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Functional & Integrative Genomics

, Volume 18, Issue 5, pp 559–567 | Cite as

Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature

  • Xiu-Kai Cao
  • Yong-Zhen Huang
  • Yi-Lei Ma
  • Jie Cheng
  • Zhen-Xian Qu
  • Yun Ma
  • Yue-Yu Bai
  • Feng Tian
  • Feng-Peng Lin
  • Yu-Lin Ma
  • Hong ChenEmail author
Original Article

Abstract

Copy number variation (CNV) of DNA sequences, functionally significant but yet fully ascertained, is believed to confer considerable increments in unexplained heritability of quantitative traits. Identification of phenotype-associated CNVs (paCNVs) therefore is a pressing need in CNV studies to speed up their exploitation in cattle breeding programs. Here, we provided a new avenue to achieve this goal that is to project the published CNV data onto meta-quantitative trait loci (meta-QTL) map which connects causal genes with phenotypes. Any CNVs overlapping meta-QTL therefore will be potential paCNVs. This study reported potential paCNVs in Bos taurus autosome 3 (BTA3). Notably, overview indexes and CNVs both highlighted a narrower region (BTA3 54,500,000–55,000,000 bp, named BTA3_INQTL_6) within one constructed meta-QTL. Then, we ascertained guanylate-binding protein 4 (GBP4) among the nine positional candidate genes was significantly associated with adult cattle stature, including body weight (BW, P < 0.05) and withers height (WHT, P < 0.05), fitting GBP4 CNV either with three levels or with six levels in the model. Although higher copy number downregulated the mRNA levels of GBP2 (P < 0.05) and GBP4 (P < 0.05) in 1-Mb window (54.0–55.0 Mb) in muscle and adipose, additional analyses will be needed to clarify the causality behind the ascertained association.

Keywords

Meta-analysis Overview analysis GBP CNV Cattle stature 

Notes

Funding information

This work was supported by the National 863 Program of China [2013AA102505], the Program of National Beef Cattle and Yak Industrial Technology System [CARS-37], the National Natural Science Foundation of China [31272408, 31601926], Bio-breeding capacity-building and industry specific projects from the National Development and Reform Commission [2014-2573], Specific Projects of Science and Technology in Henan Province [141100110200], Science and Technology Co-ordinator Innovative engineering projects of Shaanxi Province [2014KTZB02-02-02-02, 2015KTCL02-08], Project of breeding and application of Pinan Cattle, and Haixi Project of Qinghai Province: Identification of key genes of growth and high-quality meat in Yak Multi hybrids.

Compliance with ethical standards

This study was approved by the Northwest A&F University Ethics Committee.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10142_2018_613_MOESM1_ESM.xlsx (79 kb)
ESM 1 (XLSX 78 kb)

