Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T20:26:42.709Z Has data issue: false hasContentIssue false

Analysis of single nucleotide polymorphisms variation associated with important economic and computed tomography measured traits in Texel sheep

Published online by Cambridge University Press:  17 October 2017

D. Garza Hernandez
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK
S. Mucha
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, 60-637 Poznan, Poland
G. Banos
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK Roslin Institute, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, Scotland, UK
K. Kaseja
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK
K. Moore
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK
N. Lambe
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK
J. Yates
Affiliation:
British Texel Sheep Society, National Agricultural Centre, Stoneleigh Park, Kenilworth, Warwickshire, CV8 2LG, UK
L. Bunger*
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, UK
*
Get access

Abstract

Sheep are an important part of the global agricultural economy. Growth and meat production traits are significant economic traits in sheep. The Texel breed is the most popular terminal sire breed in the UK, mainly selected for muscle growth and lean carcasses. This is a study based on a genome-wide association approach that investigates the links between some economically important traits, including computed tomography (CT) measurements, and molecular polymorphisms in UK Texel sheep. Our main aim was to identify single nucleotide polymorphisms (SNP) associated with growth, carcass, health and welfare traits of the Texel sheep breed. This study used data from 384 Texel rams. Data comprised ten traits, including two CT measured traits. The phenotypic data were placed in four categories: growth traits, carcass traits, health traits and welfare traits. De-regressed estimated breeding values (EBV) for these traits together with sire genotypes derived with the Ovine 50 K SNP array of Illumina were jointly analysed in a genome wide association analysis. Eight novel chromosome-wise significant associations were found for carcass, growth, health and welfare traits. Three significant markers were intronic variants and the remainder intergenic variants. This study is a first step to search for genomic regions controlling CT-based productivity traits related to body and carcass composition in a terminal sire sheep breed using a 50 K SNP genome-wide array. Results are important for the further development of strategies to identify causal variants associated with CT measures and other commercial traits in sheep. Independent studies are needed to confirm these results and identify candidate genes for the studied traits.

Type
Research Article
Copyright
© The Animal Consortium 2017 

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.)

Footnotes

a

Present address: Universidad Autónoma de Nuevo León (UANL), Pedro de Alba S/N, Ciudad Universitaria, San Nicolás de los Garza 66451, N.L., México.

