참고문헌
- Daetwyler HD, Capitan A, Pausch H, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet 2014;46:858-65. https://doi.org/10.1038/ng.3034
- Xiong Q, Chai J, Xiong H, et al. Association analysis of HSP70 A1A haplotypes with heat tolerance in Chinese Holstein cattle. Cell Stress Chaperones 2013;18:711-8. https://doi.org/10.1007/s12192-013-0421-3
- Duan XD, Chen YY, Xu GZ, Zhang KC. Study on correlation of the temperature humidity index and physiological parameters of cows in Shanghai area. J Dairy Sci Technol 2011;1:39-41.
- Berman A. Invited review: Are adaptations present to support dairy cattle productivity in warm climates? J Dairy Sci 2011;94:2147-58. https://doi.org/10.3168/jds.2010-3962
- Boonkum W, Misztal I, Duangjinda M, et al. Genetic effects of heat stress on milk yield of Thai Holstein crossbreds. J Dairy Sci 2011;94:487-92. https://doi.org/10.3168/jds.2010-3421
- Wolfenson D, Roth Z, Meidan R. Impaired reproduction in heat-stressed cattle: basic and applied aspects. Anim Reprod Sci 2000;60-61:535-47. https://doi.org/10.1016/S0378-4320(00)00102-0
- Pryce JE, Bolormaa S, Chamberlain AJ, et al. A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. J Dairy Sci 2010;93:3331-45. https://doi.org/10.3168/jds.2009-2893
- Gaur GK, Kaushik SN, Garg RC. The Gir cattle breed of India - characteristics and present status. Anim Genet Resour Inf 2003;33:21-9. https://doi.org/10.1017/S1014233900001607
- Stankiewicz P, Lupski JR. Structural variation in the human genome and its role in disease. Annu Rev Med 2010;61:437-55. https://doi.org/10.1146/annurev-med-100708-204735
- Alkan C, Coe BP, Eichler EE. Genome structural variation discovery and genotyping. Nat Rev Genet 2011;12:363-76. https://doi.org/10.1038/nrg2958
- Huddleston J, Chaisson MJP, Meltz Steinberg KM, et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res 2017;27:677-85. https://doi.org/10.1101/gr.214007.116
- Medvedev P, Stanciu M, Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods 2009;6:S13-20. https://doi.org/10.1038/nmeth.1374
- Currall BB, Chiang C, Talkowski ME, Morton CC. Mechanisms for Structural Variation in the Human Genome. Curr Genet Med Rep 2013;1:81-90. https://doi.org/10.1007/s40142-013-0012-8
- Zhao P, Li J, Kang H, et al. Structural variant detection by largescale sequencing reveals new evolutionary evidence on breed divergence between Chinese and European pigs. Sci Rep 2016;6:Article number 18501.
- Jiang J, Wang J, Wang H, et al. Global copy number analyses by next generation sequencing provide insight into pig genome variation. BMC Genomics 2014;15:593. https://doi.org/10.1186/1471-2164-15-593
- Hou Y, Liu GE, Bickhart DM, et al. Genomic characteristics of cattle copy number variations. BMC Genomics 2011;12:127. https://doi.org/10.1186/1471-2164-12-127
- Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet 2006;7:85-97. https://doi.org/10.1038/nrg1767
- Carvalho CM, Zhang F, Lupski JR. Structural variation of the human genome: mechanisms, assays, and role in male infertility. Syst Biol Reprod Med 2011;57:3-16. https://doi.org/10.3109/19396368.2010.527427
- McCarroll SA, Altshuler DM. Copy-number variation and association studies of human disease. Nat Genet 2007;39:S37-42. https://doi.org/10.1038/ng2080
- Shelling AN, Ferguson LR. Genetic variation in human disease and a new role for copy number variants. Mutat Res 2007;622:33-41. https://doi.org/10.1016/j.mrfmmm.2007.04.011
- Crespi BJ, Crofts HJ. Association testing of copy number variants in schizophrenia and autism spectrum disorders. J Neurodev Disord 2012;4:15. https://doi.org/10.1186/1866-1955-4-15
- Boussaha M, Esquerre D, Barbieri J, et al. Genome-wide study of structural variants in bovine Holstein, montbeliarde and normande dairy breeds. PLoS One 2015;10:e0135931. https://doi.org/10.1371/journal.pone.0135931
