Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T02:02:03.408Z Has data issue: false hasContentIssue false
  • English
  • Français

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Published online by Cambridge University Press:  29 October 2010

D. P. Berry ,
K. G. Meade ,
M. P. Mullen ,
S. Butler ,
M. G. Diskin ,
D. Morris  and
C. J. Creevey
D. P. Berry*
Affiliation:
Animal and Bioscience Research Department, Teagasc, Moorepark, Co. Cork, Ireland
K. G. Meade
Affiliation:
Animal and Bioscience Research Department, Teagasc, Grange, Co. Meath, Ireland
M. P. Mullen
Affiliation:
Animal and Bioscience Research Department, Teagasc, Athenry, Co. Galway, Ireland
S. Butler
Affiliation:
Animal and Bioscience Research Department, Teagasc, Moorepark, Co. Cork, Ireland
M. G. Diskin
Affiliation:
Animal and Bioscience Research Department, Teagasc, Athenry, Co. Galway, Ireland
D. Morris
Affiliation:
Animal and Bioscience Research Department, Teagasc, Athenry, Co. Galway, Ireland
C. J. Creevey
Affiliation:
Animal and Bioscience Research Department, Teagasc, Grange, Co. Meath, Ireland
*
†E-mail: [email protected]
Get access

Abstract

Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the ‘black box’ underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the ‘omic’ disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual’s true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists – and they will also be discussed.

Keywords

‘omic’systems biologybreedingapplication
Type
Review
Information
animal , Volume 5 , Issue 4 , 23 February 2011 , pp. 493 - 505
Copyright
Copyright © The Animal Consortium 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altschul, SF, Gish, W, Miller, W, Myers, EW, Lipman, DJ 1990. Basic local alignment search tool. Journal of Molecular Biology 215, 403–410.CrossRefGoogle ScholarPubMed
Anderson, AL, Hunter, CL 2006. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Molecular and Cellular Proteomics 5, 573–588.CrossRefGoogle ScholarPubMed
Bagnicka, E, Siadkowska, E, Strzałkowska, N, Zelazowska, B, Flisikowski, K, Krzyżewski, J, Zwierzchowski, L 2010. Association of polymorphisms in exons 2 and 10 of the insulin-like growth factor 2 (IGF2) gene with milk production traits in Polish Holstein-Friesian cattle. Journal of Dairy Research 77, 37–42.CrossRefGoogle ScholarPubMed
Baxevanis, AD 2008. Searching NCBI databases using Entrez. Current Protocals In Bioinformatics Chapter 1, Unit 1.3.CrossRefGoogle Scholar
Berry, DP 2008. Improving feed efficiency in cattle with residual feed intake. In Recent advances in animal nutrition (ed. PC Garnsworthy and J Wiseman), pp. 67–99. University of Nottingham Press, Nottingham, UK.Google Scholar
Berry, DP, Buckley, F, Dillon, P, Evans, RD, Rath, M, Veerkamp, RF 2003. Genetic relationships among body condition score, body weight, milk yield and fertility in dairy cows. Journal of Dairy Science 86, 2193–2204.CrossRefGoogle ScholarPubMed
Bowman, JC 1974. An introduction to animal breeding. Edward Arnold Ltd, London, UK.Google Scholar
Bulmer, MG 1980. The mathematical theory of quantitative genetics. Clarendon, Oxford.Google Scholar
Callinan, PA, Feinberg, AP 2006. The emerging science of epigenomics. Human Molecular Genetics 15, R95–R101.CrossRefGoogle ScholarPubMed
Calus, MPL 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157–164.CrossRefGoogle ScholarPubMed
Chang, WC, Li, CW, Chen, BS 2005. Quantitative inference of dynamic regulatory pathways via microarray data. BMC Bioinformatics 6, 44.CrossRefGoogle ScholarPubMed