References

  1. Aitman TJ, Dong R, Vyse TJ, Norsworthy PJ, Johnson MD, Smith J, Mangion J, Roberton-Lowe C, Marshall AJ, Petretto E, Hodges MD, Bhangal G, Patel SG, Sheehan-Rooney K, Duda M, Cook PR, Evans DJ, Domin J, Flint J, Boyle JJ, Pusey CD, Cook HT (2006) Copy number polymorphism in Fcgr3 predisposes to glomerulonephritis in rats and humans. Nature 439(7078):851–855CrossRefPubMedGoogle Scholar
  2. Bickhart DM, Hou Y, Schroeder SG, Alkan C, Cardone MF, Matukumalli LK, Song J, Schnabe RD, Ventura M, Taylor JF, Garcia JF, Van Tasse CP, Sonstegard TS, Eichler EE, Liu GE (2012) Copy number variation of individual cattle genomes using next-generation sequencing. Genome Res 22(4):778–790CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bolormaa S, Pryce JE, Reverter A, Zhang Y, Barendse W, Kemper K, Tier B, Savin K, Hayes BJ, Goddard ME (2014) A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet 10(3):e1004198CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics 168(4):2169–2185CrossRefPubMedPubMedCentralGoogle Scholar
  5. da Silva JM, Giachetto PF, da Silva LO, Cintra LC, Paiva SR, Beleza Yamagishi ME, Caetano AR (2016) Genome-wide copy number variation (CNV) detection in Nelore cattle reveals highly frequent variants in genome regions harboring QTLs affecting production traits. BMC Genomics 17(1):454CrossRefPubMedPubMedCentralGoogle Scholar
  6. Duran Aguilar M, Roman Ponce SI, Ruiz Lopez FJ, Gonzalez Padilla E, Vasquez Pelaez CG, Bagnato A, Strillacci MG (2017) Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers. J Anim Breed Genet 134(1):49–59CrossRefPubMedGoogle Scholar
  7. Fanciulli M, Norsworthy PJ, Petretto E, Dong R, Harper L, Kamesh L, Heward JM, Gough SCL, de Smith A, Blakemore AIF, Owen CJ, Pearce SHS, Teixeira L, Guillevin L, Graham DSC, Pusey CD, Cook HT, Vyse TJ, Aitman TJ (2007) FCGR3B copy number variation is associated with susceptibility to systemic, but not organ-specific, autoimmunity. Nat Genet 39(6):721–723CrossRefPubMedPubMedCentralGoogle Scholar
  8. Feng J, Cao Z, Wang L, Wan Y, Peng N, Wang Q, Chen X, Zhou Y, Zhu Y (2017) Inducible GBP5 mediates the antiviral response via interferon-related pathways during influenza a virus infection. J Innate Immun 9(4):419–435CrossRefPubMedGoogle Scholar
  9. Finethy R, Jorgensen I, Haldar AK, de Zoete MR, Strowig T, Flavell RA, Yamamoto M, Nagarajan UM, Miao EA, Coers J (2015) Guanylate binding proteins enable rapid activation of canonical and noncanonical inflammasomes in Chlamydia-infected macrophages. Infect Immun 83(12):4740–4749CrossRefPubMedPubMedCentralGoogle Scholar
  10. Gamazon ER, Stranger BE (2015) The impact of human copy number variation on gene expression. Brief Funct Genomics 14(5):352–357CrossRefPubMedPubMedCentralGoogle Scholar
  11. Gilbert RP, Bailey DRC, Shannon NH (1993) Linear body measurements of cattle before and after 20 years of selection for postweaning gain when fed 2 different diets. J Anim Sci 71(7):1712–1720CrossRefPubMedGoogle Scholar
  12. Girirajan S, Campbell CD, Eichler EE (2011) Human copy number variation and complex genetic disease. Annu Rev Genet 45:203–226CrossRefPubMedGoogle Scholar
  13. Goffinet B, Gerber S (2000) Quantitative trait loci: a meta-analysis. Genetics 155(1):463–473PubMedPubMedCentralGoogle Scholar
  14. Gonzalez E, Kulkarni H, Bolivar H, Mangano A, Sanchez R, Catano G, Nibbs RJ, Freedman BI, Quinones MP, Bamshad MJ, Murthy KK, Rovin BH, Bradley W, Clark RA, Anderson SA, O’Connell RJ, Agan BK, Ahuja SS, Bologna R, Sen L, Dolan MJ, Ahuja SK (2005) The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 307(5714):1434–1440CrossRefPubMedGoogle Scholar
  15. Hai R, Pei Y-F, Shen H, Zhang L, Liu X-G, Lin Y, Ran S, Pan F, Tan L-J, Lei S-F, Yang T-L, Zhang Y, Zhu X-Z, Zhao L-J, Deng H-W (2012) Genome-wide association study of copy number variation identified gremlin1 as a candidate gene for lean body mass. J Hum Genet 57(1):33–37CrossRefPubMedGoogle Scholar
  16. Hollox EJ, Huffmeier U, Zeeuwen PLJM, Palla R, Lascorz J, Rodijk-Olthuis D, van de Kerkhof PCM, Traupe H, de Jongh G, den Heijer M, Reis A, Armour JAL, Schalkwijk J (2008) Psoriasis is associated with increased beta-defensin genomic copy number. Nat Genet 40(1):23–25CrossRefPubMedGoogle Scholar
  17. Hou Y, Bickhart DM, Chung H, Hutchison JL, Norman HD, Connor EE, Liu GE (2012) Analysis of copy number variations in Holstein cows identify potential mechanisms contributing to differences in residual feed intake. Funct Integr Genomics 12(4):717–723CrossRefPubMedGoogle Scholar