- Flisikowski K, Venhoranta H, Nowacka-Woszuk J, et al. A novel mutation in the maternally imprinted PEG3 domain results in a loss of MIMT1 expression and causes abortions and stillbirths in cattle (Bos taurus). PLoS One 2010;5:e15116. https://doi.org/10.1371/journal.pone.0015116
- Chen Q, Ma Y, Yang Y, et al. Genotyping by genome reducing and sequencing for outbred animals. PLoS One 2013;8:e67500. https://doi.org/10.1371/journal.pone.0067500
- Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. https://doi.org/10.1093/bioinformatics/btp324
- Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-9. https://doi.org/10.1093/bioinformatics/btp352
- Rausch T, Zichner T, Schlattl A, et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 2012;28:i333-i9. https://doi.org/10.1093/bioinformatics/bts378
- Wang Z, Chen Q, Liao R, et al. Genome-wide genetic variation discovery in Chinese Taihu pig breeds using next generation sequencing. Anim Genet 2017;48:38-47. https://doi.org/10.1111/age.12465
- Klambauer G, Schwarzbauer K, Mayr A, et al. cn.MOPS: mixture of Poissons for discovering copy number variations in nextgeneration sequencing data with a low false discovery rate. Nucleic Acids Res 2012;40:e69. https://doi.org/10.1093/nar/gks003
- Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
- Jimenez-Marin A, Collado-Romero M, Ramirez-Boo M, Arce C, Garrido JJ. Biological pathway analysis by ArrayUnlock and Ingenuity Pathway Analysis. BMC Proc 2009;3(Suppl 4):S6.
- Yu J, Gu X, Yi S. Ingenuity pathway analysis of gene expression profiles in distal nerve stump following nerve injury: insights into wallerian degeneration. Front Cell Neurosci 2016;10:274.
- Wang J, Jiang J, Fu W, et al. A genome-wide detection of copy number variations using SNP genotyping arrays in swine. BMC Genomics 2012;13:273. https://doi.org/10.1186/1471-2164-13-273
- Jiang L, Jiang J, Yang J, et al. Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins. BMC Genomics 2013;14:131. https://doi.org/10.1186/1471-2164-14-131
- Shin DH, Lee HJ, Cho S, et al. Deleted copy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics 2014;15:240. https://doi.org/10.1186/1471-2164-15-240
- Keel BN, Keele JW, Snelling WM. Genome-wide copy number variation in the bovine genome detected using low coverage sequence of popular beef breeds. Anim Genet 2017;48:141-50. https://doi.org/10.1111/age.12519
- Castellani CA, Melka MG, Wishart AE, et al. Biological relevance of CNV calling methods using familial relatedness including monozygotic twins. BMC Bioinformatics 2014;15:114. https://doi.org/10.1186/1471-2105-15-114
- Abyzov A, Urban AE, Snyder M, Gerstein M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res 2011;21:974-84. https://doi.org/10.1101/gr.114876.110
- Duan J, Zhang JG, Deng HW, Wang YP. Comparative studies of copy number variation detection methods for next-generation sequencing technologies. PLoS One 2013;8:e59128. https://doi.org/10.1371/journal.pone.0059128
- Chen K, Wallis JW, McLellan MD, et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat Methods 2009;6:677-81. https://doi.org/10.1038/nmeth.1363
- Zhang J, Wang J, Wu Y. An improved approach for accurate and efficient calling of structural variations with low-coverage sequence data. BMC Bioinformatics 2012;13(Suppl 6):S6. https://doi.org/10.1186/1471-2105-13-S6-S6
- Wang J, Ling C, Gao J. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks. Biomed Res Int 2017;2017:Article ID 6375059.
- Mohiyuddin M, Mu JC, Li J, et al. MetaSV: an accurate and integrative structural-variant caller for next generation sequencing. Bioinformatics 2015;31:2741-4. https://doi.org/10.1093/bioinformatics/btv204
- Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 2009;25:2865-71. https://doi.org/10.1093/bioinformatics/btp394
- Zhao M, Wang Q, Wang Q, Jia P, Zhao Z. Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives. BMC Bioinformatics 2013;14(Suppl 11):S1.
- Nguyen HT, Boocock J, Merriman TR, Black MA. SRBreak: a read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions. Front Genet 2016;7:160.
