Functional & Integrative Genomics

, Volume 12, Issue 1, pp 81–92 | Cite as

Genomic regions showing copy number variations associate with resistance or susceptibility to gastrointestinal nematodes in Angus cattle

  • Yali Hou
  • George E. Liu
  • Derek M. Bickhart
  • Lakshmi K. Matukumalli
  • Congjun Li
  • Jiuzhou Song
  • Louis C. Gasbarre
  • Curtis P. Van Tassell
  • Tad S. Sonstegard
Original Paper

Abstract

Genomic structural variation is an important and abundant source of genetic and phenotypic variation. We previously reported an initial analysis of copy number variations (CNVs) in Angus cattle selected for resistance or susceptibility to gastrointestinal nematodes. In this study, we performed a large-scale analysis of CNVs using SNP genotyping data from 472 animals of the same population. We detected 811 candidate CNV regions, which represent 141.8 Mb (~4.7%) of the genome. To investigate the functional impacts of CNVs, we created 2 groups of 100 individual animals with extremely low or high estimated breeding values of eggs per gram of feces and referred to these groups as parasite resistant (PR) or parasite susceptible (PS), respectively. We identified 297 (~51 Mb) and 282 (~48 Mb) CNV regions from PR and PS groups, respectively. Approximately 60% of the CNV regions were specific to the PS group or PR group of animals. Selected PR- or PS-specific CNVs were further experimentally validated by quantitative PCR. A total of 297 PR CNV regions overlapped with 437 Ensembl genes enriched in immunity and defense, like WC1 gene which uniquely expresses on gamma/delta T cells in cattle. Network analyses indicated that the PR-specific genes were predominantly involved in gastrointestinal disease, immunological disease, inflammatory response, cell-to-cell signaling and interaction, lymphoid tissue development, and cell death. By contrast, the 282 PS CNV regions contained 473 Ensembl genes which are overrepresented in environmental interactions. Network analyses indicated that the PS-specific genes were particularly enriched for inflammatory response, immune cell trafficking, metabolic disease, cell cycle, and cellular organization and movement.

Keywords

Parasite resistance Cattle genome Copy number variation (CNV) Single nucleotide polymorphism (SNP) 

