برآورد صحت ارزش های اصلاحی صفات پیچیده در جمعیت‌های بدون شجره با استفاده از نشانگرهای متراکم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد ژنتیک و اصلاح نژاد دام، گروه علوم دامی، دانشکده کشاورزی، دانشگاه شهرکرد

2 استادیار ژنتیک و اصلاح نژاد دام، گروه علوم دامی، دانشکده کشاورزی، دانشگاه شهرکرد

3 استادیار ژنتیک، گروه ژنتیک، دانشکده علوم، دانشگاه شهرکرد

چکیده

هدف از این پژوهش برآورد صحت ارزش­های اصلاحی ژنگانی با استفاده از مدلGBLUP (Genomic Best Linear Unbiased Prediction) و مقایسۀ آن با روش سنتی با (BLUP) و بدون شجره (BLUP_noPed) به وسیله همانندسازی رایانه­ای بود. در این تحقیق، ژنگانی با سی جفت کروموزوم به‌گونه‌ای همانند­سازی شد که شمار QTLها 3000 و شمار نشانگرها (SNP) 45000 جایگاه در نظر گرفته شدند. همچنین پیش‌فرض­های مختلف شامل دو جمعیت مؤثر 100 و 1000 رأسی، دو جمعیت مرجع 1000 و 2000 رأسی و مقادیر وراثت‌پذیری پایین (05/0)، متوسط (30/0) و بالا (50/0) بررسی شد و برای مقایسۀ مدل­ها از معیارهای صحت، اریبی و میانگین مربعات خطا استفاده گردید. نتایج نشان داد، با افزایش وراثت‌پذیری، صحت ارزش­های اصلاحی در روش­های سنتی و ژنگانی افزایش یافت؛ در همۀ پیش‌فرض‌ها مدل GBLUP صحت بیشتری نسبت به مدل BLUP_noPed داشت؛ اما روش ژنگانی عملکرد بهتری در وراثت­پذیری بالاتر نسبت به روش سنتی BLUP داشت. افزایش جمعیت مؤثر از 100 به 1000 منجر به کاهش صحت ارزش­های اصلاحی ژنگانی به میزان 11درصد شد؛ ولی تأثیری بر صحت ارزش­های اصلاحی روش­های سنتی نداشت. اما با افزایش جمعیت مرجع از 1000 به 2000 حیوان نسبت بهبود صحت ارزش­های اصلاحی ژنگانی به میزان 5درصد بهبود یافت. اگرچه در همۀ پیش‌فرض‌های اریبی (کم برآورد) روش ژنگانی نسبت به روش­های سنتی بیشتر بود؛ امّا با افزایش اندازۀ جمعیت مرجع و وراثت­پذیری، میانگین مربعات خطا در روش ژنگانی نسبت به روش سنتی BLUP کاهش یافت. بنابراین مدل GBLUP می­تواند برای انتخاب حیوانات برتر در جمعیت‌های بدون شجره استفاده شود.

کلیدواژه‌ها

عنوان مقاله [English]

Estimating accuracy of genomic breeding values for complex traits in populations without pedigree using dense markers

نویسندگان [English]

  • Elham Elahinejad 1
  • Hossein Mehrban 2
  • Mostafa Shakhsi-Niaei 3
1 M.Sc. Student, Department of Animal Science, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
2 Assistant Professor, Department of Animal Science, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
3 Assistant Professor, Department of Genetics, Faculty of Science, Shahrekord University, Shahrekord, Iran
چکیده [English]

The aim of this study was to estimate the accuracy of genomic breeding values using GBLUP (Genomic Best Linear Unbiased Prediction) model and compare it with the traditional method with (BLUP) and without (BLUP_noPed) pedigree by computer simulation. In this study, a genome with 30 pairs of chromosomes, 3000 QTL and 45000 marker (SNP) was simulated. The different scenarios including two effective population of 100 and 1000 animals, two reference population size of 1000 and 2000 animals and three heritabilities, low (0.05), medium (0.30) and high (0.5)  were investigated. The criteria were accuracy, bias and mean square error (MSE) of breeding value to compare traditional and genomic methods. The results showed that the accuracy of breeding values increased with increasing heritability. GBLUP mehtod was more accurate than BLUP_noPed in all scenarios and the former model had more performance in higher heritability compared with BLUP model. The accuracy of genomic breeding values decreased about 11% with increasing effective population size 100 to 1000; however, the accuracy of traditional methods was not affected by changing effective population size. Increasing the reference population size 1000 to 2000, the accuracy of genomic breeding values was improved by 5%. Although, the bias (underestimate) was higher for genomic than traditional methods in all scenario; but the MSE of breeding values was lower for GBLUP than BLUP model with increasing reference population size and heritability. Therefore, GBLUP method can be used to select top animals in populations without pedigree.

کلیدواژه‌ها [English]

  • Accuracy
  • effective population
  • Genomic Selection
  • heritability
  • simulation

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سابقه مقاله

  • تاریخ دریافت: 10 اسفند 1395
  • تاریخ بازنگری: 28 خرداد 1396
  • تاریخ پذیرش: 31 خرداد 1396

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