پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,762 |
| تعداد مقالات | 72,837 |
| تعداد مشاهده مقاله | 131,854,793 |
| تعداد دریافت فایل اصل مقاله | 103,533,009 |
استفاده از روش آماری بیز در شناسایی عوامل ژنتیکی موثر بر وزن بدن در سنین نهایی رشد در یک جمعیت از جوجه های گوشتی آمیخته | ||
| علوم دامی ایران | ||
| دوره 56، شماره 2، تیر 1404، صفحه 301-315 اصل مقاله (1.84 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijas.2024.379554.654021 | ||
| نویسندگان | ||
| زینب عسگری* 1؛ علیرضا احسانی1؛ علی اکبر مسعودی1؛ رسول واعظ ترشیزی2 | ||
| 1گروه علوم دامی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2گروه علوم دامی دانشکده کشاورزی دانشگاه تربیت مدرس تهران ایران | ||
| چکیده | ||
| صفت وزن بدن به‌عنوان یک صفت پلی‌ژنیک در اصلاح نژاد دام اثر بالایی بر سودآوری صنعت پرورش مرغ دارد. ازاین‌رو، شناسایی جایگاه‌های ژنتیکی مرتبط با این صفت حائز اهمیت است. در مطالعات پویش ژنوم معمولی، تجزیه‌وتحلیل‌ها بر اساس رگرسیون تک نوکلئوتیدی بر حسب فنوتیپهای مشاهده‌شده می‌باشد. در این روش‌ها، فرض بر این است که کل متغیرهای ژنتیکی، از یک توزیع آماری نرمال پیروی می‌کنند که با یافته‌های جدید در مورد نقش بیشتر برخی جایگاه‌های ژنومی و نقش کمتر برخی دیگر، همخوانی ندارد. برخلاف این روش‌ها، در روش‌های بیزی، امکان تعریف بیش از یک توزیع آماری برای اثرات متغیرها وجود دارد. ازاین‌رو، مطالعه‌ی حاضر باهدف شناسایی نشانگرهای تک مارکری مؤثر بر صفت وزن بدن مرغ در سنین پایانی رشد (9، 10، 11 و 12 هفتگی) و با استفاده از اطلاعات نسل دوم جوجه‌های آمیخته ایجاد شده از تلاقی دوطرفه بین لاین آرین و مرغ‌های بومی استان آذربایجان با استفاده از روش بیز Cpi انجام شد. در نهایت، 10 نشانگر معنی‌دار برای وزن بدن در سنین مختلف شناسایی شدند. این SNPها به‌صورت سببی و یا به‌واسطه وجود عدم تعادل پیوستگی با هشت ژن که بر روی شش کروموزوم توزیع‌شده‌اند، به‌طور معنی‌داری بر صفات مورد مطالعه اثرگذارند. از ژن‌های شناسایی شده، هفت ژن، کد کننده پروتئین و یک ژن ncRNA می‌باشند. برای شناسایی ژن‌های مرتبط با هر SNP در مناطق کاندیدا، Mb 0/5 اطراف هر SNP معنی‌دار در نظر گرفته شد. نتایج مطالعه حاضر، در انتخاب ژنومی و انتخاب به کمک نشانگر یا ژن برای بهبود سرعت رشد در مرغ موثر می‌باشند | ||
| کلیدواژه‌ها | ||
| روش بیزین؛ وزن بدن؛ ژن کاندیدا؛ مطالعه پویش کل ژنوم؛ چندشکلی تک نوکلئوتیدی | ||
| عنوان مقاله [English] | ||
| Using Bayes statistical method in identifying genetic factors affecting body weight at the final ages of growth in a population of mixed broiler chickens | ||
| نویسندگان [English] | ||
| Zeinab Asgari1؛ Alireza Ehsani1؛ Ali Akbar Masoudi1؛ Rasoul Vaez Torshizi2 | ||
| 1Department of Animal Sciences, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran | ||
| 2Department of Animal Science, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Body weight trait as a polygenic trait in animal breeding has a high impact on the profitability of poultry industry. For this reason, identifying the genetic loci associated with this trait is important. In typical GWAS, the analyses are based on the regression of single nucleotides on the observed phenotypes. In these methods, it is assumed that all genetic variables follow a normal statistical distribution which this is inconsistent with new findings about the role of some genomic loci. In contrast to these methods, in the Bayesian method it is possible to define more than one statistical distribution for the effects of variables. Therefore, the present study was performed to identify causal single nucleotide polymorphisms (SNPs) associated with body weight in 9, 10, 11 and 12 weeks of age, in an F2 crossbred chicken population between Arian line and native chickens of Azerbaijan province using BayesCpi methodology. Finally, 10 significant markers for body weight at different ages were identified. These SNPs are close to or within 8 genes and are distributed on 6 chromosomes. Of the above genes, 7 genes encode proteins and 1 ncRNA gene. To identify genes associated with each SNP in candidate regions, 0.5 Mb around each SNP was considered significant. Results can be used in genomic selection and marker or gene assisted selection to improve growth rate in chicken. | ||
| کلیدواژه‌ها [English] | ||
| Bayesian method, Body weight, Candidate gene, Genome-wide association study, Single nucleotide polymorphism | ||
| مراجع | ||
منابععسگری، زینب؛ احسانی، علیرضا؛ مسعودی، علی‌اکبر؛ واعظ ترشیزی، رسول (1403). مطالعه ارتباط ژنومی برای شناسایی ژن های کاندیدا در مراحل اولیه رشد در مرغ. علوم دامی ایران، doi: 10.22059/ijas.2024.372924.654003.