References

Astle, W and Balding, D 2009. Population Structure and cryptic relatedness in genetic association studies. Statistical Science 24, 451471.Google Scholar
Atlija, M, Arranz, JJ, Martinez-Valladares, M and Gutierrez-Gil, B 2016. Detection and replication of QTL underlying resistance to gastrointestinal nematodes in adult sheep using the ovine 50K SNP array. Genetics Selection Evolution 48, 4.CrossRefGoogle ScholarPubMed
Aulchenko, YS, Ripke, S, Isaacs, A and van Duijn, CM 2007. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 12941296.Google Scholar
Beh, KJ, Hulme, DJ, Callaghan, MJ, Leish, Z, Lenane, I, Windon, RG and Maddox, JF 2002. A genome scan for quantitative trait loci affecting resistance to Trichostrongylus colubriformis in sheep. Animal Genetics 33, 97106.Google Scholar
Bolormaa, S, Hayes, BJ, van der Werf, JH, Pethick, D, Goddard, ME and Daetwyler, HD 2016. Detailed phenotyping identifies genes with pleiotropic effects on body composition. BMC Genomics 17, 224.Google Scholar
Brown, DJ 2007. Variance components for lambing ease and gestation length in sheep. In Proceedings of the 17th Conference of the Association for the Advancement of Animal Breeding and Genetics, 23–26 September 2007, Armidale, Australia, pp. 268–271.Google Scholar
Bünger, L, Macfarlane, JM, Lambe, NR, Conington, J, Mclean, KA, Moore, K, Glasbey, CA and Simm, G 2011. Use of X-ray computed tomography (CT) in UK sheep production and breeding. In CT Scanning – Techniques and Applications (ed. K Subburaj), pp. 329–348. InTech, Rijeka, Croatia.Google Scholar
Cavanagh, CR, Jonas, E, Hobbs, M, Thomson, PC, Tammen, I and Raadsma, HW 2010. Mapping Quantitative Trait Loci (QTL) in sheep. III. QTL for carcass composition traits derived from CT scans and aligned with a meta-assembly for sheep and cattle carcass QTL. Genetics, Selection, Evolution 42, 36.Google Scholar
Donaldson, CL, Lambe, NR, Maltin, CA, Knott, S and Bunger, L 2014. Effect of the Texel muscling QTL (TM-QTL) on spine characteristics in purebred Texel lambs. Small Ruminant Research 117, 3440.Google Scholar
Fikse, WF and Banos, G 2001. Weighting factors of sire daughter information in international genetic evaluations. Journal of Dairy Science 84, 17591767.Google Scholar
Georges, M 2007. Mapping, fine mapping, and molecular dissection of quantitative trait Loci in domestic animals. Annual Review of Genomics and Human Genetics 8, 131162.Google Scholar
Gianola, D, Fariello, MI, Naya, H and Schon, CC 2016. Genome-wide association studies with a genomic relationship matrix: a case study with wheat and arabidopsis. G3 (Bethesda) 6, 32413256.Google Scholar
Goh, L, Yap, VB, Amos, C, Wu, X, Broderick, P, Gorlov, I, Gu, J, Eisen, T, Dong, Q, Zhang, Q, Gu, X, Vijayakrishnan, J, Sullivan, K, Matakidou, A, Wang, Y, Mills, G, Doheny, K, Tsai, Y, Chen, W, Shete, S, Spitz, M, Houlston, R, Barrett, J, Hansoul, S, Nicolae, D, Cho, J, Duerr, R, Rioux, J, Brant, S, Silverberg, M, Taylor, K, Barmada, M, Bitton, A, Dassopoulos, T, Datta, L, Green, T, Griffiths, A, Kistner, E, Murtha, M, Regueiro, M, Rotter, J, Schumm, L, Steinhart, A, Targan, S, Xavier, R, Libioulle, C, Sandor, C, Lathrop, M, Belaiche, J, Dewit, O, Gut, I, Heath, S, Laukens, D, Mni, M, Rutgeerts, P, Gossum, AV, Zelenika, D, Franchimont, D, Hugot, J, Vos, Md, Vermeire, S, Louis, E, Belgian-French, I, Cardon, L, Anderson, C, Drummond, H, Nimmo, E, Ahmad, T, Prescott, N, Onnie, C, Fisher, S, Marchini, J, Ghori, J, Bumpstead, S, Gwilliam, R, Tremelling, M, Deloukas, P, Mansfield, J, Jewell, D, Satsangi, J, Mathew, C, Parkes, M, Georges, M, Daly, M, Bernardo, MD, Crowther-Swanepoel, D, Broderick, P, Webb, E, Sellick, G, Wild, R, Sullivan, K, Vijayakrishnan, J, Wang, Y, Pittman, A, Sunter, N, Hall, A, Dyer, M, Matutes, E, Dearden, C, Mainou-Fowler, T, Jackson, G, Summerfield, G, Harris, R, Pettitt, A, Hillmen, P, Allsup, D, Bailey, J, Pratt, G, Pepper, C, Fegan, C, Allan, J, Catovsky, D, Houlston, R, Frayling, T, Nair, R, Duffin, K, Helms, C, Ding, J, Stuart, P, Goldgar, D, Gudjonsson, J, Li, Y, Tejasvi, T, Feng, B, Ruether, A, Schreiber, S, Weichenthal, M, Gladman, D, Rahman, P, Schrodi, S, Prahalad, S, Guthery, S, Fischer, J, Liao, W, Kwok, P, Menter, A, Lathrop, G, Wise, C, Begovich, A, Voorhees, J, Elder, J, Krueger, G, Bowcock, A, Abecasis, G, Bakker, Pd, Ferreira, M, Jia, X, Neale, B, Raychaudhuri, S, Voight, B, Feingold, E, Diao, G, Lin, D, Labbe, A, Wormald, H, Peng, B, Yu, R, Dehoff, K, Amos, C, Zhang, F, Liu, J, Chen, J, Deng, H, Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, M, Bender, D, Maller, J, Sklar, P, Bakker, Pd, Daly, M and Sham, P 2009. Effects of normalization on quantitative traits in association test. BMC Bioinformatics 10, 415.Google Scholar
Hayes, B and Goddard, ME 2010. Genome-wide association and genomic selection in animal breeding. Genome 53, 876883.Google Scholar
Hopkins, A and Lobley, M 2009. A scientific review of the impact of UK ruminant livestock on greenhouse gas emissions. CRPR research report. Centre for Rural Policy Research, University of Exeter, Exeter, UK.Google Scholar