Abbreviations

aCGH

Array comparative genomic hybridizations

CNV

Copy number variation

CNVR

CNV region

CT

Cycle threshold

EPG

Eggs per gram of feces

GI

Gastrointestinal

MLN

Mesenteric lymph node

OMIA

Online Mendelian Inheritance in Animals

PR

Parasite resistant

PS

Parasite susceptible

qPCR

Quantitative PCR

QTL

Quantitative trait locus

SNP

Single nucleotide polymorphism

Supplementary material

10142_2011_252_MOESM1_ESM.pdf (183 kb)
Table S1Btau_4.0 cattle CNV regions and their frequencies in the resource Angus population (472 animals). The description of CNV regions includes the coordinates (chromosome, start position, end position, length), CNV type (gain, loss, both), the start SNP name, the end SNP name, the number of SNPs in this region, the number of animals having CNV events in this region, as well as its frequency. The frequency was defined as “Unique” when the CNV region was unique only to one animal, “Multiple” when the CNV region was shared by two animals, or shown as the exact proportion of animals having CNV events in this region when the CNV region was shared by at least three animals (PDF 182 kb)
10142_2011_252_MOESM2_ESM.pdf (21 kb)
Table S2Over/underrepresentation of PANTHER molecular function, biological process, and pathway terms in the resource Angus population (472 animals) (PDF 20 kb)
10142_2011_252_MOESM3_ESM.pdf (93 kb)
Table S3Btau_4.0 cattle CNV regions and their frequencies in top 100 parasite resistant (PR100) Angus cattle. The description of CNV regions includes the coordinates (chromosome, start position, end position, length), CNV type (gain, loss, both), the start SNP name, the end SNP name, the number of SNPs in this region, the number of animals having CNV events in this region, as well as its frequency. The frequency was defined as “Unique” when the CNV region was unique only to one animal, “Multiple” when the CNV region was shared by two animals, or shown as the exact proportion of animals having CNV events in this region when the CNV region was shared by at least three animals (PDF 93.1 kb)
10142_2011_252_MOESM4_ESM.pdf (90 kb)
Table S4Btau_4.0 cattle CNV regions and their frequencies in top 100 parasite susceptible (PS100) Angus cattle. The description of CNV regions includes the coordinates (chromosome, start position, end position, length), CNV type (gain, loss, both), the start SNP name, the end SNP name, the number of SNPs in this region, the number of animals having CNV events in this region, as well as its frequency. The frequency was defined as “Unique” when the CNV region was unique only to one animal, “Multiple” when the CNV region was shared by two animals, or shown as the exact proportion of animals having CNV events in this region when the CNV region was shared by at least three animals (PDF 90.2 kb)
10142_2011_252_MOESM5_ESM.pdf (24 kb)
Table S5Over/underrepresentation of PANTHER molecular function, biological process, and pathway terms in two extreme groups (PR100 and PS100) (PDF 23 kb)
10142_2011_252_MOESM6_ESM.xls (924 kb)
Table S6Gene contents of CNV regions. See Table S6.xls (PDF 924 kb)
10142_2011_252_MOESM7_ESM.pdf (23 kb)
Table S7qPCR summary (PDF 22 kb)
10142_2011_252_MOESM8_ESM.pdf (79 kb)
Fig. S1Genomic landscape of cattle copy number variations and segmental duplications. CNV regions (811 events, 141 Mb, ~4.60% of the bovine genome) derived from all 472 SNP-genotyped Angus are shown above the chromosomes in green (gain), red (loss), and dark blue (both). Below the chromosomes are the CNV regions (682 events, 139 Mb, ~4.60% of the bovine genome) identified by the same method using the Bovine HapMap SNP genotyping data, including 539 animals of 21 modern cattle breeds and 6 outgroups (Hou et al. 2011). The bar height represents their frequencies: short (appeared in 1 sample), median (≥2 samples), and tall (≥5 samples). Segmental duplications (94.4 Mb, 3.1% of the bovine genome) predicted by two independent computational approaches (Liu et al. 2010) are illustrated on the chromosomes in red (WSSD), blue (WGAC), or purple (both). The patterns are depicted for all duplications for ≥5 kb in length and ≥90% sequence identity. The gaps in the assembly are represented on the chromosomes as white ticks (PDF 79.1 kb)
10142_2011_252_MOESM9_ESM.pdf (168 kb)
Fig. S2Comparisons between identified 811 CNVRs in this study and the other existing cattle CNVR datasets in terms of count and length. A, compared to three CNVR datasets derived from SNP array (Bae et al. 2010; Seroussi et al. 2010; Hou et al. 2011); B, compared to two CNVR datasets derived from array CGH studies (Liu et al. 2010; Fadista et al. 2010); C, the summaries and legends of existing cattle CNVR datasets (PDF 168 kb)
10142_2011_252_MOESM10_ESM.pdf (30 kb)
Fig. S3qPCR results for the TRMP7 locus. (Top) Representation of SNP CNV calls determined by PennCNV in two animals (IDs 1348 and 1461; blue and red, respectively) at the TRMP7 locus on Chr6. SNP marker coordinates are listed on the x-axis (spacing not to scale with actual distance) and the inferred copy number at the position is listed on the y-axis. One of the areas investigated using qPCR is highlighted with a black bar. (Bottom right) Whisker plot of qPCR determined copy number at the TRMP7 locus. Animal IDs are listed on the x-axis with animal 1348 represented in blue and animal 1461 represented in red. Plots were made from copy number estimates from triplicate qPCR reactions using the same conditions and primers (PDF 29.8 kb)
10142_2011_252_MOESM11_ESM.pdf (90 kb)
Fig. S4The network for the PR100 group indentified by IPA is involved in the inflammatory response pathway. For meanings of shapes and lines, see legend in the figure (PDF 89.7 kb)
10142_2011_252_MOESM12_ESM.pdf (86 kb)
Fig. S5The network for the PS100 group indentified by IPA is involved in the inflammatory response pathway. For meanings of shapes and lines, see legend in the figure (PDF 86.2 kb)