RERERENCES Asgari, Z., Ehsani, A., Masoudi, AA. 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. Italian Journal of Animal Science, 20:1468-1478. https://doi.org/10.1080/1828051X.2021.1965920. In Persian. Asgari, Z., Ehsani, A., Masoudi, AA. and Vaez Torshizi. R. (2024). Genomic association study identifying candidate genes for early growth traits in chickens. Iranian Journal of animal Science, 10.22059/ijas.2024.372924.654003. Cadigan, K. M. and Liu, Y. I. (2006). Wnt signaling: complexity at the surface. Journal of Cell Science, 119(3):395-402. https://doi.org/10.1242/jcs.02826. Cenciarelli, C., Chiaur, D., Guardavaccaro, D., Parks, W., Vidal, M. and Pagano, M. (1999). Identification of a family of human F-box proteins. Current Biology, 9(20):1177-S1173. https://doi.org/10.1016/S0960-9822(00)80020-2. 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. Genetics Selection Evolution, 47(1):1-9. https://10.1186/s12711-015-0146-0. Emmerson, D. (1997). Commercial approaches to genetic selection for growth and feed conversion in domestic poultry. Poultry Science, 76(8):1121-1125. https://10.1093/ps/76.8.1121. 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. Engelhardt, S. and Rochais, F. (2007). G proteins: more than transducers of receptor-generated signals?, pp. 1109-11, Am Heart Assoc. Fritz, S.S. and Boichard, D. (2013). QTL fine mapping with Bayesian C (π): a simulation study. Genetics Selection Evolution 45, Non paginé. Gilman, A.G. (1987). G proteins: transducers of receptor-generated signals. Annual review of biochemistry, 56(1):615-649. https://10.1161/01.RES.0000266971.15127.e8. Goddard, M., Kemper, K., MacLeod, I., Chamberlain, A. and Hayes, B. (2016). Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture. Proceedings of the Royal Society B: Biological Sciences 283, 20160569. 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://10.1038/nrg2575. Gutierrez, M.A., Dwyer, B.E. and Franco, S.J. (2019). Csmd2 Is a Synaptic Transmembrane Protein that Interacts with PSD-95 and Is Required for Neuronal Maturation. Eneuro, 6(2). https://10.1523/ENEURO.0434-18.2019. Howard, D.M., Hall, L.S., Hafferty, J.D., Zeng, Y., Adams, M.J., Clarke, T.-K., Porteous, D.J., Nagy, R., Hayward, C. and Smith, B.H. (2017). Genome-wide haplotype-based association analysis of major depressive disorder in Generation Scotland and UK Biobank. Translational psychiatry 7, 1263. Kass, R.E. and Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430):773-795. Khabiri, A., Toroghi, R., Mohammadabadi, M. and Tabatabaeizadeh, S. (2023). Introduction of a Newcastle disease virus challenge strain (sub-genotype VII. 1.1) isolated in Iran. Veterinary Research Forum, 14 (4), 221-228. Kim K., Jung J., Caetano-Anollés K., Sung S., Yoo D., 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, e0193701. Lau, W.L. and Scholnick, S.B. (2003). Identification of two new members of the CSMD gene family. Genomics, 82(3):412-415. https://10.1016/s0888-7543(03)00149-6. Ledur, M., Navarro, N. and Pérez-Enciso, M. (2010). Large-scale SNP genotyping in crosses between outbred lines: how useful is it and quest. Heredity, 105(2):173-182. https://10.1038/hdy.2009.149. Mindnich, R., Drury, J.E. and Penning, T.M. (2011). The effect of disease associated point mutations on 5β-reductase (AKR1D1) enzyme function. Chemico-Biological Interactions, https://191(1):250-254. 10.1016/j.cbi.2010.12.020. Mohammadifar, A. and Mohammadabadi, M. (2018). Melanocortin-3 receptor (mc3r) gene association with growth and egg production traits in Fars indigenous chicken. Malaysian Applied Biology, 47: 85-90. Mohammadifar, A., Faghih Imani, S.A., Mohammadabadi, M.R. and Soflaei, M. (2014). The effect of TGFb3 gene on phenotypic and breeding values of body weight traits in Fars native fowls. Agricultural biotechnology Journal, 5, 125-136. Mohammadifar, A. and Mohammadabadi, M. (2017). The effect of uncoupling protein polymorphisms on growth, breeding value of growth and reproductive traits in the fars indigenous chicken. Iranian Journal of Applied Animal Science, 7, 679-85. Mohammadabadi, M., Nikbakhti, M., Mirzaee, H., Shandi, A., Saghi, D., Romanov, M.N. and Moiseyeva, I.G. (2010). Genetic variability in three native Iranian chicken populations of the Khorasan province based on microsatellite markers. Russian journal of genetics, 46, 505-09. Nadaf, J., Pitel, F., Gilbert, H., Duclos, M.J, Vignoles, F., Beaumont, C., Vignal, A., Porter, T.E., Cogburn, L.A. and Aggrey, S.