Comparative Genomic Analysis on Novel Genes Associated with Egg Production and Disease Resistance in Layer Hens
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Abstract
Introduction: Poultry layer breeds have undergone extensive selection for egg production traits, yet the genetic basis of many economically important characteristics remains incompletely understood. In the present study, a comprehensive comparative genomic analysis of multiple commercial and indigenous poultry layer breeds was conducted to identify novel genes associated with egg production and disease resistance, such as viral and bacterial infections, particularly through innate immune pathways.
Materials and methods: A comprehensive comparative genomic analysis was conducted using whole-genome sequencing data from 135 individuals, including 30 commercial layers (White leghorn and Rhode Island red), 90 indigenous chickens from six local breeds, and 15 red jungle fowl representing the ancestral population. Using fixation index scans, haplotype-based selection analysis, and Tajima’s D, genomic regions under positive selection were identified. By integrating gene prediction tools and protein function analysis, 12 novel genes in layer breeds with strong selection signals and potential roles in egg production and immune response were identified.
Results: In silico functional analysis, utilizing protein domain annotation, structural modeling, and pathway enrichment, suggested that these novel genes are involved in eggshell formation, egg production, immune response, and metabolic regulation. Notably, GALLUS-NOVEL-3, a previously uncharacterized gene with high predicted oviduct expression and strong selection signatures in commercial layers, may play a role in calcium transport and eggshell mineralization. Protein structure prediction and domain analysis further supported the potential functionality of these novel genes, revealing conserved features linked to reproductive physiology and immune defense.
Conclusion: The present findings provided new insight into the genetic basis of economically important traits in layer chickens and highlight promising targets for breeding programs aimed at improving egg production and disease resistance.
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References
Burt DW. Chicken genome: Current status and future opportunities. Genome Res. 2005; 15(12): 1692-1698. DOI: 10.1101/gr.4141805
Qanbari S, Rubin CJ, Maqbool K, Weigend S, Weigend A, Geibel J, et al. Genetics of adaptation in modern chicken. PLoS Genet. 2019; 15(4): e1007989. DOI: 10.1371/journal.pgen.1007989
Tixier-Boichard M, Leenstra F, Flock DK, Hocking PM, and Weigend S. A century of poultry genetics. World's Poult Sci J. 2012; 68(2): 307-321. DOI: 10.1017/S0043933912000360
Wolc A, Kranis A, Arango J, Settar P, Fulton JE, O'Sullivan NP, et al. Implementation of genomic selection in the poultry industry. Anim Front. 2016; 6(1): 23-31. DOI: 10.2527/af.2016-0004
Liu Z, Yang N, Yan Y, Li G, Liu A, Wu G, et al. Genome-wide association analysis of egg production performance in chickens across the whole laying period. BMC Genet. 2019; 20: 67. DOI: 10.1186/s12863-019-0771-7
Fulton JE. Avian genetic stock preservation: An industry perspective. Poult Sci. 2006; 85(2): 227-231. DOI: 10.1093/ps/85.2.227
Georges M, Charlier C, and Hayes B. Harnessing genomic information for livestock improvement. Nat Rev Genet. 2019; 20(3): 135-156. DOI: 10.1038/s41576-018-0082-2
Andersson L, and Georges M. Domestic-animal genomics: Deciphering the genetics of complex traits. Nat Rev Genet. 2004; 5(3): 202-212. DOI: 10.1038/nrg1294
Rubin CJ, Zody MC, Eriksson J, Meadows JRS, Sherwood E, Webster MT, et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 2010; 464(7288): 587-591. DOI: 10.1038/nature08832
Tixier-Boichard M, Bed'hom B, and Rognon X. Chicken domestication: From archeology to genomics. Comptes Rendus Biol. 2011; 334(3): 197-204. DOI: 10.1016/j.crvi.2010.12.012
Minga UM, Msoffe PL, and Gwakisa PS. Biodiversity (variation) in disease resistance and in pathogens within rural chicken populations. World's Poult Sci J. 2004; 60(4): 516-525.
