Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X

Identification of genomic regions under positive selection in broiler chickens using a linkage disequilibrium-based method

Document Type : Research Paper

Authors

Hossein Mohammadi 1
Amir Hossein Khaltabadi Farahani 2
Mahdieh Mehdipour 3

1 Corresponding Author, Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran. E-mail: H-mohammadi64@araku.ac.ir

2 Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran. Email: a-farahanikh@araku.ac.ir

3 Department of Agriculture, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran. Email: mehdipourm@tabrizu.ac.ir

https://doi.org/10.22059/jap.2025.384854.623812
Abstract
Objective: Identifying selection signatures  can provide valuable insights into genomic regions that are under positive selection, which in turn leads to a better understanding of genotype-phenotype relationships Over the past few decades,, selection programs in broiler chicken have been based on fast growing and increased feed efficiency. On the other hand, this selection for rapid growth has been resulted an accumulation of fatty tissue and decrease of chicken meat quality, it has been new challenges in poultry breeding due to genetic correlation between rapid growing and fat deposition. The aim of the present study was to identify genomic regions under positive selection associated with body fat deposition in seven-week-old broiler chickens  using a statistical method based on linkage disequilibrium.
Materials and Methods: In the present study, a total 475 chickens from two chicken lines divergently selected were obtained using the Illumina chicken 60 K SNP chip. The broilers used in this study were from two Chinese broiler lines. In the first step, for the detected regions of the genome were evaluated using the XP-EHH method based on linkage disequilibrium using Selscan software v.2.0. Candidate genomic regions and genes were identified by SNPs located at 1% upper range of XP-EHH values in ten creeping windows. Finally, GeneCards and DAVID databases were also used to interpret the function of the obtained genes. Additionally, the latest published version of Animal genome database was used for defining QTLs associated with fat deposition traits in identified locations.
Results: Candidate genes STAB2, TAPT1, JDP2, FNDC3B, PTPN11, ADIPOR1 and SLC44A3 obtained these regions. Further investigation using bioinformatics tools showed these genomic regions overlapped with lipid metabolism, fatty acid transport, lipoprotein receptors, glucose metabolism and homeostasis. Various genes that were founded within these regions can be considered as candidates under selection based on function. Also, a survey on extracted QTLs showed that these QTLs involved in some economically important traits in chicken such as abdominal fat weight and carcass fat weight traits.
Conclusion: However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Identifying important economic traits and locating parts of the genome that have changed as a result of selection could be used in poultry breeding programs. The results of our research can be used to understand the genetic mechanism controlling fat deposition trait and using these findings could potentially be useful for genetic selection in chicken for increasing body weight while reducing body fat deposition during a broiler breeding.