Chicurel, M 2002. Bioinformatics: bringing it all together. Nature 419, 751–753.CrossRefGoogle ScholarPubMed
de Roos, APW, Hayes, BJ, Spelman, RJ, Goddard, ME 2008. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.CrossRefGoogle ScholarPubMed
Dekkers, JCM 2004. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science 82, E313–E328.Google ScholarPubMed
Evans, AC, Forde, N, O’Gorman, GM, Zielak, AE, Lonergan, P, Fair, T 2008. Use of microarray technology to profile gene expression patterns important for reproduction in cattle. Reproduction on Domestic Animals 43, 359–367.CrossRefGoogle ScholarPubMed
Falk, R, Ramström, M, Ståhl, S, Hober, S 2007. Approaches for systematic proteome exploration. Biomolecular Engineering 24, 155–168.CrossRefGoogle ScholarPubMed
Flisikowski, K, Adamowicz, T, Strabel, T, Jankowski, T, Switonski, M, Zwierzchowski, L 2007. An InDel polymorphism in exon 6 of IGF2 associated with the breeding value of Polish Holstein-Friesian bulls. Biochemical Genetics 45, 139–143.CrossRefGoogle ScholarPubMed
Fraga, MF, Ballestar, E, Paz, MF, Ropero, S, Setien, F, Ballestar, ML, Heine-Suñer, D, Cigudosa, JC, Urioste, M, Benitez, J, Boix-Chornet, M, Sanchez-Aguilera, A, Ling, C, Carlsson, E, Poulsen, P, Vaag, A, Stephan, Z, Spector, TD, Wu, Y-Z, Plass, C, Esteller, M 2005. Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences of the United States of America 102, 10604–10609.CrossRefGoogle ScholarPubMed
Gebert, C, Wrenzycki, C, Herrmann, D, Gröger, D, Reinhardt, R, Hajkova, P, Lucas-Hahn, A, Carnwath, J, Lehrach, H, Niemann, H 2006. The bovine IGF2 gene is differentially methylated in oocyte and sperm DNA. Genomics 88, 222–229.CrossRefGoogle ScholarPubMed
Georges, M, Nielsen, D, Mackinnon, M, Mishra, A, Okimoto, R, Pasquino, AT, Sargeant, LS, Sorensen, A, Steele, M, Zhao, X, Womack, JE, Hoeschele, I 1995. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139, 907–920.CrossRefGoogle ScholarPubMed
Goodall, JJ, Schmutz, SM 2007. IGF2 gene characterization and association with rib eye area in beef cattle. Animal Genetics 38, 154–161.CrossRefGoogle ScholarPubMed
Graham, DR, Elliott, ST, Van Eyk, JE 2005. Broad-based proteomic strategies: a practical guide to proteomics and functional screening. Journal of Physiology 563, 1–9.CrossRefGoogle ScholarPubMed
Gudbjartsson, DF, Walters, GB, Thorleifsson, G, Stefansson, H, Halldorsson, BV, Zusmanovich, P, Sulem, P, Thorlacius, S, Gylfason, A, Steinberg, S, Helgadottir, A, Ingason, A, Steinthorsdottir, V, Olafsdottir, EJ, Olafsdottir, GH, Jonsson, T, Borch-Johnsen, K, Hansen, T, Andersen, G, Jorgensen, T, Pedersen, O, Aben, KK, Witjes, JA, Swinkels, DW, den Heijer, M, Franke, B, Verbeek, ALM, Becker, DM, Yanek, LR, Becker, LC, Tryggvadottir, L, Rafnar, T, Gulcher, T, Kiemeney, LA, Kong, A, Thorsteinsdottir, U, Stefansson, U 2008. Many sequence variants affecting diversity of adult human height. Nature Genetics 40, 609–615.CrossRefGoogle ScholarPubMed
Hawkins, R, Hon, GC, Ren, B 2010. Next-generation genomics: an integrative approach. Nature Reviews 11, 476–486.CrossRefGoogle ScholarPubMed
Hurd, PJ, Nelson, CJ 2009. Advantages of next-generation sequencing versus the microarray in epigenetic research. Briefings in Functional Genomics and Proteomics 8, 174–183.CrossRefGoogle ScholarPubMed
Hutchison, CA 3rd 2007. DNA sequencing: bench to bedside and beyond. Nucleic Acids Research 35, 6227–6237.CrossRefGoogle ScholarPubMed
