Gene set enrichment analysis to identify genes and biological pathways associated with body weight in chicken

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

2 Associate Professor, Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

Abstract

The aim of the present study was to conduct a genome wide association studies (GWAS) based on gene set enrichment analysis for identifying the loci associated with body weight in four chicken breeds including Chahua, Silkie, Langshan, and Beard using the high throughput single nucleotide polymorphisms (SNPs). Phenotypic and genotypic data were obtained from the Frontiersin online public repository. In this study, the 402 cocks and hens were used with body weight from 1 to 15 weeks using GenABEL software. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in GO, KEEG, DAVID and PANTHER databases. Ten SNP markers on  chromosomes 1, 2, 5, 7, 10, 14, 18, 19, 20 and 27 located in ABCG1, MYOD1, MYH10, MYH11, MYO1B, MYO1C, MYO1E, MYL1, MYL2, MYL3, SLC2A8, ACACA, ACOX1, ACOX2, and PNPLA2 genes were identified. Some of the genes were found are consistent with some previous studies and to be involved in biological pathways related to body weight. According to pathway analysis, 17 pathways from gene ontology and KEGG pathway were associated with the body weight (P˂0.01). Among those pathways, the cytoskeletal protein binding, anatomical structure development and tricarboxylic acid cycle had significant association with skeletal muscle fiber and metabolism lipid traits. In total, this study supported previous results from GWAS of body weight; also revealed additional regions in the chicken genome associated with these economically important traits. The use of these findings can accelerate the genetic progress in the breeding programs.