  18. Hruz T, Wyss M, Docquier M, Pfaffl MW, Masanetz S, Borghi L, Verbrugghe P, Kalaydjieva L, Bleuler S, Laule O, Descombes P, Gruissem W, Zimmermann P (2011) RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. BMC Genomics 12(1):156CrossRefPubMedPubMedCentralGoogle Scholar
  19. Karczewski KJ, Snyder MP (2018) Integrative omics for health and disease. Nat Rev Genet 19(5):299–310Google Scholar
  20. Kawamura Y, Otowa T, Koike A, Sugaya N, Yoshida E, Yasuda S, Inoue K, Takei K, Konishi Y, Tanii H, Shimada T, Tochigi M, Kakiuchi C, Umekage T, Liu X, Nishida N, Tokunaga K, Kuwano R, Okazaki Y, Kaiya H, Sasaki T (2011) A genome-wide CNV association study on panic disorder in a Japanese population. J Hum Genet 56(12):852–856CrossRefPubMedGoogle Scholar
  21. Liu GE, Bickhart DM (2012) Copy number variation in the cattle genome. Funct Integr Genomics 12(4):609–624CrossRefPubMedGoogle Scholar
  22. Liu P, Yuan B, Carvalho CMB, Wuster A, Walter K, Zhang L, Gambin T, Chong Z, Campbell IM, Akdemir ZC, Gelowani V, Writzl K, Bacino CA, Lindsay SJ, Withers M, Gonzaga-Jauregui C, Wiszniewska J, Scull J, Stankiewicz P, Jhangiani SN, Muzny DM, Zhang F, Chen K, Gibbs RA, Rautenstrauss B, Cheung SW, Smith J, Breman A, Shaw CA, Patel A, Hurles ME, Lupski JR (2017) An organismal CNV mutator phenotype restricted to early human development. Cell 168(5):830–842CrossRefPubMedPubMedCentralGoogle Scholar
  23. Locke DP, Sharp AJ, McCarroll SA, McGrath SD, Newman TL, Cheng Z, Schwartz S, Albertson DG, Pinkel D, Altshuler DM, Eichler EE (2006) Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome. Am J Hum Genet 79(2):275–290CrossRefPubMedPubMedCentralGoogle Scholar
  24. Lupianez DG, Kraft K, Heinrich V, Krawitz P, Brancati F, Klopocki E, Hom D, Kayserili H, Opitz JM, Laxova R, Santos-Simarro F, Gilbert-Dussardier B, Wittler L, Borschiwer M, Haas SA, Osterwalder M, Franke M, Timmermann B, Hecht J, Spielmann M, Visel A, Mundlos S (2015) Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161(5):1012–1025CrossRefPubMedPubMedCentralGoogle Scholar
  25. Marcovecchio ML, Florio R, Verginelli F, De Lellis L, Capelli C, Verzilli D, Chiarelli F, Mohn A, Cama A (2016) Low AMY1 gene copy number is associated with increased body mass index in prepubertal boys. PLoS One 11(5):e0154961CrossRefPubMedPubMedCentralGoogle Scholar
  26. Martinez AK, Soriano JM, Tuberosa R, Koumproglou R, Jahrmann T, Salvi S (2016) Yield QTLome distribution correlates with gene density in maize. Plant Sci 242:300–309CrossRefPubMedGoogle Scholar
  27. McCarroll SA, Altshuler DM (2007) Copy-number variation and association studies of human disease. Nat Genet 39:S37–S42CrossRefPubMedGoogle Scholar
  28. Meunier E, Wallet P, Dreier RF, Costanzo S, Anton L, Ruehl S, Dussurgey S, Dick MS, Kistner A, Rigard M, Degrandi D, Pfeffer K, Yamamoto M, Henry T, Broz P (2015) Guanylate-binding proteins promote activation of the AIM2 inflammasome during infection with Francisella novicida. Nat Immunol 16(5):476–484CrossRefPubMedPubMedCentralGoogle Scholar
  29. Prinsen RTMM, Rossoni A, Gredler B, Bieber A, Bagnato A, Strillacci MG (2017) A genome wide association study between CNVs and quantitative traits in Brown Swiss cattle. Livest Sci 202:7–12CrossRefGoogle Scholar
  30. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W, Cho EK, Dallaire S, Freeman JL, Gonzalez JR, Gratacos M, Huang J, Kalaitzopoulos D, Komura D, MacDonald JR, Marshall CR, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville MJ, Tchinda J, Valsesia A, Woodwark C, Yang F, Zhang J, Zerjal T, Zhang J, Armengol L, Conrad DF, Estivill X, Tyler-Smith C, Carter NP, Aburatani H, Lee C, Jones KW, Scherer SW, Hurles ME (2006) Global variation in copy number in the human genome. Nature 444(7118):444–454CrossRefPubMedPubMedCentralGoogle Scholar
  31. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, Richards DR, McDonald-Smith GP, Gao H, Hennessy L, Finnerty CC, Lopez CM, Honari S, Moore EE, Minei JP, Cuschieri J, Bankey PE, Johnson JL, Sperry J, Nathens AB, Billiar TR, West MA, Jeschke MG, Klein MB, Gamelli RL, Gibran NS, Brownstein BH, Miller-Graziano C, Calvano SE, Mason PH, Cobb JP, Rahme LG, Lowry SF, Maier RV, Moldawer LL, Herndon DN, Davis RW, Xiao W, Tompkins RG, Inflammation Host Response I (2013) Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A 110(9):3507–3512CrossRefPubMedPubMedCentralGoogle Scholar
