Skip to main content
Log in

Gene networks for total number born in pigs across divergent environments

  • Published:
Mammalian Genome Aims and scope Submit manuscript

Abstract

For reproductive traits such as total number born (TNB), variance due to different environments is highly relevant in animal breeding. In this study, we aimed to perform a gene-network analysis for TNB in pigs across different environments using genomic reaction norm models. Thus, based on relevant single-nucleotide polymorphisms and linkage disequilibrium blocks across environments obtained from GWAS, different sets of candidate genes having biological roles linked to TNB were identified. Network analysis across environment levels resulted in gene interactions consistent with known mammal’s fertility biology, captured relevant transcription factors for TNB biology and pointing out different sets of candidate genes for TNB in different environments. These findings may have important implication for animal production, as optimal breeding may vary depending on later environments. Based on these results, genomic diversity was identified and inferred across environments highlighting differential genetic control in each scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Barrett JC et al (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265

    Article  CAS  PubMed  Google Scholar 

  • Bergsma R et al (2008) Genetic parameters and predicted selection results for maternal traits related to lactation efficiency in sows. J Anim Sci 86:1067–1080

    Article  CAS  PubMed  Google Scholar 

  • Bianco B et al (2012) The nuclear factor-kB functional promoter polymorphism is associated with endometriosis and infertility. Hum Immunol 73(11):1190–1193

    Article  CAS  PubMed  Google Scholar 

  • Bindea G et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8):1091–1093

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Blomqvist SR et al (2006) Epididymal expression of the forkhead transcription factor Foxi1 is required for male fertility. EMBO J 25(17):4131–4141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Calus MPL, Veerkamp R (2003) Estimation of environmental sensitivity of genetic merit for milk production traits using a random regression model. J Dairy Sci 86:3756–3764

    Article  CAS  PubMed  Google Scholar 

  • Choi S-W, Friso S (2010) Epigenetics: anew bridge between nutrition and health. Adv Nutr 1:8–16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dickinson RE et al (2008) Novel regulated expression of the SLIT/ROBO pathway in the ovary: possible role during luteolysis in women. Endocrinology 149:5024–5034

    Article  CAS  PubMed  Google Scholar 

  • Dickinson RE et al (2010) Involvement of the SLIT/ROBO pathway in follicle development in the fetal ovary. Reproduction 139:395–407

    Article  CAS  PubMed  Google Scholar 

  • Evans JJ, Anderson GM (2012) Balancing ovulation and anovulation: integration of the reproductive and energy balance axes by neuropeptides. Hum Reprod Update 18:313–332

    Article  CAS  PubMed  Google Scholar 

  • Fernandez-Fernandez R et al (2006) Novel signals for the integration of energy balance and reproduction. Mol Cell Endocrinol 254:127–132

    Article  PubMed  Google Scholar 

  • Forni S et al (2011) Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol 43(1):1

    Article  PubMed  PubMed Central  Google Scholar 

  • Fortes MR et al (2010) Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci 107(31):13642–13647

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fu Y et al (2012) Association of EphA4 polymorphism with swine reproductive traits and mRNA expression of EphA4 during embryo implantation. Mol Biol Rep 39(3):2689–2696

    Article  CAS  PubMed  Google Scholar 

  • Fukuda N et al (2013) The transacting factor CBF-A/Hnrnpab binds to the A2RE/RTS element of protamine 2 mRNA and contributes to its translational regulation during mouse spermatogenesis. PLoS Genet 9(10):e1003858

    Article  PubMed  PubMed Central  Google Scholar 

  • Gebhardt KM et al (2011) Human cumulus cell gene expression as a biomarker of pregnancy outcome after single embryo transfer. Fertil Steril 96(1):47–52

    Article  CAS  PubMed  Google Scholar 

  • Gilmour AR et al (2002) ASReml user guide release 1.0. VSN Int. Ltd., Hemel Hempstead

    Google Scholar 

  • Groenen MA et al (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491(7424):393–398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Guo X et al (2016) Genome-wide association analyses using a Bayesian approach for litter size and piglet mortality in Danish Landrace and Yorkshire pigs. BMC Genom 17(1):468

    Article  Google Scholar 

  • Hemberger M, Cross JC (2001) Genes governing placental development. Trends Endocrinol Metab 12(4):162–168

    Article  CAS  PubMed  Google Scholar 

  • Hernandez SC et al (2014) A genome—wide linkage analysis for reproductive traits in F2 Large White × Meishan cross gilts. Anim Genet 45(2):191–197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Horogh G et al (2005) Oestrogen receptor genotypes and litter size in Hungarian Large White pigs. J Anim Breed Genet 122(1):56–61

    Article  CAS  PubMed  Google Scholar 

  • Ibeagha-Awemu EM, Zhao X (2015) Epigenetic marks: regulators of livestock phenotypes and conceivable sources of missing variation in livestock improvement programs. Front Genet 6:302

