Identifying candidate genes for late-feathering subtypes of the Shouguang Chicken by genome-wide association study and differential expression analysis

Identifying candidate genes for late-feathering subtypes of the Shouguang Chicken by genome-wide association study and differential expression analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 October 2025 V1 Latest version Share on Identifying candidate genes for late-feathering subtypes of the Shouguang Chicken by genome-wide association study and differential expression analysis Authors : Xiayi Liu , junying li , and Haigang Bao 0000-0003-4591-3957 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176039289.96893980/v1 Published Animal Genetics Version of record Peer review timeline 201 views 139 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Late-feathering phenotype can be divided into different subtypes based on the length difference between primary and primary-covert feathers of one-day old chicks. In this study, a chick with its primary feathers shorter than primary-covert feathers is identified as Subtype 1 (T1), a chick with its primary feathers as long as primary-covert feathers is Subtype 2 (T2). The objective of this study was to obtain candidate genes for late-feathering subtypes in Shouguang chickens. Fifty-three Shouguang chickens were genotyped with a 600 K SNP chip and two genomic regions in Chromosome 3 and 10 were identified to be associated with late-feathering subtypes by GWAS. Twelve hair follicle samples of flight feathers from 12 one-day old Shouguang chicks, including 6 of each subtype, were collected. RNA sequencing analysis was performed and no overlap genes were found between differentially expressed genes (DEGs) by differential expression analysis and candidate genes by GWAS. In order to find the relationship between the two set genes, a protein‐protein interaction (PPI) network was constructed using all protein-coding genes in the two sets and the result showed that four genes ( CDC40 , EFL1 , AK9 , and ZBTB24 ) from GWAS and 28 DEGs were enriched in the top one cluster associated with muscle function. Considering the spatiotemporal characteristics of gene differential expression and the fact that two most significant SNP loci in GWAS were located on EFL1 and CDC40 , we suggested that the two genes be more suitable as candidate genes for late-feathering subtypes. Title : Identifying candidate genes for late-feathering subtypes of the Shouguang Chicken by genome-wide association study and differential expression analysis Running title : Genes for chick late-feathering subtypes The full names of the authors: Xiayi Liu , Beijing Capital Agribusiness Co., Ltd. Bio-Breeding Technology Center, Beijing, China; State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, China; College of Animal Science and Technology, China Agricultural University, Beijing, China Junying Li , State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, China; College of Animal Science and Technology, China Agricultural University, Beijing, China Haigang Bao , State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, China; College of Animal Science and Technology, China Agricultural University, Beijing, China * Correspondence: Haigang Bao Email: [email protected] Identifying candidate genes for late-feathering subtypes of Shouguang chicken by genome-wide association study and differential expression analysis Xiayi Liu 1,2 , Junying Li 1 , Haigang Bao 1 * 1 State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China 2 Beijing Capital Agribusiness Co., Ltd. Bio-Breeding Technology Center, Beijing, China * Correspondence: Haigang Bao Email: [email protected] Late-feathering phenotype can be divided into different subtypes based on the length difference between primary and primary-covert feathers of one-day old chicks. In this study, a chick with its primary feathers shorter than primary-covert feathers is identified as Subtype 1 (T1), a chick with its primary feathers as long as primary-covert feathers is Subtype 2 (T2). The objective of this study was to obtain candidate genes for late-feathering subtypes in Shouguang chickens. Fifty-three Shouguang chickens were genotyped with a 600 K SNP chip and two genomic regions in Chromosome 3 and 10 were identified to be associated with late-feathering subtypes by GWAS. Twelve hair follicle samples of flight feathers from 12 one-day old Shouguang chicks, including 6 of each subtype, were collected. RNA sequencing analysis was performed and no overlap genes were found between differentially expressed genes (DEGs) by differential expression analysis and candidate genes by GWAS. In order to find the relationship between the two set genes, a protein‐protein interaction (PPI) network was constructed using all protein-coding genes in the two sets and the result showed that four genes ( CDC40 , EFL1 , AK9 , and ZBTB24 ) from GWAS and 28 DEGs were enriched in the top one cluster associated with muscle function. Considering the spatiotemporal characteristics of gene differential expression and the fact that two most significant SNP loci in GWAS were located on EFL1 and CDC40 , we suggested that the two genes be more suitable as candidate genes for late-feathering subtypes. Keywords GWAS, differential expression analysis, late-feathering subtypes, candidate gene, Shouguang chicken The autosexing system of the feathering rate is widely used in chicken industry. Briefly, a newborn chick within 24 hours after being hatched out with its primary feathers longer than its primary-covert feathers by more than 2 mm is identified as early-feathering (EF) phenotype, otherwise, it would be identified as late-feathering (LF) phenotype. The LF phenotype can be divided into different subtypes. Many researches focus on differences between EF and LF birds (Elferink et al., 2008; Luo et al., 2012;Derks et al., 2018; Zhao et al., 2016), while few on differences between LF subtypes. In this study, LF chicks with primary feathers shorter than their primary-covert feathers are identified as Subtype 1 (T1), primary feathers as long as their primary-covert feathers are identified as Subtype 2 (T2). Compared with T1, T2 type may be more suitable as a representative of LF phenotype to reduce the misjudgment of feathering phenotype in poultry production. We performed GWAS, differential expression analysis (DEA) and protein-protein interaction (PPI) network analysis to reveal reliable candidate genes for LF subtypes. Fifty-three blood samples of LF Shouguang chickens, including 29 T1 and 24 T2, were collected and genotyped with the 600K SNP Axiom® Genome-Wide Chicken Genotyping Array. GWAS was performed using Plink software with the basic association analysis (Purcell et al., 2007). The quality control parameters for individuals and SNPs were as follows: mind 0.1, geno 0.1, maf 0.05 and hwe 0.000001. Linkage disequilibrium (LD) decay distance for r 2 equal to 0.1 was estimated using PopLDdecay software (Zhang et al., 2019). Numbers (N) of non redundant SNPs were calculated by Plink operation of –indep-pairwise, and the significance threshold of GWAS was determined by 0.1/N. Twelve one-day-old healthy Shouguang chicks, including 6 of T1 and 6 of T2, were randomly selected. The procedures of wing feather follicles collection, RNA extraction, cDNA library construction and pair-end sequencing (PE, 150 bp) were described by Liu (2020). After removing low quality reads, DEA was performed using the pipeline of HISAT2, StringTie, and DESeq2. The criterions of | log2FoldChange | ≥ log2(1.5) and Pvalue ≤ 0.05 were used to filter differentially expressed genes (DEGs). After quality control, 53 chicks and 336457 SNPs were used in GWAS. The LD decay distance for r 2 equal to 0.1 was about 520 kb. After running the Plink operation of –indep-pairwise with 3 parameters of 520 kb, 20, and 0.1, we got the number of non-redundant SNPs as 3171, and the significance level of GWAS was set as P < 3.15E-5. The Manhattan plot of GWAS was shown as Figure 1A. From Figure 1A, we can see 2 obviously peeks with 4 significant SNPs in Chromosome 3 and 10, corresponding to 12 protein-coding genes around significant SNPs 200 Kb (Supplementary table S1). After removing low quality reads, totally 77.7 Gb clean data were obtained (Supplementary table S2). The Q30 value of each sample was greater than 93.7% and the alignment rate was above 93.3% against the reference of GRCg7b, which affirmed that RNA sequencing data was good enough for subsequent analysis. A total of 276 DEGs (Supplementary table S3, Supplementary file.vcf ) were detected out between T1 and T2 chicks. In order to understand DEGs’ roles in differences between T1 and T2 chicks, we used DAVID 6.8 (https://davidbioinformatics.nih.gov/) to perform GO and KEGG enrichments by default, and the results were shown in Figue 1B. From Figure 1B, we can see that DEGs be annotated to several GO terms or KEGG pathways significantly associated with muscle function, such as GO:0006936~muscle contraction, GO:0016459~myosin complex, GO:0005861~troponin complex, gga04820:Cytoskeleton in muscle cells, and gga04814:Motor proteins, etc. N o overlap gene was found between the results of DEA and GWAS. Considering the spatiotemporal characteristics of gene expression, we hypothesized that candidate genes from GWAS may be related to DEGs at certain developmental stages. Therefore, we merged all 97 known protein coding genes from DEA and GWAS together for a PPI network analysis using the STRING database (https://cn.string-db.org/) online by default. A PPI network was constructed with a enrichment p-value less than 1.0e-16, which meant that the proteins were at least partially biologically connected as a group. The top one PPI cluster was shown in Figure 1C. From Figure 1C, we can see that 32 protein-coding genes are enriched in the top one cluster associated with muscle function, which is consistent with DEA results (Figure 1B). There are no other muscles around hair follicles except for arrector pili muscles, so the DEGs and candidate genes from GWAS related to muscle functions may be related to the development of arrector pili muscles. Under cold or other stimulated conditions, arrector pili muscles will contract and cause hairs or feathers to stand up from skin surfaces. Some studies showed that arrector pili muscles may play roles in maintaining follicular integrity and stability (Torkamani et al., 2014). In this study, we found that protein-coding genes related to muscle function of arrector pili muscles were associated with different LF subtypes. In Figure 1C, four genes, namely CDC40 , EFL 1, AK9 , and ZBTB24 , were detected by GWAS (Supplementary table S1), while the other 28 genes were DEGs obtained through DEA (Supplementary table S2). Due to the spatiotemporal characteristics of gene differential expression and the fact that the two most significant SNP loci in GWAS were located on EFL1 and CDC40 , we suggested that the two genes be more suitable as candidate genes for LF subtypes in chicks. In summary, our results suggested that protein-coding genes related to muscle function of arrector pili muscles be associated with LF phenotype, and EFL1 and CDC40 be important candidate genes for LF subtypes in chicks. Acknowledgements This work was supported by China Agriculture Research System (CARS-40). Conflict of interest statement The authors have no conflict of interest to declare. References Derks, M.F.L., Herrero-Medrano, J.M., Crooijmans, R.P.M.A., Vereijken, A., Long, J.A., Megens, H., et al. (2018) Early and late feathering in turkey and chicken: same gene but different mutations. Genet. Sel. Evol., 50, 7. Elferink, M.G., Vallée, A.A., Jungerius, A.P., Crooijmans, R.P., Groenen, M.A. (2008) Partial duplication of the PRLR and SPEF2 genes at the late feathering locus in chicken. BMC Genomics , 9, 391. Liu, X., Zhou, W., Li, J., Bao, H., Wu., C. (2020) Genome-wide association study and transcriptome differential expression analysis of the feather rate in Shouguang chickens. Front. Genet., 11, 613078. Luo, C., Shen, X., Rao, Y., Xu, H., Tang, J., Sun, L., et al. (2012) Differences of Z chromosome and genomic expression between early- and late-feathering chickens. Mol. Biol. Rep ., 39(5), 6283-6288. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81(3), 559-575. Torkamani N., Rufaut, N.W., Jones, L., Sinclair, R.D. (2014) Beyond goosebumps: does the arrector pili muscle have a role in hair loss? Int. J. Trichology, 6(3), 88-94. Zhang, C., Dong, S.S., Xu, J.Y., He, W.M., Yang, T.L. (2019) PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics, 35(10), 1786-1788. Zhao, J., Yao, J., Li, F., Yang, Z., Sun, Z., Qu, L., et al. (2016) Identification of candidate genes for chicken early- and late-feathering. Poult. Sci., 95(7), 1498-1503. Figure 1. Screening candidate genes for late-feathering subtypes combining GWAS (A), RNA sequencing analysis (B) and protein-protein interaction analysis (C). Data availability The RNA sequencing data of this paper has been submitted to NGBC Database with the BioProject accession PRJCA047026. Supporting Information Supplementary table S1.xlsx Supplementary table S2.xlsx Supplementary table S3.xlsx Supplementary file.vcf Information & Authors Information Version history V1 Version 1 13 October 2025 Peer review timeline Published Animal Genetics Version of Record 11 May 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords candidate gene differential expression analysis gwas late-feathering subtypes shouguang chicken Authors Affiliations Xiayi Liu Beijing Capital Agribusiness Co Ltd Bio-Breeding Technology Center View all articles by this author junying li China Agricultural University College of Animal Science and Technology View all articles by this author Haigang Bao 0000-0003-4591-3957 [email protected] China Agricultural University College of Animal Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 201 views 139 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiayi Liu, junying li, Haigang Bao. Identifying candidate genes for late-feathering subtypes of the Shouguang Chicken by genome-wide association study and differential expression analysis. Authorea . 13 October 2025. DOI: https://doi.org/10.22541/au.176039289.96893980/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176039289.96893980/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feb418b0d341b23',t:'MTc3OTI3OTIxMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();