Comparing Different Marker Densities and Various Reference Populations Using Pedigree-Marker Best Linear Unbiased Prediction (BLUP) Model
محورهای موضوعی : Camelش. برجسته 1 , غ.ر. داشاب 2 , م. رکوعی 3 , م.م. شریعتی 4 , م. وفای واله 5
1 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
2 - دانشگاه زابل
3 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
4 - Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
5 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
کلید واژه: Simulation, Accuracy, genomic selection, marker density,
چکیده مقاله :
In order to have successful application of genomic selection, reference population and marker density should be chosen properly. This study purpose was to investigate the accuracy of genomic estimated breeding values in terms of low (5K), intermediate (50K) and high (777K) densities in the simulated populations, when different scenarios were applied about the reference populations selecting. After simulating the historical (undergoing drift and mutation) and recent (undergoing selection) population structures, 800 individuals were remained in reference population. Three scenarios were considered for reducing the reference population number including: 1) 400 individuals which had the highest relationships with the validation set, 2) 400 individuals which had the highest inbreeding, and 3) 400 selected individuals by random. The genomic breeding values were predicted for traits with two heritability levels (0.25 and 0.5) using best linear unbiased prediction (BLUP) with different markers and pedigree information combinations of included pedigree-based BLUP (ABLUP), which was used a numerator relationships matrix (A) only, genomic best linear unbiased prediction (GBLUP) which was used a genomic relationship matrix (G) only, and BLUP|GA, which combined both A and G by using a weight parameter (l). By increasing l, the prediction model was changed from GBLUP (l=0) to ABLUP (l=1). The results indicated that without considering the panel density effects, G matrix (l=0) and A matrix (l=1) usages had the highest and lowest prediction accuracy, respectively. Comparative analyses of different scenarios of reference population selection revealed that all individuals’ inclusion in reference population yielded the highest estimation accuracy for breeding values (p <0.05). On the contrary, using reduced single nucleotide polymorphism (SNP) panels considerably decreased the accuracy of breeding value prediction. Individuals selecting in the reference set with a high genetic relationship to target animals, considerably improved the reduction in genomic prediction accuracy because of small reference population size.
برای استفاده موفقیت آمیز از انتخاب ژنومی، تراکم مارکری و ساختار جمعیت مرجع بسیار تأثیرگذار خواهد بود. تحقیق حاضر به منظور مقایسه صحت پیش بینی ارزش های اصلاحی ژنومیکی در تراکم های مختلف نشانگری شامل پائین (K5)، متوسط (K50) و بالا (K777) در جمعیت شبیه سازی شده براساس سناریوهای مختلف انتخاب جمعیت مرجع بود. بعد از شبیه سازی ساختار جمعیت پایه (تحت تأثیر جهش و رانش) و جمعیت اخیر (تحت تأثیر انتخاب)، تعداد 800 حیوان در جمعیت مرجع قرار گرفتند. سه سناریو جهت کاهش تعداد جمعیت مرجع مورد استفاده قرار گرفت که شامل: 1) 400 حیوان با بیشترین رابطه خویشاوندی با جمعیت هدف، 2) 400 حیوان با بیشتر میزان همخونی، 3) 400 حیوان انتخاب شده به صورت تصادفی. ارزش اصلاحی ژنومی افراد برای صفات با دو سطح وراثتپذیری (0.5 و 0.25) با استفاده از بهترین پیش بینی نااریب خطی (BLUP) با ترکیب های مختلفی از اطلاعات شجرهای و ژنومی شامل، ABLUP که تنها از ماتریس روابط شجرهای (A) استفاده نموده، GBLUP که تنها از ماتریس روابط ژنومی (G) استفاده نموده، و BLUP|GA که از ترکیب هر دو ماتریس A و G براساس یک پارامتر وزنی (l) استفاده می نماید، برآورد گردید. با افزایش l مدل پیش بینی از GBLUP (که l برابر صفر است) به ABLUP (که l برابر یک می باشد) تغییر می نماید. نتایج نشان داده که بدون توجه به اثرات تراکم پنل، استفاده از ماتریس G (l=0) و ماتریس A (l=1) به ترتیب بیشترین و کمترین مقادیر صحت پیش بینی را داشتند. با مقایسه سناریوهای مختلف انتخاب جمعیت مرجع نشان داده شد که استفاده از همه افراد در جمعیت مرجع منجر به بیشترین برآورد صحت ارزش های اصلاحی گردید (P<0.05). در مقابل استفاده از پنل های با تراکم کمتر به طور قابل ملاحظه ای صحت پیش بینی ارزش های اصلاحی را کاهش دادند. انتخاب افراد با روابط خویشاوندی ژنتیکی بالاتر جمعیت مرجع با هدف به طور معنی داری کاهش صحت پیش بینی ژنومی به علت اندازه جمعیت مرجع کوچک را بهبود بخشید.
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