How bioinformatics enables livestock applied sciences in the genomic era

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7409 LNBI, Issue , 2012, Pages 192-201

  Garcia, José Fernando ^a   Do Carmo, Adriana Santana ^a   Utsunomiya, Yuri Tani ^a   De Rezende Neves, Haroldo Henrique ^a   Carvalheiro, Roberto ^b   Van Tassell, Curtis ^c   Sonstegard, Tad Stewart ^c   Da Silva, Marcos Vinicius Gualberto Barbosa ^d  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^b GenSys Associated Consultants   (Brazil)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

^d EMBRAPA CERRADOS   (Brazil)

Author keywords

Bos taurus indicus; Genome Wide Association Study; Genomic Selection; Livestock; Signatures of Selection; Tropical Environment

Indexed keywords

BOS TAURUS; GENOME-WIDE ASSOCIATION; GENOMIC SELECTION; LIVESTOCK; SIGNATURES OF SELECTION; TROPICAL ENVIRONMENTS;

AGRICULTURE; GENES;

BIOINFORMATICS;

EID: 84865584538     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31927-3_17     Document Type: Conference Paper

References (36)

1. FAO (Food and Agriculture Organization of United Nations) (2012), http://www.fao.org/fileadmin/templates/wsfs/docs/expert-paper/ How-to-Feed-the-World-in-2050.pdf
2. Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E.: Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829 (2001)
3. Schaeffer, L.R.: Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123, 218-223 (2006)
4. Bovine Genome Sequencing and Analysis Consortium. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science 324(5926), 522-528 (2009)
5. Van Tassell, C.P., Smith, T.P., Matukumalli, L.K., Taylor, J.F., Schnabel, R.D., Lawley, C.T., Haudenschild, C.D., Moore, S.S., Warren, W.C., Sonstegard, T.S.: SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature Methods 5(3), 247-252 (2008)
6. Harris, B.L., Johnson, D.L., Spelman, R.J.: Genomic selection in New Zealand and the implications for national genetic evaluation. In: Proc. Interbull Meeting, Niagara Falls, Canada (2008)
7. VanRaden, P.M., Van Tassell, C.P., Wiggans, G.R., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Schenkel, F.S.: Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92, 16-24 (2009)
8. Moser, G., Tier, B., Crump, R.E., Khatkar, M.S., Raadsma, H.W.: A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet. Sel. Evol. 41, 56 (2009)
9. Luan, T., Woolliams, J.A., Lien, S., Kent, M., Svendsen, M., Meuwissen, T.H.: The accuracy of genomic selection in Norwegian Red cattle assessed by cross-validation. Genetics 183, 1119-1126 (2009)
10. Legarra, A., Robert-Granié, C., Croiseau, P., Guillaume, F., Fritz, S.: Improved Lasso for genomic selection. Genet. Res. 93(1), 77-87 (2011)
11. Frazer, K.A., et al.: Human genetic variation and its contribution to complex traits. Nature Reviews 10, 241-251 (2009)
12. Hirschhorn, J.N., Daly, M.J.: Genome-wide association studies for common diseases and complex traits. Nature Reviews 6(1), 95-198 (2005)
13. Pearson, T.A., Manolio, T.A.: How to Interpret a Genome-wide Association Study. Journal of American Medical Association 299(11), 1335-1344 (2008)
14. Cantor, R.M., et al.: Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application. The American Journal of Human Genetics 86, 6-22 (2010)
15. Chan, E.K.F., et al.: The combined effect of SNP-marker and phenotype attributes in genome-wide association studies. Animal Genetics 40, 149-156 (2009)
16. Chuang, H., et al.: A decade of Systems Biology. Reviews in Advance 20(1), 15-20 (2010)
17. Webber, C.: Functional Enrichment Analysis with Structural Variants: Pitfalls and Strategies. Cytogenet. Genome Res. 135, 277-285 (2011)
18. Fontanillo, C., et al.: Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms. PloS ONE 6(9), 242 (2011)
19. The gene ontology consortium. Gene ontology: tool for the unification of biology. Nature Genetics 25(1), 25-29 (2000)
20. Kanehisa, M., Goto, S.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28(1), 27-30 (2000)
21. Oleksyk, T.K., Smith, M.W., O'Brien, S.J.: Genome-wide scans for footprints of natural selection. Phil. Trans. R. Soc. B 365, 185-205 (2010)
22. Sabeti, P.C., et al.: Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913-918 (2007)
23. Tang, K., Thornton, K.R., Stoneking, M.: A new approach for using genome scans to detect recent positive selection in the human genome. PLoS Biology 5, e171 (2007)
24. Grossman, S.R., et al.: A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883-886 (2010)
25. Nielsen, R., Yang, Z.: Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148, 929-936 (1998)
26. Rubin, C.J., et al.: Whole-genome resequencing reveals loci under selection during chicken domestication. Nature 464, 587-593 (2010)
27. Tajima, F.: Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585-595 (1989)
28. Williamson, S.H., Hubisz, M.J., Clark, A.G., Payseur, B.A., Bustamante, C.D., Nielsen, R.: Localizing recent adaptive evolution in the human genome. PLoS Genet. 3, e90 (2007)
29. Weir, B.S., Cockerham, C.C.: Estimating F-Statistics for the analysis of population structure. Evolution 38(6), 1358-1370 (1984)
30. Sabeti, P.C., et al.: Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832-837 (2002)
31. Voight, B.F., Kudaravalli, S., Wen, X., Pritchard, J.K.: A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006)
32. Ong, R.T.-H., Teo, Y.Y.: varLD: a program for quantifying variation in linkage disequilibrium patterns between populations. Bioinformatics 26(9), 1269-1270 (2010)
33. Gautier, M., Naves, M.: Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology 20, 3128-3143 (2011)
34. Qanbari, S., et al.: Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle. BMC Genomics 12, 318 (2011)
35. Flori, L., et al.: The genome response to artificial selection: a case study in dairy cattle. PLoS ONE 4(8), e6595 (2009)
36. Kijas, J.W., et al.: Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology 10(2), e1001258 (2012)