  32. Smemo S, Tena JJ, Kim K-H, Gamazon ER, Sakabe NJ, Gomez-Marin C, Aneas I, Credidio FL, Sobreira DR, Wasserman NF, Lee JH, Puviindran V, Tam D, Shen M, Son JE, Vakili NA, Sung H-K, Naranjo S, Acemel RD, Manzanares M, Nagy A, Cox NJ, Hui C-C, Luis Gomez-Skarmeta J, Nobrega MA (2014) Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507(7492):371–375CrossRefPubMedPubMedCentralGoogle Scholar
  33. Soriano JM, Royo C (2015) Dissecting the genetic architecture of leaf rust resistance in wheat by QTL meta-analysis. Phytopathology 105(12):1585–1593CrossRefPubMedGoogle Scholar
  34. Sriroopreddy R, Sudandiradoss C (2018) Integrative network-based approach identifies central genetic and transcriptomic elements in triple-negative breast cancer. Funct Integr Genomics 18(2):113–124CrossRefPubMedGoogle Scholar
  35. Vandesompele J, De Paepe A, Speleman F (2002) Elimination of primer-dimer artifacts and genomic coamplification using a two-step SYBR green I real time RT-PCR. Anal Biochem 303(1):95–98CrossRefPubMedGoogle Scholar
  36. Visscher PM, Goddard ME (2011) Cattle gain stature. Nat Genet 43(5):397–398.  https://doi.org/10.1038/ng.819 CrossRefPubMedGoogle Scholar
  37. Wang X, Nahashon S, Feaster TK, Bohannon-Stewart A, Adefope N (2010) An initial map of chromosomal segmental copy number variations in the chicken. BMC Genomics 11(1):351CrossRefPubMedPubMedCentralGoogle Scholar
  38. Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, Henning E, Blackburn H, Loos RJF, Wareham NJ, O’Rahilly S, Hurles ME, Barroso I, Farooqi IS (2013) Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet 45(5):513–517CrossRefPubMedPubMedCentralGoogle Scholar
  39. Will AJ, Cova G, Osterwalder M, Chan W-L, Wittler L, Brieske N, Heinrich V, de Villartay J-P, Vingron M, Klopocki E, Visel A, Lupianez DG, Mundlos S (2017) Composition and dosage of a multipartite enhancer cluster control developmental expression of Ihh (Indian hedgehog). Nat Genet 49(10):1539–1545CrossRefPubMedPubMedCentralGoogle Scholar
  40. Xiang R, MacLeod IM, Bolormaa S, Goddard ME (2017) Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle. Sci Rep 7(1):9248CrossRefPubMedPubMedCentralGoogle Scholar
  41. Xu Y, Zhang L, Shi T, Zhou Y, Cai H, Lan X, Zhang C, Lei C, Chen H (2013) Copy number variations of MICAL-L2 shaping gene expression contribute to different phenotypes of cattle. Mamm Genome 24(11–12):508–516CrossRefPubMedGoogle Scholar
  42. Xu L, Hon Y, Bickhart DM, Song J, Van Tassell CP, Sonstegard TS, Liu GE (2014) A genome-wide survey reveals a deletion polymorphism associated with resistance to gastrointestinal nematodes in Angus cattle. Funct Integr Genomics 14(2):333–339CrossRefPubMedGoogle Scholar
  43. Yang Y, Chung EK, Wu YL, Savelli SL, Nagaraja HN, Zhou B, Hebert M, Jones KN, Shu Y, Kitzmiller K, Blanchong CA, McBride KL, Higgins GC, Rennebohm RM, Rice RR, Hackshaw KV, Roubey RAS, Grossman JM, Tsao BP, Birmingham DJ, Rovin BH, Hebert LA, Yu CY (2007) Gene copy-number variation and associated polymorphisms of complement component C4 in human systemic lupus erythematosus (SLE): low copy number is a risk factor for and high copy number is a protective factor against SLE susceptibility in European Americans. Am J Hum Genet 80(6):1037–1054CrossRefPubMedPubMedCentralGoogle Scholar
  44. Zamani-Ahmadmahmudi M (2016) Relationship between microRNA genes incidence and cancer-associated genomic regions in canine tumors: a comprehensive bioinformatics study. Funct Integr Genomics 16(2):143–152CrossRefPubMedGoogle Scholar
  45. Zhou Y, Utsunomiya YT, Xu L, Hay EHA, Bickhart DM, Alexandre PA, Rosen BD, Schroeder SG, Carvalheiro R, de Rezende Neves HH, Sonstegard TS, Van Tassell CP, Sterman Ferraz JB, Fukumasu H, Garcia JF, Liu GE (2016) Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus. BMC Genomics 17(1):419CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Animal Science and TechnologyNorthwest A&F UniversityYanglingChina
  2. 2.College of Life ScienceXinyang Normal UniversityXinyangChina
  3. 3.Henan Genetic Performance Determination Center for CattleZhengzhouChina
  4. 4.College of Food Science and EngineeringNorthwest A&F UniversityYanglingChina
  5. 5.Biyang Bureau of Animal HusbandryBiyangChina
  6. 6.Animal Disease Control Center of Haixi Mongolian and Tibetan Autonomous PrefectureDelinghaChina

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