    Article  PubMed  PubMed Central  Google Scholar 

  • Ietta F et al (2006) Dynamic HIF1A regulation during human placental development. Biol Reprod 75:112–121

    Article  CAS  PubMed  Google Scholar 

  • Jang H, Serra C (2014) Nutrition, epigenetics, and diseases. Clin Nutr Res 3:1–8

    Article  PubMed  PubMed Central  Google Scholar 

  • Johnson SA et al (2004) The Nhlh2 transcription factor is required for female sexual behavior and reproductive longevity. Horm Behav 46(4):420–427

    Article  CAS  PubMed  Google Scholar 

  • Khorram O et al (2004) Expression of aryl hydrocarbon receptor (AHR) and aryl hydrocarbon receptor nuclear translocator (ARNT) mRNA expression in human spermatozoa. Med Sci Monit 10(5):BR135–BR138

    CAS  PubMed  Google Scholar 

  • King AE et al (2010) An additive interaction between the NF κ B and estrogen receptor signalling pathways in human endometrial epithelial cells. Hum Reprod 25(2):510–518

    Article  CAS  PubMed  Google Scholar 

  • Knap PW, Su G (2008) Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis. Animal 2:1742–1747

    Article  CAS  PubMed  Google Scholar 

  • Kolmodin R et al (2002) Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric Scand 52:11–24

    Google Scholar 

  • Machado MA et al (2010) Genome wide scan for quantitative trait loci affecting tick resistance in cattle (Bos taurus × Bos indicus). BMC Genom 11:280

    Article  Google Scholar 

  • Maere S et al (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21(16):3448–3449

    Article  CAS  PubMed  Google Scholar 

  • Mota RR et al (2016) Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models. Genet Sel Evol 48:3

    Article  PubMed  PubMed Central  Google Scholar 

  • Onteru SK et al (2012) A whole-genome association study for pig reproductive traits. Anim Genet 43(1):18–26

    Article  CAS  PubMed  Google Scholar 

  • Pasquali R et al (2007) Obesity and infertility. Curr Opin Endocrinol Diabetes Obes 14:482–487

    Article  PubMed  Google Scholar 

  • Ramayo-Caldas Y et al (2014) From SNP co-association to RNA co-expression: Novel insights into gene networks for intramuscular fatty acid composition in porcine. BMC Genom 15(1):1

    Article  Google Scholar 

  • Ramos AM et al (2009) Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE 4:e6524

    Article  PubMed  PubMed Central  Google Scholar 

  • Rauw WM, Gomez-Raya L (2015) Genotype by environment interaction and breeding for robustness in livestock. Front Genet 6:310

    Article  PubMed  PubMed Central  Google Scholar 

  • Robker RL et al (2014) Identification of sites of STAT3 action in the female reproductive tract through conditional gene deletion. PloS one 9(7):e101182

    Article  PubMed  PubMed Central  Google Scholar 

  • Rothschild MF, Bidanel JP (1998) Biology and genetics of reproduction. In: Rothschild MF, Ruvinsky A (eds) The Genetics of the Pig. CAB Int., Wallingford, pp 313–343

    Google Scholar 

  • Safarinejad MR et al (2010) Association of polymorphisms in the estrogen receptors alpha, and beta (ESR1, ESR2) with the occurrence of male infertility and semen parameters. J Steroid Biochem Mol Biol 122(4):193–203

    Article  CAS  PubMed  Google Scholar 

  • Sandelin A et al (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32:D91–D94

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Santoro M et al (2013) Sperm metabolism in pigs: a role for peroxisome proliferator-activated receptor gamma (PPARγ). J Exp Biol 216(6):1085–1092

    Article  CAS  PubMed  Google Scholar 

  • Sell-Kubiak E et al (2015) Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genom 16(1):1

    Article  Google Scholar 

  • Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Silva FF et al (2014) Sire evaluation for total number born in pigs using a genomic reaction norms approach. J Anim Sci 92(9):3825–3834

    Article  CAS  PubMed  Google Scholar 

  • Song Y et al (2012) NF-kappaB expression increases and CFTR and MUC1 expression decreases in the endometrium of infertile patients with hydrosalpinx: a comparative study. Reprod Biol Endocrinol 10:86

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Souza CJ et al (2001) The Booroola (FecB) phenotype is associated with a mutation in the bone morphogenetic receptor type 1 B (BMPR1B) gene. J Endocrinol 169(2):R1–R6

    Article  CAS  PubMed  Google Scholar 

  • Steffl M et al (2008) Expression of transforming growth factor-beta3 (TGF- beta3) in the porcine ovary during the oestrus cycle. Histol Histopathol 23:665–671

    CAS  PubMed  Google Scholar 

  • Sugiura K et al (2005) Oocyte control of metabolic cooperativity between oocytes and companion granulosa cells: energy metabolism. Dev Biol 279(1):20–30

    Article  CAS  PubMed  Google Scholar 

  • Tomas A et al (2006) An association study between polymorphisms of the porcine bone morphogenetic protein receptor type1β (BMPR1B) and reproductive performance of Iberian × Meishan F2 sows. Anim Genet 37(3):297–298

    Article  CAS  PubMed  Google Scholar 

  • Touzet H, Varré JS (2007) Efficient and accurate P-value computation for position weight matrices. Algorithms Mol Biol 2:15. doi:10.1186/1748-7188-2-15.