Ideker, T, Thorsson, V, Ranish, JA, Christmas, R, Buhler, J, Eng, JK, Bumgarner, R, Goodlett, DR, Aebersold, R, Hood, L 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934.CrossRefGoogle ScholarPubMed
IHGSC 2004. Finishing the euchromatic sequence of the human genome-international human genome sequencing consortium. Nature 431, 931–945.CrossRefGoogle Scholar
Jansen, RC, Nap, JP 2001. Genetical genomics: the added value from segregation. Trends in Genetics 17, 388–391.CrossRefGoogle ScholarPubMed
Jensen, LJ, Julien, P, Kuhn, M, von Mering, C, Muller, J, Doerks, T, Bork, P 2008. eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Research 36, D250–D254.CrossRefGoogle ScholarPubMed
Jensen, LJ, Kuhn, M, Stark, M, Chaffron, S, Creevey, C, Muller, J, Doerks, T, Julien, P, Roth, A, Simonovic, M, Bork, P, von Mering, C 2009. STRING 8 – a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37, D412–D416.CrossRefGoogle ScholarPubMed
Jin, C, Lan, H, Attie, AD, Churchill, GA, Bulutuglo, D, Yandell, BS 2004. Selective phenotyping for increased efficiency in genetic mapping studies. Genetics 168, 2285–2293.CrossRefGoogle ScholarPubMed
Jirtle, RL, Skinner, MK 2007. Environmental epigenomics and disease susceptibility. Nature Reviews Genetics 8, 253–262.CrossRefGoogle ScholarPubMed
Kadarmideen, HN 2008. Genetical systems biology in livestock: application to gonadotrophin releasing hormone and reproduction. IET Systems Biology 2, 423–441.CrossRefGoogle ScholarPubMed
Kahraman, A, Avramov, A, Nashev, LG, Popov, D, Ternes, R, Pohlenz, H-D, Weiss, B 2005. PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics. Bioinformatics 21, 418–420.CrossRefGoogle ScholarPubMed
Kanehisa, M, Goto, S 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28, 27–30.CrossRefGoogle ScholarPubMed
Kitano, H 2002. Computational systems biology. Nature 420, 206–210.CrossRefGoogle ScholarPubMed
Kraly, JR, Holcomb, RE, Guan, Q, Henry, C 2009. Review: microfluidic applications in metabolomics and metabolomic profiling. Analytica Chimica Acta 653, 23–35.CrossRefGoogle Scholar
Krishna, RG, Wold, F 1998. Post translational modifications. In Proteins: analysis and design (ed. RH Angeletti), pp. 121–206. Academic Press, San Diego, CA.CrossRefGoogle Scholar
Kuhn, M, von Mering, C, Campillos, M, Jensen, LJ, Bork, P 2008. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Research 36, D684–D688.CrossRefGoogle ScholarPubMed
Langmead, B, Schatz, MC, Lin, J, Pop, M, Salzberg, SL 2009a. Searching for SNPs with cloud computing. Genome Biology 10, R134.CrossRefGoogle ScholarPubMed
Langmead, B, Trapnell, C, Pop, M, Salzberg, SL 2009b. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10, R25.CrossRefGoogle ScholarPubMed
Lettre, G, Jackson, AU, Gieger, C, Schumacher, FR, Berndt, SI, Sanna, S, Eyheramendy, S, Voight, BF, Butler, JL, Guiducci, C, Illig, T, Hackett, R, Heid, IR, Jacobs, KB, Lyssenko, V, Uda, M, The Diabetes Genetics Initiative, FUSION, KORA, The Prostate, Lung Colorectal and Ovarian Cancer Screening Trial, The Nurses’ Health Study, SardiNIA Boehnke, M, Chanock, SJ, Groop, LC, Hu, FB, Isomaa, B, Kraft, P, Peltonen, L, Salomaa, V, Schlessinger, D, Hunter, DJ, Hayes, RB, Abecasis, GR, Wichmann, H-E, Mohlke, KL, Hirschhorn, JN 2008. Identification of ten loci associated with height highlights new biological pathways in human growth. Nature Genetics 40, 584–591.CrossRefGoogle ScholarPubMed