Keywords

Main Subjects

References

Abdalla E. A., Peñagaricano F., Byrem T. M., Weigel K. A. and Rosa G. J. 2016. Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population. Animal Genetics, 47: 395-407.
Abdalla B. A., Chen J., Nie Q. and Zhang X. 2018. Genomic insights in to the multiple factors controlling abdominal fat deposition in a chicken model. Frontiers in Genetics, 19­(9): 262-271.
Almeida O. A. C., Moreira G. C. M., Rezende F. M., Boschiero C., de Oliveira Peixoto J., Ibelli A. M. G., Ledur M. C., de Novais F. J. and Coutinho L. L. 2019. Identification of selection signatures involved in performance traits in a paternal broiler line. BMC Genomics, 20(1): 449-461.
Aulchenko Y. S., Ripke S., Isaacs A. and van Duijn C. M. 2007. GenABEL: an R library for genome-wide association analysis. Bioinformatics, 23: 1294-1296.
Chen B., Xu J., He X., Xu H., Li G., Du H., Nie Q. and Zhang X. 2015. A genome-wide mRNA screen and functional analysis reveal FOXO3 as a candidate gene for chicken growth. PLoS One, 10(9): e0137087.
Coudert E., Praud C., Dupont J., Crochet S., Cailleau-Audouin E., Bordeau T., Godet E., Collin A., Berri C., Tesseraud S. and Métayer-Coustard S. 2018. Expression of glucose transporters SLC2A1, SLC2A8, and SLC2A12 in different chicken muscles during ontogenesis. Journal of Animal Science, 96(2): 498-509.
Cui H. X., Liu R. R., Zhao G. P., Zheng M. Q., Chen J. L. and Wen J. 2012. Identification of differentially expressed genes and pathways for intramuscular fat deposition in pectoralis major tissues of fast-and slow-growing chickens. BMC Genomics, 13: 213-220.
Dadousis C., Pegolo S., Rosa G. J. M., Gianola D., Bittante G. and Cecchinato A. 2017. Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle. Journal of Dairy Science, 100: 1223-1231.  
Durinck S., Spellman P. T., Birney E. and Huber W. 2009. Mapping identifiers for the integration of genomic datasets with the R/bioconductor package biomaRt. Nature Protocols, 4: 1184-1191.
Emrani H., Vaez Torshizi R., Masoudi A. A. and Ehsani A. R. 2017. Identification of new loci for body weight traits in F2 chicken population using genome-wide association study. Livestock Science, 206: 125-131.
Li Y., Chen Y., Jin W., Fu S., Li D., Zhang Y., Sun G., Jiang R., Han R., Li Z., Kang X. and Li G. 2019. Analyses of microRNA and mRNA expression profiles reveal the crucial interaction networks and pathways for regulation of chicken breast muscle development. Frontiers in Genetics, 10: 197-205.
Mooney M. A. and Wilmot B. 2015. Gene Set Analysis: A Step-By-Step Guide. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168: 517-527.
Ouyang H., Wang Z., Chen X., Yu J., Li Z. and Nie Q. 2017. Proteomic analysis of chicken skeletal muscle during embryonic development. Frontiers in Physiology, 8: 281-289.
Park J. W., Lee J. H., Kim S. W., Han J. S., Kang K. S., Kim S. J. and Park T. S. 2018. Muscle differentiation induced up-regulation of calcium-related gene expression in quail myoblasts. Asian-Australasian Journal of Animal Science, 31(9): 1507-1515.
Peñagaricano F., Weigel K. A., Rosa G. J. and Khatib H. 2013. Inferring quantitative trait pathways associated with bull fertility from a genome-wide association study. Frontiers in Genetics, 3: 307-314.  
Peng G., Luo L., Siu H., Zhu Y., Hu P., Hong S., Zhao J., Zhou X., Reveille J. D., Jin L., Amos C. I. and Xiong M. 2010. Gene and pathway-based second wave analysis of genome-wide association studies. European Journal of Human Genetics, 18: 111-117.
Powell J. A. 2014. GO2MSIG, an automated GO based multi-species gene set generator for gene set enrichment analysis. BMC Bioinformatics, 15: 146-149.
Resnyk C. W., Chen C., Huang H., Wu C. H., Simon J., Le Bihan-Duval E., Duclos M. J. and Cogburn L. A. 2015. RNA-Seq analysis of abdominal fat in genetically fat and lean chickens highlights a divergence in expression of genes controlling adiposity, hemostasis, and lipid metabolism. PLoS One, 9­(10): e0139549.
Seabury C. M., Oldeschulte D. L., Saatchi M., Beever J. E., Decker J. E., Halley Y. A., Bhattarai E. K., Molaei M., Freetly H. C., Hansen S. L., Yampara-Iquise H., Johnson K. A., Kerley M. S, Kim J., Loy D. D., Marques E., Neibergs H. L., Schnabel R. D., Shike D. W., Spangler M. L., Weaber R. L., Garrick D. J. and Taylor J. F. 2017. Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle. BMC Genomics, 18(1): 386-396.
Wang L., Jia P., Wolfinger R. D., Chen X. and Zhao Z. 2011. Gene set analysis of genome-wide association studies: Methodological issues and perspectives, Genomics, 98: 1-8.
Young M. D., Wakefield M. J., Smyth G. K. and Oshlack A. 2010. Method gene ontology analysis for RNA-seq: Accounting for selection bias. Genome Biology, 11: 14-23.
Yousefi Zonuz A., Alijani A., Mohammadi H., Rafat S. A. and Daghigh Kia H. 2013. Estimation of genetic parameters for productive and reproductive traits in Esfahan native chickens. Journal of Livestock Science and Technologies, 1: 36-40.
Ye M., Ye F., He L., Luo B., Yang F., Cui C., Zhao X., Yin H., Li D., Xu H., Wang Y. and Zhu Q. 2017. Transcriptomic analysis of chicken Myozenin 3 regulation reveals its potential role in cell proliferation. PLoS One, 13(12): e0189476.
Yuan Y., Peng D., Gu X., Gong Y., Sheng Z. and Hu X. 2018. Polygenic basis and variable genetic architectures contribute to the complex nature of body weight -A genome-wide study in four Chinese indigenous chicken breeds. Frontiers in Genetics, 2(9): 229-238.
Animal Production Research
Volume 9, Issue 3
December 2020
Pages 47-57

Files

Share

How to cite

Statistics