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شناسایی ژنهای موثر بر صفات رشد در جوجه های گوشتی با استفاده از روشهای رگرسیون خطی و یادگیری ماشین | ||
| علوم دامی ایران | ||
| دوره 55، شماره 3، مهر 1403، صفحه 583-602 اصل مقاله (1.76 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijas.2023.363825.653963 | ||
| نویسندگان | ||
| حسین بانی سعادت1؛ رسول واعظ ترشیزی* 2؛ علی اکبر مسعودی3؛ علیرضا احسانی3؛ صالح شاهین فر4 | ||
| 1گروه علوم دامی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2گروه علوم دامی دانشکده کشاورزی دانشگاه تربیت مدرس تهران ایران | ||
| 3گروه علوم دامی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران. | ||
| 4تحقیقات کشاورزی ویکتوریا، مرکز آگریبایوساینس، بوندورا، ویکتوریا ۳۰۸۳، استرالیا. | ||
| چکیده | ||
| آگاهی از ارتباط چندشکلیهای تکنوکلئوتیدی با صفات مهم اقتصادی یکی از ابزارهای مهم برنامههای اصلاح نژاد در صنعت طیور است. مطالعات پویش ژنومی برای کشف چندشکلیهای تکنوکلئوتیدی (نشانگرها) مرتبط با این صفات، اغلب با استفاده از مدلهای خطی ساده صورت میگیرد که به دلیل وجود برخی از فرضیات این مدلها، ممکن است بعضی از نشانگرها شناسایی نشوند. این مطالعه با هدف ارزیابی کارآیی روشهای جنگل تصادفی و گرادیان بوستینگ و ارزیابی عملکرد آنها در مقابل مدل خطی برای شناسایی نشانگرهای همبسته با صفات وزن بدن در سنین 6 و 9 هفتگی در جوجههای گوشتی نسل دوم حاصل از تلاقیهای دوطرفه لاین تجاری آرین با پرندههای بومی ارومیه انجام شد. نتایج نشان داد که دو روش یادگیری ماشین توانستند نشانگرهای مهمی از جمله GGaluGA308573، GGaluGA255033، Gga_rs13614212، Gga_rs13743072، GGaluGA258772، Gga_rs14034395 و Gga_rs13858398 را برای صفات وزن بدن شناسایی کنند که به ترتیب با ژنهای MAP2، ACSL1، CAMSAP2، FAM117B، SLC4A4، TIMP4 و LncRNA در ارتباط بودند. تقسیم سلولی، کنترل رشد، تنظیم ساختار اسکلت سلولی و میکروتوبول، و فعالیت رونویسی مهمترین فرآیند بیولوژیکی این ژنها میباشند. مطالعه ژنهای جدید شناسایی شده توسط روشهای یادگیری ماشین، که مدل خطی قادر به شناسایی آنها در جمعیت مورد مطالعه نبودند، می‌تواند بینش جدیدی را برای کنترل ژنتیکی صفات رشد در جوجههای گوشتی باز کند. علاوه بر این، نشانگرهای با اهمیت کشف شده، قابلیت استفاده در برنامههای اصلاح ژنتیکی جوجههای گوشتی را دارند. | ||
| کلیدواژه‌ها | ||
| چندشکلیهای تکنوکلئوتیدی؛ مطالعات پویش ژنومی؛ جوجه های گوشتی؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Identification of genes affecting growth traits in broiler chickens using linear regression and machine learning methods | ||
| نویسندگان [English] | ||
| Hossein Bani Saadat1؛ Rasoul Vaez Torshizi2؛ Ali Akbar Masoudi3؛ Alireza Ehsani3؛ Saleh Shahinfar4 | ||
| 1Department of Animal Science, Faculty of Agriculture,, Tarbiat Modares University, Tehran, Iran. | ||
| 2Department of Animal Science, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran | ||
| 3Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. | ||
| 4Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia. | ||
| چکیده [English] | ||
| Knowledge about the association between single nucleotide polymorphisms (SNPs) and important economic traits is one of the crucial tools in breeding programs within the poultry industry. Genome-wide studies for discovering SNP variations related to these traits are often conducted using simple linear models. However, due to certain assumptions of these models, some SNP markers may not be identified. This study aimed to evaluate the performance of random forest and gradient boosting methods compared to linear models in identifying SNP markers associated with body weight traits at 6 and 9 weeks of age in F2 broiler chickens resulting from crosses between the commercial Arian line and native Urmia birds. The results showed that the machine learning approaches were able to identify important markers, such as GGaluGA308573, GGaluGA255033, Gga_rs13614212, Gga_rs13743072, GGaluGA258772, Gga_rs14034395, and Gga_rs13858398, associated with body weight traits, which were related to genes MAP2, ACSL1, CAMSAP2, FAM117B, SLC4A4, TIMP4, and LncRNA, respectively. These genes are primarily involved in cellular division, growth control, regulation of cellular skeleton structure and microtubules, and transcription activity, constituting the most important biological processes. The identification of these novel genes using machine learning methods, which were not detected by linear models and previous studies in this population, could provide new insights into genetic control of growth traits in broiler chickens. Moreover, the discovered significant markers can be utilized in genetic improvement programs for broiler chickens. | ||
| کلیدواژه‌ها [English] | ||
| nucleotide polymorphisms, Genome-wide association studies, broiler chickens, machine learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 422 تعداد دریافت فایل اصل مقاله: 320 |
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