    Article  PubMed  PubMed Central  Google Scholar 

  • Van Raden P (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423

    Article  Google Scholar 

  • Verardo LL et al (2016a) Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways. Genet Sel Evol 48(1):1

    Article  Google Scholar 

  • Verardo LL et al (2016b) After genome-wide association studies: Gene networks elucidating candidate genes divergences for number of teats across two pig populations. J Anim Sci. doi:10.2527/jas.2015-9917

    PubMed  Google Scholar 

  • Veroneze R et al (2014) Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations. BMC Genet 15(1):126

    Article  PubMed  PubMed Central  Google Scholar 

  • Webster KE et al (2005) Meiotic and epigenetic defects in Dnmt3L-knockout mouse spermatogenesis. Proc Natl Acad Sci 102(11):4068–4073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wei Z et al (2014) Targeted deletion of C1q/TNF-related protein 9 increases food intake, decreases insulin sensitivity, and promotes hepatic steatosis in mice. Am J Physiol—Endocrinol Metab 306(7):E779–E790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wendling O et al (1999) Retinoid X receptors are essential for early mouse development and placentogenesis. Proc Natl Acad Sci 96(2):547–551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), National Institute of Science and Technology–Animal Science, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/NUFFIC, CAPES/DGU, and CAPES/PDSE), and Wageningen University and Topigs Norsvin Research Center.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lucas L. Verardo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Fig. 1—Number of records (y axis) across the number of sires (x axis) used in this study. (PDF 50 KB)

Supplementary material 2 (PDF 45 KB)

Supplementary material 3 (XLSX 56 KB)

335_2017_9696_MOESM4_ESM.pdf

Supplementary Fig. 2—Manhattan plots for the Low, Mean and High environments groups. It was build based on the average SNP variance (y axis) of the levels from each group over chromosome regions (x axis). (PDF 529 KB)

Supplementary material 5 (XLSX 115 KB)

335_2017_9696_MOESM6_ESM.xlsx

Supplementary Table 4—Biological process identified for the three sets of Transcription factors (TF) identified for total number born in each HYM group (Low, Mean and High). The table presents the gene ontology identification (GO-ID), p-values, biological process description and TFs in the test set (XLSX 67 KB)

335_2017_9696_MOESM7_ESM.pdf

Supplementary Fig. 3—Gene-Transcription Factor (TF) network for the Low group. Genes overlapping with relevant SNPs and/or LD blocks for TNB in the Low group (blue nodes), and in common to all groups (pink nodes). Green nodes with blue border are genes in common to Mean group. Associated with these genes, we have TFs (yellow nodes). The node size corresponds to the network analyses (Cytoscape) score where bigger nodes represent higher edges density associated with the number of TF binding sites. (PDF 339 KB)

335_2017_9696_MOESM8_ESM.pdf

Supplementary Fig. 4—Gene-Transcription Factor (TF) network for the Mean group. Genes overlapping with relevant SNPs and/or LD blocks for TNB in the Mean group (green nodes), and in common to all groups (pink nodes). Green nodes with blue and red borders are genes in common to Low and High group, respectively. Associated with these genes, we have TFs (yellow nodes). The node size corresponds to the network analyses (Cytoscape) score where bigger nodes represent higher edges density associated with the number of TF binding sites. (PDF 361 KB)

335_2017_9696_MOESM9_ESM.pdf

Supplementary Fig. 5—Gene-Transcription Factor (TF) network for the High group. Genes overlapping with relevant SNPs and/or LD blocks for TNB in the High group (red nodes), and in common to all groups (pink nodes). Green nodes with red borders are genes in common to Mean group. Associated with these genes, we have TFs (yellow nodes). The node size corresponds to the network analyses (Cytoscape) score where bigger nodes represent higher edges density associated with the number of TF binding sites. (PDF 368 KB)

335_2017_9696_MOESM10_ESM.pdf

Supplementary Fig. 6—Venn diagram showcasing the number of genes and TF in common between the networks from each group: Low, Mean and High. (PDF 24 KB)

335_2017_9696_MOESM11_ESM.pdf

Supplementary Fig. 7—Graph containing the 95% Confidence Interval (based on standard error for solutions of mixed models) for the random slopes (y axis) of the reaction norms for the total number born among 340 sires (horizontal line). (PDF 54 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verardo, L.L., Lopes, M.S., Mathur, P. et al. Gene networks for total number born in pigs across divergent environments. Mamm Genome 28, 426–435 (2017). https://doi.org/10.1007/s00335-017-9696-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00335-017-9696-5

Keywords

Navigation