Keywords

Abdominal fat
Candidate gene
Chicken
Linkage disequilibrium
Signature of selection
Reference
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., & Coutinho, L.L. (2019). Identification of selection signatures involved in performance traits in a paternal broiler line. BMC Genomics, 20(1), 449.
Coulombe, G., & Rivard, N. (2016). New and Unexpected Biological Functions for the Src-Homology 2 Domain-Containing Phosphatase SHP-2 in the Gastrointestinal Tract. Cell Molecular Gastroenterology Hepatology, 2, 11-21.
Chen, L., X. Wang, D. Cheng, K. Chen, Y. Fan, G. Wu, J. You, S. Liu, H. Mao, and J. Ren. 2019. Population genetic analyses of seven Chinese indigenous chicken breeds in a context of global breeds. Animal Genetics, 50, 82-86.
da Cruz, V.A., Schenkel, F.S., Savegnago, R.P., Grupioni, N.V., Stafuzza, N.B., Sargolzaei, M., Ibelli, A.M., Peixoto, Jde, O., Ledur, M.C., & Munari, D.P. (2015). Association of Apolipoprotein B and Adiponectin Receptor 1 Genes with Carcass, Bone Integrity and Performance Traits in a Paternal Broiler Line. PLoS One, 10(8), e0136824.
Dou, D., Shen, L., Zhou, J., Cao, Z., Luan, P., Li, Y., Xiao, F., Guo, H., Li, H., & Zhang, H. (2022). Genome-wide association studies for growth traits in broilers. BMC Genom Data, 23(1), 11. 
Gurgul, A., Szmatoła, T., Ropka-Molik, K., Jasielczuk, I., Pawlina, K., Semik, E., & Bugno-Poniewierska, M. (2015). Identification of genome-wide selection signatures in the Limousin beef cattle breed. Journal of Animal & Breeding Genetics, 3, 1-13.
Hu, M., Jiang, H., Lai, W., Shi, L., Yi, W., Sun, H., Chen, C., Yuan, B., Yan, S., & Zhang, J. (2023). Assessing Genomic Diversity and Signatures of Selection in Chinese Red Steppe Cattle Using High-Density SNP Array. Animals, 13, 1717.
Letaief, R., Rebours, E., Grohs, C., Meersseman, C., Fritz, S., Trouilh, L., Esquerré, D., Barbieri, J., Klopp, C., Philippe, R., & Blanquet, V. (2017). Identification of copy number variation in French dairy and beef breeds using next-generation sequencing. Genetics Selection Evolution, 49(1), 77.
Liu, D., Chen, Z., Zhao, W., Guo, L., Sun, H., Zhu, K., Liu, G., Shen, X., Zhao, X., Wang, Q., Ma, P., & Pan Y. (2021). Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits. BMC Genomics, 22(1), 747. 
Mastrangelo, S., Ben-Jemaa, S., Perini, F., Cendron, F., Biscarini, F., Lasagna, E., Penasa, M., & Cassandro, M. (2023). Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens. Genetics Selection Evolution, 55(1), 20.
Moreira, G.C.M., Boschiero, C., Cesar, A.S.M., Reecy, J.M., Godoy, T.F., Trevisoli, P.A., Cantão, M.E., Ledur, M.C., Ibelli, A.M.G., Peixoto, J.O., Moura, A.S.A.M.T., Garrick, D., & Coutinho, L.L. (2018). A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens. BMC Genomics, 19(1), 374. 
Moreira, G.C.M., Salvian, M., Boschiero, C., Cesar, A.S.M., Reecy, J.M., Godoy, T.F., Ledur, M.C., Garrick, D., Mourão, G.B., & Coutinho, L.L. (2019). Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens. BMC Genomics, 20(1), 669. 
Nadaf, J., Pitel, F., Gilbert, H., Duclos, M.J., Vignoles, F., Beaumont, C., Vignal, A., Simon, J., & Le Bihan-Duval, E. (2009). QTL for several metabolic traits map to loci controlling growth and body composition in an F2 intercross between high- and low-growth chicken lines. Physiological Genomics, 38(3), 241-9.
Nones, K.M., Ledur, M.C., Zanella, E.L., Klein, C., Pinto, L.F., Moura, A.S., Burt, D.W., & Coutinho, L.L. (2012). Quantitative trait loci associated with chemical composition of the chicken carcass. Animal Genetics, 43(5), 570-576.
Javanrouh, A., Vaez Torshizi, R., Masoudi, A. A., & Ehsani, A. (2017). Genome wide association study (GWAS) for body composition traits in a F2 population crosses of Arian broiler line and Azerbaijan native chicken. Animal Sciences Journal, 30(116), 231-246. (In Persian)
Pouraskari, M., Harakinezhad, T., & Zandi, M. B. (2019). Study of SNPs associated with carcass fat in Afshari, Moghani and Qezel sheep breeds. Animal Sciences Journal, 32(123), 247-258. (In Persian).
Qi, L., Xiao, L., Fu, R., Nie, Q., Zhang, X., & Luo, W. (2024). Genetic characteristics and selection signatures between Southern Chinese local and commercial chickens. Poultry Science, 103(7), 103863.
Rachman, M.P., Bamidele, O., & Dessie, T. (2024).  Genomic analysis of Nigerian indigenous chickens reveals their genetic diversity and adaptation to heat-stress. Scientific Reports, 14, 2209.
Romanov, M.N., Shakhin, A.V., Abdelmanova, A.S., Volkova, N.A., Efimov, D.N., Fisinin, V.I., Korshunova, L.G., Anshakov, D.V., Dotsev, A.V., & Griffin, D.K. (2024). Dissecting Selective Signatures and Candidate Genes in Grandparent Lines Subject to High Selection Pressure for Broiler Production and in a Local Russian Chicken Breed of Ushanka. Genes, 15, 524.
Tan, X., Liu, R., Li, W., Zheng, M., Zhu, D., Liu, D., Feng, F., Li, Q., Liu, L., Wen, J., & Zhao, G. (2022). Assessment the effect of genomic selection and detection of selective signature in broilers. Poultry Science, 101(6), 101856.
Voight, B.F., Kudaravalli, S., Wen, X., & Pritchard, J.K. (2006). A map of recent positive selection in the human genome. PLoS Biology, 4, e72.
Waineina, R.W., Okeno, T.O., Ilatsia, E.D., & Ngeno, K. (2022). Selection Signature Analyses Revealed Genes Associated with Adaptation, Production, and Reproduction in Selected Goat Breeds in Kenya. Frontiers in Genetics, 13, 858923.
Xie, X.F., Wang, Z.Y., Zhong, Z.Q., Pan, D.Y., Hou, G.Y., & Xiao, Q. (2024). Genome-wide scans for selection signatures in indigenous chickens reveal candidate genes associated with local adaptation. Animal,18(5),101151.
Zhang, H., Du, Z.Q., Dong, J.Q., & Wang, H.X. (2014). Detection of genome-wide copy number variations in two chicken lines divergently selected for abdominal fat content. BMC Genomics, 15, e517.
Zhu, Y., Liu, X., Wang, Y., Liu, L., Wang, Y., Zhao, G., Wen, J., & Cui, H. (2022). Genome-Wide Association Study Revealed the Effect of rs312715211 in ZNF652 Gene on Abdominal Fat Percentage of Chickens. Biology, 11, 1849.
Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X
Volume 27, Issue 1
Winter 2025
Pages 1-14

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us