- Kronenberg ZN, Osborne EJ, Cone KR, et al. Wham: Identifying Structural variants of biological consequence. PLoS Comput Biol 2015;11:e1004572. https://doi.org/10.1371/journal.pcbi.1004572
- Ezzat Alnakip M, Quintela-Baluja M, Bohme K, et al. The immunology of mammary gland of dairy ruminants between healthy and inflammatory conditions. J Vet Med 2014;2014: Article ID 659801.
- Van Werven T, Noordhuizen-Stassen EN, Daemen AJ, et al. Preinfection in vitro chemotaxis, phagocytosis, oxidative burst, and expression of CD11/CD18 receptors and their predictive capacity on the outcome of mastitis induced in dairy cows with Escherichia coli. J Dairy Sci 1997;80:67-74. https://doi.org/10.3168/jds.S0022-0302(97)75913-7
- Yang J, Fu Z, Hong Y, et al. The differential expression of immune genes between water buffalo and yellow cattle determines species-specific susceptibility to Schistosoma japonicum infection. PLoS One 2015;10:e0130344. https://doi.org/10.1371/journal.pone.0130344
- Buehring GC, Shen HM, Jensen HM, et al. Exposure to bovine leukemia virus is associated with breast cancer: a case-control study. PLoS One 2015;10:e0134304. https://doi.org/10.1371/journal.pone.0134304
- An D, Yang J, Zhang P. Transcriptome profiling of low temperature-treated cassava apical shoots showed dynamic responses of tropical plant to cold stress. BMC Genomics 2012;13:64. https://doi.org/10.1186/1471-2164-13-64
- Stentoft C, Rojen BA, Jensen SK, et al. Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows. Br J Nutr 2015;113:560-73. https://doi.org/10.1017/S0007114514004000
- Zollner N. Purine and pyrimidine metabolism. Proc Nutr Soc 1982;41:329-42. https://doi.org/10.1079/PNS19820048
- Harada N, Yokoyama T, Yamaji R, Nakano Y, Inui H. RanBP10 acts as a novel coactivtor for the androgen receptor. Biochem Biophys Res Commun 2008;368:121-5. https://doi.org/10.1016/j.bbrc.2008.01.072
- Sallam AM, Zare Y, Alpay F, et al. An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle. J Dairy Res 2017;84:61-7. https://doi.org/10.1017/S0022029916000807
- Gurgul A, Szmatola T, Ropka-Molik K, et al. Identification of genome-wide selection signatures in the Limousin beef cattle breed. J Anim Breed Genet 2016;133:264-76. https://doi.org/10.1111/jbg.12196
- Jaeger A, Hadlich F, Kemper N, et al. MicroRNA expression profiling of porcine mammary epithelial cells after challenge with Escherichia coli in vitro. BMC Genomics 2017;18:660. https://doi.org/10.1186/s12864-017-4070-2
- Grum DE, Drackley JK, Hansen LR, Cremin JD, Jr. Production, digestion, and hepatic lipid metabolism of dairy cows fed increased energy from fat or concentrate. J Dairy Sci 1996;79:1836-49. https://doi.org/10.3168/jds.S0022-0302(96)76552-9
- Killeen AP, Morris DG, Kenny DA, et al. Global gene expression in endometrium of high and low fertility heifers during the mid-luteal phase of the estrous cycle. BMC Genomics 2014;15:234. https://doi.org/10.1186/1471-2164-15-234
- Heaton MP, Smith TP, Carnahan JK, et al. Using diverse U.S. beef cattle genomes to identify missense mutations in EPAS1, a gene associated with pulmonary hypertension. F1000Res 2016;5:2003. https://doi.org/10.12688/f1000research.9254.2
- Poleti MD, DeRijk RH, Rosa AF, et al. Genetic variants in glucocorticoid and mineralocorticoid receptors are associated with concentrations of plasma cortisol, muscle glycogen content, and meat quality traits in male Nellore cattle. Domest Anim Endocrinol 2015;51:105-13. https://doi.org/10.1016/j.domaniend.2014.12.004
- Omae Y, Toyo-Oka L, Yanai H, et al. Pathogen lineage-based genome-wide association study identified CD53 as susceptible locus in tuberculosis. J Hum Genet 2017;62:1015-22. https://doi.org/10.1038/jhg.2017.82
- Groenen MA, Archibald AL, Uenishi H, et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature 2012;491:393-8. https://doi.org/10.1038/nature11622
- Li M, Tian S, Jin L, et al. Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars. Nat Genet 2013;45:1431-8. https://doi.org/10.1038/ng.2811