References

  1. Araujo RN, Padilha T, Zarlenga D, Sonstegard T, Connor EE, Van Tassel C, Lima WS, Nascimento E, Gasbarre LC (2009) Use of a candidate gene array to delineate gene expression patterns in cattle selected for resistance or susceptibility to intestinal nematodes. Vet Parasitol 162:106–115PubMedCrossRefGoogle Scholar
  2. Bae JS, Cheong HS, Kim LH, NamGung S, Park TJ, Chun JY, Kim JY, Pasaje CF, Lee JS, Shin HD (2010) Identification of copy number variations and common deletion polymorphisms in cattle. BMC Genomics 11:232PubMedCrossRefGoogle Scholar
  3. Coltman DW, Wilson K, Pilkington JG, Stear MJ, Pemberton JM (2001) A microsatellite polymorphism in the gamma interferon gene is associated with resistance to gastrointestinal nematodes in a naturally-parasitized population of Soay sheep. Parasitology 122:571–582PubMedCrossRefGoogle Scholar
  4. Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y, Aerts J, Andrews TD, Barnes C, Campbell P, Fitzgerald T, Hu M, Ihm CH, Kristiansson K, Macarthur DG, MacDonald JR, Onyiah I, Pang AW, Robson S, Stirrups K, Valsesia A, Walter K, Wei J, Tyler-Smith C, Carter NP, Lee C, Scherer SW, Hurles ME (2009) Origins and functional impact of copy number variation in the human genome. Nature 464:704–712PubMedCrossRefGoogle Scholar
  5. Coppieters W, Mes TH, Druet T, Farnir F, Tamma N, Schrooten C, Cornelissen AW, Georges M, Ploeger HW (2009) Mapping QTL influencing gastrointestinal nematode burden in Dutch Holstein-Friesian dairy cattle. BMC Genomics 10:96PubMedCrossRefGoogle Scholar
  6. Crawford AM, Paterson KA, Dodds KG, Diez TC, Williamson PA, Roberts TM, Bisset SA, Beattie AE, Greer GJ, Green RS, Wheeler R, Shaw RJ, Knowler K, McEwan JC (2006) Discovery of quantitative trait loci for resistance to parasitic nematode infection in sheep: I. Analysis of outcross pedigrees. BMC Genomics 7:178PubMedCrossRefGoogle Scholar
  7. Diez-Tascon C, Keane OM, Wilson T, Zadissa A, Hyndman DL, Baird DB, McEwan JC, Crawford AM (2005) Microarray analysis of selection lines from outbred populations to identify genes involved with nematode parasite resistance in sheep. Physiol Genomics 21:59–69PubMedCrossRefGoogle Scholar
  8. Fadista J, Thomsen B, Holm LE, Bendixen C (2010) Copy number variation in the bovine genome. BMC Genomics 11:284PubMedCrossRefGoogle Scholar
  9. Finkelman FD, Shea-Donohue T, Morris SC, Gildea L, Strait R, Madden KB, Schopf L, Urban JF Jr (2004) Interleukin-4- and interleukin-13-mediated host protection against intestinal nematode parasites. Immunol Rev 201:139–155PubMedCrossRefGoogle Scholar
  10. Gasbarre LC, Leighton EA, Sonstegard T (2001) Role of the bovine immune system and genome in resistance to gastrointestinal nematodes. Vet Parasitol 98:51–64PubMedCrossRefGoogle Scholar
  11. Hou Y, Liu GE, Bickhart DM, Cardone MF, Wang K, Kim ES, Matukumalli LK, Ventura M, Song J, VanRaden PM, Sonstegard TS, Van Tassell CP (2011) Genomic characteristics of cattle copy number variations. BMC Genomics 12:127PubMedCrossRefGoogle Scholar
  12. Ingham A, Reverter A, Windon R, Hunt P, Menzies M (2008) Gastrointestinal nematode challenge induces some conserved gene expression changes in the gut mucosa of genetically resistant sheep. Int J Parasitol 38:431–442PubMedCrossRefGoogle Scholar
  13. Jongstra-Bilen J, Jongstra J (2006) Leukocyte-specific protein 1 (LSP1): a regulator of leukocyte emigration in inflammation. Immunol Res 35:65–74PubMedCrossRefGoogle Scholar
  14. Kadiyala RK, McIntyre BW, Krensky AM (1990) Molecular cloning and characterization of WP34, a phosphorylated human lymphocyte differentiation and activation antigen. Eur J Immunol 20:2417–2423PubMedCrossRefGoogle Scholar