Lippolis, JD, Reinhardt, TA 2008. Centennial paper. Proteomics in animal science. Journal of Animal Science 86, 2430–2441.CrossRefGoogle ScholarPubMed
Liu, Y, Qin, X, Song, XZ, Jiang, H, Shen, Y, Durbin, KJ, Lien, S, Kent, MP, Sodeland, M, Ren, Y, Zhang, L, Sodergren, E, Havlak, P, Worley, KC, Weinstock, GM, Gibbs, RA 2009. Bos taurus genome assembly. BMC Genomics 10, 180.CrossRefGoogle ScholarPubMed
Lynn, DJ, Winsor, GL, Chan, C, Richard, N, Laird, MR, Barsky, A, Gardy, JL, Roche, FM, Chan, TH, Shah, N, Lo, R, Naseer, M, Que, J, Yau, M, Acab, M, Tulpan, D, Whiteside, MD, Chikatamarla, A, Mah, B, Munzner, T, Hokamp, K, Hancock, RE, Brinkman, FS 2008. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Molecular and Systems Biology 4, 218.CrossRefGoogle ScholarPubMed
Mackay, TF 2001. Quantitative trait loci in Drosophila. Nature Review Genetics 2, 11–20.CrossRefGoogle ScholarPubMed
Maher, B 2008. The case of the missing heritability. Nature 456, 18–21.CrossRefGoogle ScholarPubMed
Maningat, PD, Sen, P, Rijnkels, M, Sunehag, AL, Hadsell, DL, Bray, M, Haymond, MW 2009. Gene expression in the human mammary epithelium during lactation: the milk fat globule transcriptome. Physiology Genomics 37, 12–22.CrossRefGoogle ScholarPubMed
Mardis, ER 2008. The impact of next-generation sequencing technology on genetics. Trends in Genetics 24, 133–141.CrossRefGoogle ScholarPubMed
Matlin, AJ, Clark, F, Smith, CWJ 2005. Understanding alternative splicing: towards a cellular code. Nature Review in Molecular and Cell Biology 6, 386–398.CrossRefGoogle ScholarPubMed
Meade, KG, Gormley, E, Park, SD, Fitzsimons, T, Rosa, GJ, Costello, E, Keane, J, Coussens, PM, MacHugh, DE 2006. Gene expression profiling of peripheral blood mononuclear cells (PBMC) from Mycobacterium bovis infected cattle after in vitro antigenic stimulation with purified protein derivative of tuberculin (PPD). Veterinary Immunology Immunopathology 113, 73–89.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Morozova, O, Marra, MA 2008. Applications of next-generation sequencing technologies in functional genomics. Genomics 92, 255–264.CrossRefGoogle ScholarPubMed
Mortazavi, A, Williams, BA, McCue, K, Schaeffer, L, Wold, B 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621–628.CrossRefGoogle ScholarPubMed
Ogden, ER, Weigel, K 2007. Can you shrinkwrap a cow? Protections available for the intellectual property of the animal breeding industry. Animal Genetics 38, 647–654.CrossRefGoogle ScholarPubMed
Perecin, F, Méo, SC, Yamazaki, W, Ferreira, CR, Merighe, GKF, Meirelles, FV, Garcia, JM 2009. Imprinted gene expression in in vivo- and in vitro-produced bovine embryos and chorio-allantoic membranes. Genetics and Molecular Research 8, 76–85.CrossRefGoogle ScholarPubMed
Pryce, JE, Bolormaa, S, Chamberlain, AJ, Bowman, PJ, Savin, K, Goddard, ME, Hayes, BJ 2010. A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. Journal of Dairy Science 93, 3331–3345.CrossRefGoogle ScholarPubMed
Purcell, S, Cherny, SS, Sham, PC 2003. Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150.CrossRefGoogle ScholarPubMed
Raes, J, Bork, P 2008. Molecular eco-systems biology: towards an understanding of community function. Nature Reviews Microbiology 6, 693–699.CrossRefGoogle ScholarPubMed
Rendel, J, Robertson, A 1950. Estimation of genetic gain in milk yield by selection in a closed herd of dairy cattle. Journal of Genetics 50, 1–8.CrossRefGoogle Scholar
Rogers, S, Girolami, M, Kolch, W, Waters, KM, Liu, T, Thrall, B, Wiley, HS 2008. Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics 24, 2894–2900.CrossRefGoogle ScholarPubMed