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):241-249. https://10.1152/physiolgenomics.90384.2008. Pan R., Qi L., Xu Z., Zhang D., Nie Q., Zhang, X. and Luo, W. (2024). Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population. Poultry Science 103, 103341. 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. Journal of Animal Science, 90(10):3398-3409. https://10.2527/jas.2012-4507. Rao, Y., Shen, X., Xia, M., Luo, C., Nie, Q., Zhang, D. and Zhang, X. (2007). SNP mapping of QTL affecting growth and fatness on chicken GGA1. Genetics Selection Evolution, 39(5):1. https://10.1186/1297-9686-39-5-569. Rico-Bautista, E., Flores-Morales, A. and Fernández-Pérez, L. (2006). Suppressor of cytokine signaling (SOCS) 2, a protein with multiple functions. Cytokine and Growth Factor Reviews, 17(6):431-439. https://10.1016/j.cytogfr.2006.09.008. Risch, N. and Merikangas, K. (1996). The future of genetic studies of complex human diseases. Science, 273:1516-1517. https://10.1126/science.273.5281.1516. Rozenblum, E., Vahteristo, P., Sandberg, T., Bergthorsson, J., Syrjakoski, K., Weaver, D., Haraldsson, K., Johannsdottir, H., Vehmanen, P. and Nigam, S. (2002). A genomic map of a 6-Mb region at 13q21-q22 implicated in cancer development: identification and characterization of candidate genes. Human genetics, 110(2):111-121. https://10.1007/s00439-001-0646-6. Saadatabadi, L. M., Mohammadabadi, M., Nanaei, H. A., Ghanatsaman, Z. A., Stavetska, R. V., Kalashnyk, O., Kochuk-Yashchenko, O. A. and Kucher, D. M. (2023). Unraveling candidate genes related to heat tolerance and immune response traits in some native sheep using whole genome sequencing data. Small Ruminant Research. 225: 107018. Seery, L., Nestor, P. and FitzGerald, G. (1998). Molecular evolution of the aldo-keto reductase gene superfamily. Journal of Molecular Evolution, 46(2):139-146. https://10.1007/pl00006288. Semenova, E., Wang, X., Jablonski, M.M., Levorse, J. and Tilghman, S.M. (2003). An engineered 800 kilobase deletion of Uchl3 and Lmo7 on mouse chromosome 14 causes defects in viability, postnatal growth and degeneration of muscle and retina. Human molecular genetics, 12(11):1301-1312. https://10.1093/hmg/ddg140. Shahdadnejad, N., Mohammadabadi, M. and Shamsadini, M. (2016), Typing of clostridium perfringens isolated from broiler chickens using multiplex PCR. Genetics in the 3rd millennium, 14, 4368-74. Stern, M.E.R. (2015). The Impact of the Ubiquitin-Proteasome Pathway and CRM1-Independent Exportins on Thyroid Hormone Receptor Localization and the Expression of T3-Responsive Genes. B.Sc. Dissertation. College of William and Mary. Surmann-Schmitt, C., Widmann, N., Dietz, U., Saeger, B., Eitzinger, N., Nakamura, Y., Rattel, M., Latham, R., Hartmann, C. and von der Mark, H. (2009). Wif-1 is expressed at cartilage-mesenchyme interfaces and impedes Wnt3a-mediated inhibition of chondrogenesis. Journal of Cell Science, 122(20):3627-3637. https://10.1242/jcs.048926. Van den Berg, I., Fritz, S. and Boichard, D. (2013). QTL fine mapping with Bayes C (π): a simulation study. Genetics Selection Evolution, 45(1):1. https://10.1186/1297-9686-45-19. Varona Aguado, L., García Cortés, L.A. and Perez Enciso, M. (2001). Bayes factors for detection of quantitative trait loci. Genetics Selection Evolution, 33;133-152. https://10.1186/1297-9686-33-2-133. 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. Genetics and molecular research, 11(11):956-965. Xie, L., Luo, C., Zhang, C., Zhang, R., Tang, J., Nie, Q., Ma, L., Hu, X., Li, N. and Da, Y. (2012). Genome-wide association study identified a narrow chromosome 1 region associated with chicken growth traits. PLoS One, 7(2):e30910. https://1371/journal.pone.0030910. | ||
|
آمار تعداد مشاهده مقاله: 149 تعداد دریافت فایل اصل مقاله: 47 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب