مطالعه ارتباط ژنومی برای شناسایی ژن‌های کاندیدا در مراحل اولیه رشد در مرغ

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم دامی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

2 گروه علوم دامی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

چکیده

تاکنون روش­های متعددی برای شناسایی فاکتورهای ژنتیکی مؤثر بر صفات پلی­ژنیک مورداستفاده قرارگرفته است. روش‌های رایج استفاده‌شده شامل آنالیز رگرسیونی مبتنی بر حداقل مربعات، آنالیز رگرسیونی مبتنی بر حداکثر درست نمایی و روش‌های بیزی می‌باشد. از برتری روش‌های بیزین نسبت به سایر روش‌ها می‌توان به امکان استفاده هم‌زمان از کل مارکرها در مدل اشاره نمود که منجر به جلوگیری از بیش برآورد اثرات نشانگرها شده و احتمال شناسایی SNPهای مثبت واقعی را افزایش می‌دهد. ازاین‌رو، در مطالعه حاضر برای شناسایی SNPهای مرتبط با وزن بدن در سنین اولیه (2 هفتگی)، در یک جمعیت F2 از مرغان آمیخته، از روش بیز Cpi استفاده شد. سپس، برای تعیین ژنوتیپ جمعیت حاضر شامل 312 مرغ F2، چیپ‌های تجاریIllumina 60K  استفاده گردید. درنهایت، 16 مارکر SNP که دارای فاکتور بیز بین 20 تا 150 بودند، به‌عنوان مارکرهای پیشنهادی برای وزن بدن در این سن در نظر گرفته شد. این SNPها بر روی 4 کروموزوم توزیع‌شده‌اند و به‌صورت سببی و یا به‌واسطه وجود عدم تعادل پیوستگی با 16 ژن ارتباط نزدیک دارند. از ژن­های شناسایی‌شده 12 ژن، کد کننده پروتئین و 4 ژن RNA غیرکدکننده می‌باشند. برای شناسایی ژن‌های مرتبط با هر SNP در مناطق کاندیدا، Mb 5/0 اطراف هر SNP معنی­دار در نظر گرفته شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Genomic association study to identify candidate genes for early growth traits in chickens

نویسندگان [English]

  • Zeinab Asgari 1
  • Alireza Ehsani 2
  • Ali Akbar Masoudi 2
  • Rasoul Vaez Torshizi 1
1 Department of Animal Sciences, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran.
2 Department of Animal Science, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran.
چکیده [English]

So far, several methods have been used to identify genetic factors affecting polygenic traits. Common methods are least squares regression analysis, maximum likelihood regression analysis, and Bayesian. The superiority of Bayesian methods over other methods is that it is possible to use all SNPs in the model simultaneously.  The simultaneous presence of markers can prevent overestimation of marker effects and increase the probability of identifying true positive SNPs. Therefore, in the present study, the BayesCpi method was used to identify SNPs related to body weight at early stages of growth (i.e. body weight at week 2) in an F2 population of mixed chickens. For this purpose, the Illumina 60K SNP bead chip was used to genotype the present population, including 312 chickens from the F2 population. According to the results of the analysis, 16 SNPs with a Bayes Factor (BF) between 20 and 150 were known and suggested as markers for body weight at early age. Results of post-GWAS showed that these SNPs were distributed across 4 chromosomes and were located close to, or inside the 16 genes. Among the identified genes, 12 genes were protein-encoding and 4 were noncoding RNAs. To identify genes associated with each SNP in candidate regions, 0.5 Mb around each significant SNP was considered.

کلیدواژه‌ها [English]

  • Genome-wide association study
  • Chicken
  • Single nucleotide polymorphism
  • Bayesian method
  • Candidate gene

Extended Abstract

Introduction

Many crucial traits in chickens are quantitative and influenced by numerous genes, hence making their improvement vital. In this regard, genome-wide association studies (GWAS) have successfully uncovered single nucleotide polymorphisms (SNPs) associated with these traits, aiming to elucidate regions contributing to their heritability. Utilizing GWAS findings facilitates the identification of candidate regions for genomic selection programs. Bayesian inference, a method employing all markers simultaneously, mitigates issues like false positives and overestimation of the effects of Quantitative Trait Loci (QTLs) and SNPs. Additionally, employing an F2 population enhances mapping accuracy. Hence, this study aims to identify genes influencing early-stage body weight in chickens using the Bayes CPI method within an F2 population of crossbred chickens

Background and objectives

In the present study, the Bayes Cpi method was utilized to identify SNPs associated with body weight during the early stages of growth (i.e., body weight at week 2) in an F2 population of mixed chickens. For this purpose, the Illumina 60K SNP bead chip was employed to genotype the population, comprising 312 chickens from the F2 generation

 

Materials and method

Initially, phenotypic data were collected from the offspring of the F2 generation. This F2 population was bred from crosses between a commercial broiler line (Arian (AA)) and Orumieh Iranian native fowl (NN). Arian chickens were selected for their body weight and meat quality traits, while native chickens were chosen for their natural resilience to diseases and pathogens. After 23 generations of strong selection, 450 F2 chickens were produced in 6 different hatches and utilized for testing. Each bird underwent weighing weekly after an 8-hour fasting period, individually assessed using a digital scale with a capacity of 50 kg and a measurement accuracy of 1 gram. Subsequently, genotyping was conducted on a random subset of 312 birds from the F2 generation using Illumina 60k SNP chips. Finally, the data underwent analysis using a mixed regression model and the GS3 software. The nearest genes to the proposed marker SNPs were identified using the NCBI website, considering 0.5 Mbp around each SNP

 

Result

Applying a significant threshold of Bayes factor ranging from 20 to 150, we identified 16 significant SNPs associated with body weights at 2 weeks of age, which were located near or within 16 genes distributed over 4 different chromosomes. These genes exhibit various metabolic functions contributing to the growth and weight gain of the body. They were categorized based on their rules: one group involved in Wnt signaling, another in the development of the nervous system, and others in different metabolic pathways, biosynthesis of various compounds, body metabolism, and substance transfer. The CACNA1C gene is implicated in two pathways. Additionally, the GALNTL6 gene plays a role in the protein glycosylation pathway, which facilitates protein modification.

 

Conclusion

This study identified 16 SNP markers associated with body weight at 2 weeks, shedding light on genes affecting early chicken growth. Chromosomes 1 and 4 emerged as key contributors to chicken genome variation at week 2 of age.

مراجع

Ahsan, M. (2014). Mapping and functional characterization of candidate genes and mutations for chicken growth. Ph.D. thesis, Swedish University of Agricultural Sciences Uppsala.
Asgari, Z., Ehsani, A., Masoudi, A.A, and Vaez Torshizi, R. (2021). Bayes factors revealed selection signature for time to market body weight in chicken: a genome-wide association study using BayesCpi methodology. Ital. J. Anim. Sci. 20, 1468-1478. https://doi.org/10.1242/jcs.02826.
Baumkötter, F., Schmidt, N., Vargas, C., Schilling, S., Weber, R., Wagner, K., Fiedler, S., Klug, W., Radzimanowski, J. and Nickolaus, S. (2014). Amyloid precursor protein dimerization and synaptogenic function depend on copper binding to the growth factor-like domain. J. Neurosci. 34, 11159-11172. https://doi.org/10.1523/JNEUROSCI.0180-14.2014.
Cadigan, K.M, and Liu, Y.I. (2006). Wnt signaling: complexity at the surface. J. Cell Sci. 119(3), 395-402. https://doi.org/10.1242/jcs.02826.
Cai, L., Fritz, D., Stefanovic, L. and Stefanovic, B. (2010). Nonmuscle myosin-dependent synthesis of type I collagen. J. Mol. Bio. 401, 564-578. https://doi.org/10.1016/j.jmb.2010.06.057.
Carlborg, Ö, Hocking, P.M., Burt, D.W, and Haley, C.S. (2004) Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth. Genet. Res. 83(3):197–209. https://doi.org/10.1017/s0016672304006779.
Carlborg, Ö., Kerje, S., Schütz, K., Jacobsson, L., Jensen, P. and Andersson, L. (2003). A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res, 13(3):413-421.
Cappai, R., Cheng, F., Ciccotosto, G.D., Needham, B.E., Masters, C.L., Multhaup, G., Fransson, L.Å, and Mani, K. (2005). The amyloid precursor protein (APP) of Alzheimer disease and its paralog, APLP2, modulate the Cu/Zn-Nitric Oxide-catalyzed degradation of glypican-1 heparan sulfate in vivo. JBC. 280, 13913-13920. https://doi.org/10.1074/jbc.M409179200.
Clevers, H. (2006). Wnt/β-catenin signaling in development and disease. Cell, 127(3), 469-480. https://doi.org/10.1016/j.cell.2006.10.018.
Cook, B.D., Dynek, J.N., Chang, W., Shostak, G. and Smith, S. (2002). Role for the related poly (ADP-Ribose) polymerases tankyrase 1 and 2 at human telomeres. MCB. 22, 332-342. https://doi.org/10.1128/MCB.22.1.332-342.2002.
Costa, R.B., Camargo, G.M., Diaz, I.D., Irano, N., Dias, M.M., Carvalheiro, R., Boligon, A.A., Baldi, F., Oliveira, H.N, and Tonhati, H. (2015). Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference. Genet. Sel. 47(1), 1-9. https://doi.org/10.1186/s12711-015-0146-0.
Emrani, H. (2016). Genome Wide Association Study(GWAS) for Growth traits and Ascites Syndrome in Iranian chicken. Ph.D. thesis, Tarbiat Modarres University Faculty of Agriculture.
Filali, M., Cheng, N., Abbott, D., Leontiev, V. and Engelhardt, J.F. (2002). Wnt-3A/β-catenin signaling induces transcription from the LEF-1 promoter. JBC. 277, 33398-33410. https://doi.org/10.1074/jbc.M107977200.
Fu, Y., Westenbroek, R.E., Scheuer, T. and Catterall, W.A. (2014). Basal and β-adrenergic regulation of the cardiac calcium channel CaV1. 2 requires phosphorylation of serine 1700. PNAS. 111. https://doi.org/16598-16603. 10.1073/pnas.1419129111.
Gaudet, P., Livstone, M.S., Lewis, S.E, and Thomas, P.D. (2011). Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium. Brief. Bioinform. 12, 449-462. https://doi.org/10.1093/bib/bbr042.
Goddard, M.E, and Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 10, 381–391. https://doi.org/10.1038/nrg2575.
He, X., Semenov, M., Tamai, K. and Zeng, X. (2004). LDL receptor-related proteins 5 and 6 in Wnt/β-catenin signaling: arrows point the way. J. Dev. 131(8), 1663-1677. https://doi.org/10.1242/dev.01117.