Ron, M, Weller, JI 2007. From QTL to QTN identification in livestock – winning by points rather than knock-out: a review. Animal Genetics 38, 429–439.CrossRefGoogle ScholarPubMed
Royer, C 1999. Protein-protein interactions, outline of the thermodynamic and structural principles governing the ways that proteins interact with other proteins. Previously Published in the Biophysics Textbook Online (BTOL). Retrieved October, 2009, from http://www.biophysics.org/education/croyer.pdf.Google Scholar
Sanger, F, Air, GM, Barrell, BG, Brown, NL, Coulson, AR, Fiddes, JC, Hutchison, CA, Slocombe, PM, Smith, M 1977. Nucleotide sequence of bacteriophage X174 DNA. Nature 265, 687–695.CrossRefGoogle ScholarPubMed
Schadt, EE, Monks, SA, Drake, TA, Lusis, AJ, Che, N, Colinayo, V, Ruff, TG, Milligan, SB, Lamb, JR, Cavet, G, Linsley, PS, Mao, M, Stoughton, RB, Friend, SH 2003. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302.CrossRefGoogle ScholarPubMed
Schaeffer, LR 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.CrossRefGoogle ScholarPubMed
Sellner, EM, Kim, JW, McClure, MC, Taylor, KH, Schnabel, RD, Taylor, JF 2007. Board-invited review: applications of genomic information in livestock. Journal of Animal Science 85, 3148–3158.CrossRefGoogle ScholarPubMed
Shendure, J, Ji, H 2008. Next-generation DNA sequencing. Nature Biotechnology 26, 1135–1145.CrossRefGoogle ScholarPubMed
Shulaev, V 2006. Metabolomics technology and bioinformatics. Briefings in Bioinformatics 7, 128–139.CrossRefGoogle ScholarPubMed
Smith, KD, Bolouri, H 2005. Dissecting innate immune responses with the tools of systems biology. Current Opinion in Immunology 17, 49–54.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dardenne, P, Dehareng, F, Lognay, G, Veselko, D, Marlier, M, Bertozzi, C, Mayeres, P, Gengler, N 2006. Estimating fatty acid content in cow milk using mid-infrared spectrometry. Journal of Dairy Science 89, 3690–3695.CrossRefGoogle ScholarPubMed
Stranger, BE, Forrest, MS, Dunning, M, Ingle, CE, Beazley, C, Thorne, N, Redon, R, Bird, CP, de Grassi, A, Lee, C, Tyler-Smith, C, Carter, N, Scherer, SW, Tavare, S, Deloukas, P, Hurles, ME, Dermitzakis, ET 2007. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853.CrossRefGoogle ScholarPubMed
Tatusov, RL, Koonin, EV, Lipman, DJ 1997. A genomic perspective on protein families. Science 278, 631–637.CrossRefGoogle ScholarPubMed
Tatusov, RL, Fedorova, ND, Jackson, JD, Jacobs, AR, Kiryutin, B, Koonin, EV, Krylov, DM, Mazumder, R, Mekhedov, SL, Nikolskaya, AN, Rao, BS, Smirnov, S, Sverdlov, AV, Vasudevan, S, Wolf, YI, Yin, JJ, Natale, DA 2003. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41.CrossRefGoogle ScholarPubMed
Tian, Q, Stepaniants, SB, Mao, M, Weng, L, Feetham, MC, Doyle, MJ, Yi, EC, Dai, H, Thorsson, V, Eng, J, Goodlett, D, Berger, JP, Gunter, B, Linseley, PS, Stoughton, RB, Aebersold, RB, Collins, SJ, Hanlon, WA, Hood, LE 2004. Integrated genomic and proteomic analyses of gene expression in mammalian cells. Molecular and Cellular Proteomics 3, 960–969.CrossRefGoogle ScholarPubMed
Trapnell, C, Pachter, L, Salzberg, SL 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111.CrossRefGoogle ScholarPubMed
Tveden-Nyborga, PY, Alexopoluosa, NI, Cooney, MA, French, AJ, Tecirliogluc, RT, Holland, MK, Thomsena, PD, D’Cruzb, NT 2008. Analysis of the expression of putatively imprinted genes in bovine peri-implantation embryos. Theriogenology 70, 1119–1128.CrossRefGoogle Scholar
