. University of Guelph, Ontario, Canada.
27. Herd, R. M., and P. F. Arthur. 2009. Physiological basis for residual feed intake. J. Anim. Sci. 87:E64-E71.
28. IDC. 2011. The 2011 Digital Universe Study: Extracting Value from Chaos. Accessed Aug. 3, 2011. http://www.emc.com/collateral/ demos/microsites/emc-digital-universe-2011/index.htm.
29. Interbull. 2011. Interbull Routine Genetic Evaluation for Female Fertility Traits. Accessed July 28, 2011. http://www-interbull. slu.se/Female_fert/framesida-fert.htm.
30. Janesick, J. R. 2001. Scientific Charge-Coupled Devices. SPIE Press, Bellingham, WA.
31. Legarra, A., I. Aguilar, and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663.
32. Lin, J., and C. Dyer. 2010. Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies). Morgan and Claypool, San Rafael, CA.
33. Løvendahl, P., and M. G. G. Chagunda. 2010. On the use of physical activity monitoring for estrus detection in dairy cows. J. Dairy Sci. 93:249-259.
34. Maltecca, C., K. A. Gray, K. A. Weigel, J. P. Cassady, and M. Ashwell. 2011. A genome-wide association study of direct gestation length in US Holstein and Italian Brown populations. Anim. Genet. 42:585-591.
35. McParland, S., G. Banos, E. Wall, M. P. Coffey, H. Soyeurt, R. F. Veerkamp, and D. P. Berry. 2011. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. J. Dairy Sci. 94:3651-3661.
36. Misztal, I., I. Aguilar, S. Tsuruta, J. P. Sanchez, and B. Zumbach. 2010. Studies on heat stress in dairy cattle and pigs. Commun. No. 625 in Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany.
37. Misztal, I., and D. Gianola. 1987. Indirect solution of mixed model equations. J. Dairy Sci. 70:716-723.
38. Misztal, I., A. Legarra, and I. Aguilar. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci. 92:4648-4655.
39. Moore, J., P. L. Gay, K. Hogan, C. Lintott, C. Impey, and C. Watson. 2011. Facebooking citizen science with the Zooniverse. Bull. Am. Astronom. Soc. 43:158.13. (Abstr.)
40. NIH. 2007. Policy for Sharing of Data Obtained in NIH-Supported or Conducted Genome-Wide Association Studies (GWAS). Accessed Aug. 3, 2011. http://grants.nih.gov/grants/guide/ notice-files/NOT-OD-07-088.html.
41. Norman, H. D., L. G. Waite, G. R. Wiggans, and L. M. Walton. 1994. Improving accuracy of the United States genetics database with a new editing system for dairy records. J. Dairy Sci. 77:3198-3208.
42. NVIDIA Corporation. 2011. CUDA: Parallel Programming Made Easy. Accessed Aug. 9, 2011. http://www.nvidia.com/object/ cuda_home_new.html.
43. Patry, C., and V. Ducrocq. 2011. Accounting for genomic pre-selection in national BLUP evaluations in dairy cattle. Genet. Sel. Evol. doi:10.1186/1297-9686-43-30.
44. Reese, J. T., C. P. Childers, J. P. Sundaram, C. M. Dickens, K. L. Childs, D. C. Vile, and C. G. Elsik. 2010. Bovine Genome Database: Supporting community annotation and analysis of the Bos taurus genome. BMC Genomics 11:645. doi:10.1186/1471-2164-11-645.
45. Schaeffer, L. R., and B. W. Kennedy. 1986. Computing strategies for solving mixed model equations. J. Dairy Sci. 69:575-579.
46. Shaffer, J. P. 1995. Multiple hypothesis testing. Annu. Rev. Psychol. 46:561-584.
47. Soyeurt, H., F. Dehareng, N. Gengler, S. McParland, E. Wall, D. P. Berry, M. Coffey, and P. Dardenne. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657-1667.
48. Strandén, I., and M. Lidauer. 1999. Solving large mixed linear models using preconditioned conjugate gradient iteration. J. Dairy Sci. 82:2779-2787.
49. Tan, P.-N., M. Steinbach, and V. Kumar. 2005. Introduction to Data Mining. Addison-Wesley, New York, NY.
50. Tsuruta, S., I. Misztal, I. Aguilar, and T. J. Lawlor. 2011. Multipletrait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94:4198-4204.
51. Tsuruta, S., I. Misztal, and I. Stranden. 2001. Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications. J. Anim. Sci. 79:1166-1172.
52. Tufte, E. R. 1983. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT. Tufte, E. R. 2006. Beautiful Evidence. Graphics Press, Cheshire, CT.
53. VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423.
54. VanRaden, P. M., J. R. O'Connell, G. R. Wiggans, and K. A. Weigel. 2011. Genomic evaluations with many more genotypes. Genet. Sel. Evol. 43:10.
55. VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor, and F. S. Schenkel. 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.
56. Vitezica, Z. G., I. Aguilar, I. Misztal, and A. Legarra. 2011. Bias in genomic predictions for populations under selection. Genet. Res. (Camb.) 93:357-366.
57. von Ahn, L., B. Maurer, C. McMillen, D. Abraham, and M. Blum. 2008. reCAPTCHA: Human-based character recognition via Web security measures. Science 321:1465-1468.
58. Wickham, H. 2009. ggplot2: Elegant Graphics for Data Analysis. Springer, New York, NY.
59. Wiggans, G. R., T. A. Cooper, P. M. VanRaden, K. M. Olson, and M. E. Tooker. 2012. Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation. J. Dairy Sci. doi:10.3168/jds.2011-4985.
60. Wiggans, G. R., P. M. VanRaden, and T. A. Cooper. 2011. The genomic evaluation system in the United States: Past, present, future. J. Dairy Sci. 94:3202-3211.
61. Wiggans, G. R., P. M. VanRaden, and J. C. Philpot. 2003. Technical note: Detection and adjustment of abnormal test-day yields. J. Dairy Sci. 86:2721-2724.