Kadir, R., Harel, T., Markus, B., Perez, Y., Bakhrat, A., Cohen, I., Volodarsky, M., Feintsein-Linial, M., Chervinski, E. and Zlotogora, J. (2016). ALFY-controlled DVL3 autophagy regulates Wnt signaling, determining human brain size. PLoS Genet. 12, e1005919. https://doi.org/10.1371/journal.pgen.1005919.
Kang, H.M., Sul, J.H., Service, S.K., Zaitlen, N.A., Kong, S.Y., Freimer, N.B., Sabatti, C. and Eskin, E. (2010). Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 42(4):348–354.
Kass, R.E, and Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773-795.
Kim, K., Jung, J., Caetano-Anolles, K., Sung, S., Yoo DAhn, Choi, B.H., Kim, H-C., Jeong, J-Y., Cho, Y-M. and Park, E-W. (2018). Artificial selection increased body weight but induced increase of runs of homozygosity in Hanwoo cattle. PLoS One. 13(3):e0193701.
Kerje, S., Carlborg, Ö., Jacobsson, L., Schütz, K., Hartmann, C., Jensen, P. and Andersson, L. (2003). The twofold difference in adult size between the red junglefowl and White Leghorn chickens is largely explained by a limited number of QTLs. Anim. Genet. 34(4), 264-274. https://doi.org/10.1046/j.1365-2052.2003.01000.x.
Kitamoto, Y., Yuan, X., Wu, Q., McCourt, D.W, and Sadler, J.E. (1994). Enterokinase, the initiator of intestinal digestion, is a mosaic protease composed of a distinctive assortment of domains. PNAS. 91, 7588-7592. https://doi.org/10.1073/pnas.91.16.7588.
Ledur, M., Navarro, N. and Pérez-Enciso, M. (2010). Large-scale SNP genotyping in crosses between outbred lines: how useful is it and quest. Hered. 105(2), 173-182. https://doi.org/10.1038/hdy.2009.149.
Li, X., Liu, P., Liu, W., Maye, P., Zhang, J., Zhang, Y., Hurley, M., Guo, C., Boskey, A. and Sun, L. (2005). Dkk2 has a role in terminal osteoblast differentiation and mineralized matrix formation. Nat. Genet. 37(9), 945-952. https:// doi.org/10.1038/ng1614.
Lin, R.-Y., Vera, J.C., Chaganti, R.S, and Golde, D.W. (1998). Human monocarboxylate transporter 2 (MCT2) is a high affinity pyruvate transporter. JBC. 273, 28959-28965. https://doi.org/10.1074/jbc.273.44.28959.
Ma, Y., Gu, L., Yang, L., Sun, C., Xie, S., Fang, C., Gong, Y. and Li S. (2018). Identifying artificial selection signals in the chicken genome. Plos one 13, e0196215. https://doi.org/10.1371/journal.pone.0196215.
Madsen, P. and Jensen, J. (2002). A package for analysing multivariate mixed models. Tjele (Denmark): Danish Institute of Agricultural Sciences (DIAS).
Maher, B. (2008). Personal genomes: The case of the missing heritability. Nature, 456(7218), 18-21.
Nystoriak, M.A., Nieves-Cintrón, M., Patriarchi, T., Buonarati, O.R., Prada, M.P., Morotti, S., Grandi, E., Fernandes, J.D.S., Forbush, K. and Hofmann, F. (2017). Ser1928 phosphorylation by PKA stimulates the L-type Ca2+ channel CaV1. 2 and vasoconstriction during acute hyperglycemia and diabetes. Sci. Signal. 10(463), 9647. https://doi.org/10.1126/scisignal.aaf9647.
Peters, S., Kizilkaya, K., Garrick, D., Fernando, R., Reecy, J., Weaber, R., Silver, G. and Thomas, M. (2012). Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers. J. Anim. Sci. 90(10), 3398-3409. https://doi.org/10.2527/jas.2012-4507.
Rorsman, P. (2005). Insulin secretion: function and therapy of pancreatic beta-cells in diabetes. BJDVD. 5, 187-191. https://doi.org/10.1177/14746514050050040201.
Sewalem, A., Morrice, D.M., Law, A., Windsor, D., Haley, C.S., Ikeobi, C.O, and Hocking, P.M. (2002). Mapping of quantitative trait loci for body weight at three, six, and nine weeks of age in a broiler layer cross. Poult. Sci. 81(12), 1775-1781.‏ https://doi.org/10.1093/ps/81.12.1775.
Splawski, I., Timothy, K.W., Sharpe, L.M., Decher, N., Kumar, P., Bloise, R., Napolitano, C., Schwartz, P.J., Joseph, R.M, and Condouris, K. (2004). CaV1. 2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 119, 19-31. https://doi.org/10.1016/j.cell.2004.09.011.
