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ارزیابی عملکرد ماشین بردار پشتیبان با کرنل های مختلف در تجزیه ژنومی در سطوح مختلف واریانس غالبیت | ||
تحقیقات تولیدات دامی | ||
دوره 14، شماره 1، اردیبهشت 1404، صفحه 1-17 اصل مقاله (1.37 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22124/ar.2025.29391.1872 | ||
نویسندگان | ||
حمید صاحب علم؛ محسن قلی زاده* ؛ حسن حافظیان | ||
گروه علوم دامی، دانشکده علوم دامی و شیلات، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
چکیده | ||
هدف از پژوهش حاضر، بررسی و مقایسه صحت پیشبینی ژنومی روش ماشین بردار پشتیبان (SVM) بر اساس توابع کرنل مختلف شامل خطی (SVM-lin)، شعاعی (SVM-rad)، چندجملهای (SVM-pol) و حلقوی (SVM-sig)، و روش GBLUP در مدلهای کنش ژنی صرفاً افزایشی و افزایشی-انحراف غالبیت با در نظر گرفتن سطوح مختلف واریانس غالبیت بود. بدین منظور، ژنومی حاوی شش کروموزوم و بهطول 600 سانتیمورگان شبیهسازی شد. روی هر کروموزوم، 1000 نشانگر چندشکلی تک نوکلئوتیدی (SNP) با فواصل یکسان و 100 جایگاه صفت کمّی (QTL) بهطور تصادفی در نظر گرفته شد. واریانس فنوتیپی و وراثت پذیری بهترتیب برابر با 1 و 4/0 در نظر گرفته شد. واریانس انحراف غالبیت برابر با 10/0، 15/0، 20/0، 25/0، 30/0 و 35/0 در نظر گرفته شد. صحت پیشبینی بهعنوان ضریب همبستگی پیرسون بین ارزش ژنتیکی واقعی (TGV) یا ارزش اصلاحی واقعی(TBV) و ارزش ژنتیکی ژنومی (GEGV) یا ارزش اصلاحی ژنومی (GEBV) تعریف شد. روش مرسوم GBLUP در تمام سناریوهای مختلف واریانس غالبیت، صحت پیشبینی GEBV و GEGV بالاتری را نشان داد. در بین رویکردهای مختلف SVM، در مدل صرفاً افزایشی و افزایشی- انحراف غالبیت بر اساس صحت پیشبینی GEGV، رویکردهای SVM-rad و SVM-sig بهترتیب بالاترین عملکرد را نشان دادند. بر اساس صحت پیشبینی GEBV، با افزایش واریانس غالبیت، این برتری بهشدت کاهش یافت، بهطوری که در واریانس غالبیت بیشتر از 30/0، رویکردهای SVM-lin و SVM-sig بهترتیب صحت پیشبینی GEBV اندکی بالاتر و برابر با SVM-rad نشان دادند. بهطورکلی، در برازش فنوتیپ روی نشانگرها با روش ناپارامتری SVM، استفاده از تابع کرنل شعاعی در مدل پیشنهاد میشود. | ||
کلیدواژهها | ||
ارزش اصلاحی ژنومی؛ تابع کرنل؛ تجزیه ژنومی؛ صحت پیش بینی؛ ماشین بردار پشتیبان | ||
مراجع | ||
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