  15. Keane OM, Zadissa A, Wilson T, Hyndman DL, Greer GJ, Baird DB, McCulloch AF, Crawford AM, McEwan JC (2006) Gene expression profiling of naive sheep genetically resistant and susceptible to gastrointestinal nematodes. BMC Genomics 7:42PubMedCrossRefGoogle Scholar
  16. Li RW, Gasbarre LC (2009) A temporal shift in regulatory networks and pathways in the bovine small intestine during Cooperia oncophora infection. Int J Parasitol 39:813–824PubMedCrossRefGoogle Scholar
  17. Li RW, Sonstegard TS, Van Tassell CP, Gasbarre LC (2007) Local inflammation as a possible mechanism of resistance to gastrointestinal nematodes in Angus heifers. Vet Parasitol 145:100–107PubMedCrossRefGoogle Scholar
  18. Liu GE, Hou Y, Zhu B, Cardone MF, Jiang L, Cellamare A, Mitra A, Alexander LJ, Coutinho LL, Dell’aquila ME, Gasbarre LC, Lacalandra G, Li RW, Matukumalli LK, Nonneman D, Regitano LC, Smith TP, Song J, Sonstegard TS, Van Tassell CP, Ventura M, Eichler EE, McDaneld TG, Keele JW (2010) Analysis of copy number variations among diverse cattle breeds. Genome Res 20:693–703PubMedCrossRefGoogle Scholar
  19. Liu GE, Brown T, Hebert DA, Cardone MF, Hou Y, Choudhary RK, Shaffer J, Amazu C, Connor EE, Ventura M, Gasbarre LC (2011) Initial analysis of copy number variations in cattle selected for resistance or susceptibility to intestinal nematodes. Mamm Genome 22:111–121PubMedCrossRefGoogle Scholar
  20. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedCrossRefGoogle Scholar
  21. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4:e5350PubMedCrossRefGoogle Scholar
  22. McCarroll SA (2008) Extending genome-wide association studies to copy-number variation. Hum Mol Genet 17:R135–R142PubMedCrossRefGoogle Scholar
  23. Menzies M, Reverter A, Andronicos N, Hunt P, Windon R, Ingham A (2010) Nematode challenge induces differential expression of oxidant, antioxidant and mucous genes down the longitudinal axis of the sheep gut. Parasite Immunol 32:36–46PubMedCrossRefGoogle Scholar
  24. Minegishi Y, Coustan-Smith E, Wang YH, Cooper MD, Campana D, Conley ME (1998) Mutations in the human lambda5/14.1 gene result in B cell deficiency and agammaglobulinemia. J Exp Med 187:71–77PubMedCrossRefGoogle Scholar
  25. Mohanty TR, Seo KS, Park KM, Choi TJ, Choe HS, Baik DH, Hwang IH (2008) Molecular variation in pigmentation genes contributing to coat colour in native Korean Hanwoo cattle. Anim Genet 39:550–553PubMedCrossRefGoogle Scholar
  26. Nishiyama T, Takahashi K, Tango T, Pinto D, Scherer SW, Takami S, Kishino H (2011) A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data. BMC Bioinformatics 12:205PubMedCrossRefGoogle Scholar
  27. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, Bailey AJ, Baird G, Battaglia A, Berney T, Bolshakova N, Bolte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Bryson SE, Carson AR, Casallo G, Casey J, Chung BH, Cochrane L, Corsello C, Crawford EL, Crossett A, Cytrynbaum C, Dawson G, de Jonge M, Delorme R, Drmic I, Duketis E, Duque F, Estes A, Farrar P, Fernandez BA, Folstein SE, Fombonne E, Freitag CM, Gilbert J, Gillberg C, Glessner JT, Goldberg J, Green A, Green J, Guter SJ, Hakonarson H, Heron EA, Hill M, Holt R, Howe JL, Hughes G, Hus V, Igliozzi R, Kim C, Klauck SM, Kolevzon A, Korvatska O, Kustanovich V, Lajonchere CM, Lamb JA, Laskawiec M, Leboyer M, Le Couteur A, Leventhal BL, Lionel AC, Liu XQ, Lord C, Lotspeich L, Lund SC, Maestrini E, Mahoney W, Mantoulan C, Marshall CR, McConachie H, McDougle CJ, McGrath J, McMahon WM, Merikangas