Wragg D, Mwacharo JM, Alcalde JA, Hocking PM, and Hanotte O. Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens. Heredity. 2012; 109(1): 6-18. DOI: 10.1038/hdy.2012.9
Wang K, Hu H, Tian Y, Li J, Scheben A, Zhang C, et al. The chicken pan-genome reveals gene content variation and a promoter region deletion in IGF2BP1 affecting body size. Mol Biol Evol. 2021; 38(11): 5066-5081. DOI: 10.1093/molbev/msab231
Tautz D, and Domazet-Lošo T. The evolutionary origin of orphan genes. Nat Rev Genet. 2011; 12(10): 692-702. DOI: 10.1038/nrg3053
Wu P, Yan J, Lai YC, Ng CS, Li A, Jiang X, et al. Multiple regulatory modules are required for scale-to-feather conversion. Mol Biol and Evol. 2018; 35(2): 417-430. DOI: 10.1093/molbev/msx295
Cheng HH, Kaiser P, and Lamont SJ. Integrated genomic approaches to enhance genetic resistance in chickens. Annu Rev Anim Biosci. 2019; 1: 239-260. DOI: 10.1146/annurev-animal-031412-103701
Liu C, Liu J, Guo H, Liu S, Liu P, Zhu T, et al. Whole-genome sequencing revealed genetic structure, patterns of selection and molecular identity card in "Yufen 1" D line chickens. Poult Sci. 2025: 105377. DOI: 10.1016/j.psj.2025.105377
Xu D, Zhu W, Wu Y, Wei S, Shu G, Tian Y, et al. Whole-genome sequencing revealed genetic diversity, structure and patterns of selection in Guizhou indigenous chickens. BMC Genom. 2023; 24(1): 570. DOI: 10.1186/s12864-023-09621-w
Tan X, Zhang J, Dong J, Huang M, Li Q, Wang H, et al. Whole-genome variants dataset of 209 local chickens from China. Sci Data. 2024; 11(1): 169. DOI: 10.1038/s41597-024-02995-w
Andrews S. FastQC: A quality control tool for high throughput sequence data. 2010. Available at: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Bolger AM, Lohse M, and Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15): 2114-2120. DOI: 10.1093/bioinformatics/btu170
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at: https://arxiv.org/abs/1303.3997; 2013.
Broad institute. Picard toolkit. Broad Institute, GitHub repository. 2019. Available at: https://broadinstitute.github.io/picard/
Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, et al. Multi-view variational autoencoder for missing value imputation in untargeted metabolomics. ArXiv. 2024; arXiv:2310.07990v2. DOI: 10.1101/gr.107524.110
Patterson N, Price AL, and Reich D. Population structure and eigenanalysis. PLoS Genet. 2006; 2(12): e190. DOI: 10.1371/journal.pgen.0020190
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011; 27(15): 2156-2158. DOI: 10.1093/bioinformatics/btr330
Szpiech ZA, and Hernandez RD. Selscan: An efficient multithreaded program to perform EHH-based scans for positive selection. Mol Biol Evol. 2014; 31(10): 2824-2827. DOI: 10.1093/molbev/msu211
Stanke M, Keller O, Gunduz I, Hayes A, Waack S, and Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucl Acids Res. 2006; 34(Suppl 2): W435-W439. DOI: 10.1093/nar/gkl200
Cantarel BL, Korf I, Robb SM, Parra G, Ross E, Moore B, et al. (2008). MAKER: An easy-to-use annotation pipeline designed for emerging model organism genomes. Genom Res. 18(1): 188-196. DOI: 10.1101/gr.6743907
Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, et al. CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucl Acids Res. 2017; 45(W1): W12-W16. DOI: 10.1093/nar/gkx428
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucl Acids Res. 1997; 25(17): 3389-3402. DOI: 10.1093/nar/25.17.3389
Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019; 37(4): 420-423. DOI: 10.1038/s41587-019-0036-z
Krogh A, Larsson B, Von Heijne G, and Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J Mol Biol. 2001; 305(3): 567-580. DOI: 10.1006/jmbi.2000.4315
Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol. 1999; 292(2): 195-202. DOI: 10.1006/jmbi.1999.3091
Yang J, Yan R, Roy A, Xu D, Poisson J, and Zhang Y. The I-TASSER suite: Protein structure and function prediction. Nat Methods. 2015; 12(1): 7-8. DOI: 10.1038/nmeth.3213
Wiederstein M, and Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucl Acids Res. 2007; 35(S2): W407-W410. DOI: 10.1093/nar/gkm290
Sormanni P, Aprile FA, and Vendruscolo M. The CamSol method of rational design of protein mutants with enhanced solubility. J Mol Biol. 2015; 427(2): 478-490. DOI: 10.1016/j.jmb.2014.09.026
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. Correction to the STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucl Acids Res. 2021; 49(18): D605-D612. DOI: 10.1093/nar/gkab835
R Core Team. R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, 2021. Available at: https://cir.nii.ac.jp/crid/1574231874043578752#citations_container
Wickham H. ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag; 2016. Available at: https://link.springer.com/book/10.1007/978-3-319-24277-4
Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. (2009). Circos: An information aesthetic for comparative genomics. Genome Res. 19(9): 1639-1645. DOI: 10.1101/gr.092759.109
Kolde R. Pheatmap: Pretty heatmaps. R package version 1.0.12. 2019. Available at: https://cran.r-project.org/web/packages/pheatmap/index.html
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11): 2498-2504. DOI: 10.1101/gr.1239303
International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004; 432(7018): 695-716. DOI: 10.1038/nature03154