van Ommen, B, Stierum, R 2002. Nutrigenomics: exploiting systems biology in the nutrition and health arena. Current Opinion in Biotechnology 13, 517–521.CrossRefGoogle ScholarPubMed
Van Tassell, CP, Smith, TPL, Matukumalli, LK, Taylor, JF, Schnabel, RD, Lawley, CT, Haudenschild, CD, Moore, SS, Warren, WC, Sonstegard, TS 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature Methodology 5, 247–252.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF, Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Visscher, PM 2008. Sizing up human height variation. Nature Genetics 40, 489–490.CrossRefGoogle ScholarPubMed
Visscher, PM, Hill, WG, Wray, NR 2008. Heritability in the genomics era-concepts and misconceptions. Nature Reviews Genetics 9, 255–266.CrossRefGoogle ScholarPubMed
Voelkerding, KV, Dames, SA, Durtschi, JD 2009. Next-generation sequencing: from basic research to diagnostics. Clinical Chemistry 55, 641–658.CrossRefGoogle ScholarPubMed
Walsh, CT 2006. Posttranslational modification of proteins expanding nature’s inventory. Robert and Company, CO., USA.Google Scholar
Waters, KM, Pounds, JG, Thrall, BD 2006. Data merging for integrated microarray and proteomic analysis. Briefings in Functional Genomics and Proteomics 5, 261–272.CrossRefGoogle ScholarPubMed
Weedon, MN, Lango, H, Lindgren, CM, Wallace, C, Evans, DM, Mangino, M, Freathy, RM, Perry, JRB, Stevens, S, Hall, AS, Samani, NJ, Shields, B, Prokopenko, I, Farrall, M, Dominiczak, A, Johnson, T, Bergmann, S, Beckmann, JS, Vollenweider, P, Waterworth, DM, Mooser, V, Palmer, CAN, Morris, AD, Ouwehand, WH, Caulfield, M, Munroe, PB, Hattersley, AT, McCarthy, MI, Frayling, TM 2008. Genome-wide association analysis identifies 20 loci that influence adult height. Nature Genetics 40, 575–583.CrossRefGoogle ScholarPubMed
Weller, JI 1994. Economic aspects of animal breeding. Chapman & Hall, London.Google Scholar
Weller, JI 2009. Quantitative trait loci analysis in animals. CABI Publishing, London.CrossRefGoogle Scholar
Wilson, AG 2008. Epigenetic regulation of gene expression in the inflammatory response and relevance to common diseases. Journal of Periodontology 79, 1514–1519.CrossRefGoogle ScholarPubMed
Wishart, DS, Tzur, D, Knox, C, Eisner, R, Guo, AC, Young, N, Cheng, D, Jewell, K, Arndt, D, Sawhney, S, Fung, C, Nikolai, L, Lewis, M, Coutouly, M-A, Forsythe, I, Tang, P, Shrivastava, S, Jeroncic, K, Stothard, P, Amegbey, G, Block, D, Hau, DD, Wagner, J, Miniaci, J, Clements, M, Gebremedhin, M, Guo, N, Zhang, Y, Duggan, GE, MacInnis, GD, Weljie, AM, Dowlatabadi, R, Bamforth, F, Clive, D, Greiner, R, Li, L, Marrie, T, Sykes, BD, Vogel, HJ, Querengesser, L 2007. HMDB: the human metabolome database. Nucleic Acids Research 35, D521–D526.CrossRefGoogle ScholarPubMed
Xu, Z, Zou, F, Vision, TJ 2005. Improving quantitative trait loci mapping resolution in experimental crosses by the use of genotypically selected samples. Genetics 170, 401–408.CrossRefGoogle ScholarPubMed
Yates, JR, Ruse, CI, Nakorchevsky, A 2009. Proteomics by mass spectrometry: approaches, advances, and applications. Annual Review of Biomedical Engineering 11, 49–79.CrossRefGoogle ScholarPubMed
Zimin, AV, Delcher, AL, Florea, L, Kelley, DR, Schatz, MC, Puiu, D, Hanrahan, F, Pertea, G, Van Tassell, CP, Sonstegard, TS, Marcais, G, Roberts, M, Subramanian, P, Yorke, JA, Salzberg, SL 2009. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biology 10, R42.CrossRefGoogle ScholarPubMed