Hu, ZL, Park, CA, Wu, XL and Reecy, JM 2013. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Research 41, D871D879.CrossRefGoogle ScholarPubMed
Jairath, L, Dekkers, JC, Schaeffer, LR, Liu, Z, Burnside, EB and Kolstad, B 1998. Genetic Evaluation for Herd Life in Canada. Journal of Dairy Science 81, 550562.CrossRefGoogle ScholarPubMed
Jiang, Y, Xie, M, Chen, W, Talbot, R, Maddox, JF, Faraut, T, Wu, C, Muzny, DM, Li, Y, Zhang, W, Stanton, JA, Brauning, R, Barris, WC, Hourlier, T, Aken, BL, Searle, SM, Adelson, DL, Bian, C, Cam, GR, Chen, Y, Cheng, S, DeSilva, U, Dixen, K, Dong, Y, Fan, G, Franklin, IR, Fu, S, Fuentes-Utrilla, P, Guan, R, Highland, MA, Holder, ME, Huang, G, Ingham, AB, Jhangiani, SN, Kalra, D, Kovar, CL, Lee, SL, Liu, W, Liu, X, Lu, C, Lv, T, Mathew, T, McWilliam, S, Menzies, M, Pan, S, Robelin, D, Servin, B, Townley, D, Wang, W, Wei, B, White, SN, Yang, X, Ye, C, Yue, Y, Zeng, P, Zhou, Q, Hansen, JB, Kristiansen, K, Gibbs, RA, Flicek, P, Warkup, CC, Jones, HE, Oddy, VH, Nicholas, FW, McEwan, JC, Kijas, JW, Wang, J, Worley, KC, Archibald, AL, Cockett, N, Xu, X, Wang, W and Dalrymple, BP 2014. The sheep genome illuminates biology of the rumen and lipid metabolism. Science 344, 11681173.Google Scholar
Jones, HE, Lewis, RM, Young, MJ and Wolf, BT 2002. The use of X-ray computer tomography for measuring the muscularity of live sheep. Animal Science 75, 387399.Google Scholar
Lewis, R 2004. Genetic lessons from the United Kingdom. Paper presented at the Virginia-North Carolina Shepherds’ Symposium, 9–10 January 2004, Blacksburg, VA, USA, pp. 24–34.Google Scholar
Lidauer, MMK, Mantysaari, E and Stranden, I 2011. MiX99: solving large mixed model equations manual. MTT, Jokioinen.Google Scholar
Macfarlane, JM, Lewis, RM, Emmans, GC, Young, MJ and Simm, G 2006. Predicting carcass composition of terminal sire sheep using X-ray computed tomography. Animal Science 82, 289300.CrossRefGoogle Scholar
Macfarlane, JM, Lewis, RM, Emmans, GC, Young, MJ and Simm, G 2009. Predicting tissue distribution and partitioning in terminal sire sheep using x-ray computed tomography. J Anim Sci 87, 107118.Google Scholar
Marshall, K, Maddox, JF, Lee, SH, Zhang, Y, Kahn, L, Graser, HU, Gondro, C, Walkden-Brown, SW and Van Der Werf, JHJ 2009. Genetic mapping of quantitative trait loci for resistance to Haemonchus contortus in sheep. Animal Genetics 40, 262272.Google Scholar
Matika, O, Riggio, V, Anselme-Moizan, M, Law, AS, Pong-Wong, R, Archibald, AL and Bishop, SC 2016. Genome-wide association reveals QTL for growth, bone and in vivo carcass traits as assessed by computed tomography in Scottish Blackface lambs. Genetics Selection Evolution 48, 11.CrossRefGoogle ScholarPubMed
Pollott, GE 2014. The breeding structure of the British sheep industry 2012. Defra, London, UK.Google Scholar
R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/ Google Scholar
Raadsma, HW, Thomson, PC, Zenger, KR, Cavanagh, C, Lam, MK, Jonas, E, Jones, M, Attard, G, Palmer, D and Nicholas, FW 2009. Mapping quantitative trait loci (QTL) in sheep. I. A new male framework linkage map and QTL for growth rate and body weight. Genetic Selection Evolution 41, 34.Google Scholar
Royston, P 1995. Remark AS R94: a remark on algorithm AS 181: The W-test for normality. Journal of the Royal Statistical Society. Series C (Applied Statistics) 44, 547551.Google Scholar
Silva, SR 2016. Use of ultrasonographic examination for in vivo evaluation of body composition and for prediction of carcass quality of sheep. Small Ruminant Research 152, 144157.Google Scholar
Skinner, ME, Uzilov, AV, Stein, LD, Mungall, CJ and Holmes, IH 2009. JBrowse: a next-generation genome browser. Genome Research 19, 16301638.Google Scholar
Thye, T, Vannberg, FO, Wong, SH, Owusu-Dabo, E, Osei, I, Gyapong, J, Sirugo, G, Sisay-Joof, F, Enimil, A, Chinbuah, MA, Floyd, S, Warndorff, DK, Sichali, L, Malema, S, Crampin, AC, Ngwira, B, Teo, YY, Small, K, Rockett, K, Kwiatkowski, D, Fine, PE, Hill, PC, Newport, M, Lienhardt, C, Adegbola, RA, Corrah, T, Ziegler, A, Morris, AP, Meyer, CG, Horstmann, RD and Hill, AVS 2010. Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11.2. Nature genetics 42, 739741.Google Scholar
Verbeek, E, Kanis, E, Bett, RC and Kosgey, IS 2011. Optimisation of breeding schemes for litter size, lambing interval, body weight and parasite resistance for sheep in Kenya. Livestock Research for Rural Development 23, Article #187.Google Scholar
Walling, GA, Visscher, PM, Wilson, AD, McTeir, BL, Simm, G and Bishop, SC 2004. Mapping of quantitative trait loci for growth and carcass traits in commercial sheep populations. Journal of Animal Science 82, 22342245.Google Scholar
Zhang, L, Liu, J, Zhao, F, Ren, H, Xu, L, Lu, J, Zhang, S, Zhang, X, Wei, C, Lu, G, Zheng, Y and Du, L 2013. Genome-wide association studies for growth and meat production traits in sheep. PLoS One 8, e66569.Google Scholar
Supplementary material: File

Garza Hernandez et al supplementary material 1

Garza Hernandez et al supplementary material

Download Garza Hernandez et al supplementary material 1(File)
File 16.6 KB