A, Migita O, Minshew NJ, Mirza GK, Munson J, Nelson SF, Noakes C, Noor A, Nygren G, Oliveira G, Papanikolaou K, Parr JR, Parrini B, Paton T, Pickles A, Pilorge M, Piven J, Ponting CP, Posey DJ, Poustka A, Poustka F, Prasad A, Ragoussis J, Renshaw K, Rickaby J, Roberts W, Roeder K, Roge B, Rutter ML, Bierut LJ, Rice JP, Salt J, Sansom K, Sato D, Segurado R, Sequeira AF, Senman L, Shah N, Sheffield VC, Soorya L, Sousa I, Stein O, Sykes N, Stoppioni V, Strawbridge C, Tancredi R, Tansey K, Thiruvahindrapduram B, Thompson AP, Thomson S, Tryfon A, Tsiantis J, Van Engeland H, Vincent JB, Volkmar F, Wallace S, Wang K, Wang Z, Wassink TH, Webber C, Weksberg R, Wing K, Wittemeyer K, Wood S, Wu J, Yaspan BL, Zurawiecki D, Zwaigenbaum L, Buxbaum JD, Cantor, RM, Cook EH, Coon H, Cuccaro ML, Devlin B, Ennis S, Gallagher L, Geschwind DH, Gill M, Haines JL, Hallmayer J, Miller J, Monaco AP, Nurnberger JI Jr, Paterson AD, Pericak-Vance MA, Schellenberg GD, Szatmari P, Vicente AM, Vieland VJ, Wijsman EM, Scherer SW, Sutcliffe JS, Betancur C (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466(7304):368–372Google Scholar
  28. Raychaudhuri S, Korn JM, McCarroll SA, Altshuler D, Sklar P, Purcell S, Daly MJ (2010) Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function. PLoS Genet 6(9):e1001097. doi:10.1371/journal.pgen.1001097
  29. 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 444:444–454PubMedCrossRefGoogle Scholar
  30. Rogers AN, Vanburen DG, Hedblom E, Tilahun ME, Telfer JC, Baldwin CL (2005) Function of ruminant gammadelta T cells is defined by WC1.1 or WC1.2 isoform expression. Vet Immunol Immunopathol 108:211–217PubMedCrossRefGoogle Scholar
  31. Scherer SW, Lee C, Birney E, Altshuler DM, Eichler EE, Carter NP, Hurles ME, Feuk L (2007) Challenges and standards in integrating surveys of structural variation. Nat Genet 39:S7–S15PubMedCrossRefGoogle Scholar
  32. Seroussi E, Glick G, Shirak A, Yakobson E, Weller JI, Ezra E, Zeron Y (2010) Analysis of copy loss and gain variations in Holstein cattle autosomes using BeadChip SNPs. BMC Genomics 11:673PubMedCrossRefGoogle Scholar
  33. Sonstegard TS, Gasbarre LC (2001) Genomic tools to improve parasite resistance. Vet Parasitol 101:387–403PubMedCrossRefGoogle Scholar
  34. Sonstegard TS, Garrett WM, Ashwell MS, Bennett GL, Kappes SM, Van Tassell CP (2000) Comparative map alignment of BTA27 and HSA4 and 8 to identify conserved segments of genome containing fat deposition QTL. Mamm Genome 11:682–688PubMedCrossRefGoogle Scholar
  35. 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 315:848–853PubMedCrossRefGoogle Scholar
  36. Wang F, Herzig CT, Chen C, Hsu H, Baldwin CL, Telfer JC (2011) Scavenger receptor WC1 contributes to the gammadelta T cell response to Leptospira. Mol Immunol 48:801–809PubMedCrossRefGoogle Scholar
  37. Zhang F, Gu W, Hurles ME, Lupski JR (2009) Copy number variation in human health, disease, and evolution. Annu Rev Genomics Hum Genet 10:451–481PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag (outside the USA) 2011

Authors and Affiliations

  • Yali Hou
    • 1
    • 2
  • George E. Liu
    • 1
  • Derek M. Bickhart
    • 1
  • Lakshmi K. Matukumalli
    • 1
    • 3
  • Congjun Li
    • 1
  • Jiuzhou Song
    • 2
  • Louis C. Gasbarre
    • 1
  • Curtis P. Van Tassell
    • 1
  • Tad S. Sonstegard
    • 1
  1. 1.Bovine Functional Genomics LaboratoryANRI, USDA-ARSBeltsvilleUSA
  2. 2.Department of Animal and Avian SciencesUniversity of MarylandCollege ParkUSA
  3. 3.School of Systems BiologyGeorge Mason UniversityManassasUSA

Personalised recommendations