Su, Z.-J., Hahn, C.N., Goodall, G.J., Reck, N.M., Leske, A.F., Davy, A., Kremmidiotis, G., Vadas, M.A, and Gamble, J.R. (2004). A vascular cell-restricted RhoGAP, p73RhoGAP, is a key regulator of angiogenesis. PNAS. 101. https://doi.org/12212-12217. 10.1073/pnas.0404631101.
Team, R.C., 2018. R: A Language and environment for Statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.htm/.
Uaesoontrachoon, K., Yoo, H.-J., Tudor, E.M., Pike, R.N., Mackie, E.J, and Pagel, C.N. (2008). Osteopontin and skeletal muscle myoblasts: association with muscle regeneration and regulation of myoblast function in vitro. Int. J. Biochem. Cell Biol. 40(10), 2303-2314. https://doi.org/10.1016/j.biocel.2008.03.020.
Van Kaam, J., Van Arendonk, J., Groenen, M., Bovenhuis, H., Vereijken, A., Crooijmans, R., Van der Poel, J. and Veenendaal, A. (1998). Whole genome scan for quantitative trait loci affecting body weight in chickens using a three generation design. Livest. Prod. Sci. 54(2), 133-150. https://doi.org/10.1186/1297-9686-33-2-133.
Saravanan, K., Panigrahi, M., Kumar, H., Bhushan, B., Dutt, T. and Mishra, B. (2020), Selection signatures in livestock genome: A review of concepts, approaches and applications. Livest. Sci.241, 104257.
Varona Aguado, L., García Cortés, L.A, and Perez Enciso, M. (2001). Bayes factors for detection of quantitative trait loci. Genet. Sel.  33, 133-152. https://doi.org/10.1186/1297-9686-33-2-133.     
Walugembe, M., Bertolini, F., Dematawewa, C.M.B., Reis, M.P., Elbeltagy, A.R., Schmidt, C.J., Lamont, S.J. and Rothschild, M.F. (2018). Detection of selection signatures Among Brazilian, Sri Lankan, and Egyptian chicken populations under different environmental conditions. Front Genet. 9:737.
Wang, S., Hu, X., Wang, Z., Li, X., Wang, Q., Wang, Y., Tang, Z. and Li, H. (2012). Quantitative trait loci associated with body weight and abdominal fat traits on chicken chromosomes 3, 5 and 7. GMR. 11(11), 956-965. https://doi.org/10.4238/2012.April.19.1.
Weng, Z., Xu, Y., Li, W., Chen, J., Zhong, M., Zhong, F., Du, B., Zhang, B. and Huang, X. (2020). Genomic variations and signatures of selection in Wuhua yellow chicken. PLoS One. 15(10): e0241137.
Williams, J., Xu, S. and Ferreira, M.A. (2023). BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies. BMC bioinformatics. 24, 194.
Wolc, A. and Dekkers, J.C. (2022). Application of Bayesian genomic prediction methods to genome-wide association analyses. Genet. Sel. 54, 31.
Xie, L., Luo, C., Zhang, C., Zhang, R., Tang, J., Nie, Q., Ma, L., Hu, X., Li, N., Da, Y., and Zhang, X. (2012). Genome-wide association study identified a narrow chromosome 1 region associated with chicken growth traits. PLoS One. 7(2):e30910.
Xu, Y.p., Liang, L. and Wang, X.M. (2011). The levels of Pdx1/insulin, Cacna1c and Cacna1d, and β-cell mass in a rat model of intrauterine undernutrition. J. Matern.-Fetal Neonatal Med. 24, 437-443. https://doi.org/10.3109/14767058.2010.497571.
Yamashina, I. (1956). The action of enterokinase on trypsinogen. BBA. 20, 433-434. https://doi.org/10.1016/0006-3002(56)90329-8.
Zhang, Y., Liu, S., Mickanin, C., Feng, Y., Charlat, O., Michaud, G.A., Schirle, M., Shi, X., Hild, M. and Bauer, A. (2011). RNF146 is a poly (ADP-ribose)-directed E3 ligase that regulates axin degradation and Wnt signalling. Nat. Cell Biol. 13, 623-629. https://doi.org/10.1038/ncb2222.
علوم دامی ایران
دوره 55، شماره 4
دی 1403
صفحه 651-664

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  • تاریخ دریافت: 14 اسفند 1402
  • تاریخ بازنگری: 30 فروردین 1403
  • تاریخ پذیرش: 01 اردیبهشت 1403

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