بررسی صحت مدل ‏های تک صفتی و چند صفتی ژنومی در حضور اثر متقابل ژنوتیپ و محیط

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه علوم دامی، باشگاه پژوهشگران جوان و نخبگان، دانشگاه آزاد اسلامی، واحد آستارا

چکیده

هدف از این تحقیق ارزیابی مدل­های حیوانی مختلف در سناریوهای متفاوت ژنومی جهت برآورد ارزش­‌های اصلاحی ژنومی و تشخیص وجود اثر متقابل ژنوتیپ و محیط (G × E) بود. داده‌­های ژنومی با تراکم‌­های متفاوت جایگاه­های صفات کمّی (100 و 500) و سطوح مختلف عدم تعادل پیوستگی (کم و زیادLD =) با تراکم K10 برای 30 کروموزم شبیه­‌سازی شدند. صفتی در سه محیط مختلف با وراثت­پذیری­­های 10/0، 25/0 و 50/0 روی این ژنوم شبیه­سازی شد. در مرحله بعد، همبستگی ژنتیکی ضعیف (47/0) و قوی‌ (83/0)  بین محیط سه (50/0h2=) با محیط­های یک (10/0h2=) و دو (25/0h2=) ایجاد شد. نتایج نشان داد صحت پیش­بینی ژنومی با افزایش هر یک از عوامل سطح LD ، وراثت­پذیری و همبستگی ژنتیکی بین صفات افزایش یافت. استفاده از مدل چند صفتی نسبت به مدل تک صفتی باعث افزایش صحت پیش­‌بینی ژنومی شد. صحت پیش­­بینی­های ژنومی هنگامی که ارزش های اصلاحی ژنومی حیوانات محیط سوم با استفاده از اطلاعات ژنومی خویشاوندانشان در محیط دوم برآورد شد بالاتر بود و بیش­ترین صحت پیش­بینی ژنومی (422/0) برای سناریو با تعداد پایین QTL و عدم تعادل پیوستگی بالا مشاهده شد. همچنین، با افزایش درصد حیوانات از 25 به 75 درصد، صحت پیش­بینی ژنومی افزایش یافت. به طور کلی در صورت وجود اثر متقابل G × E، سطح LD، نوع حیوانات موجود در جمعیت مرجع و همبستگی ژنتیکی بین محیط­های مختلف نقش مهمی را ایفا می­کنند. در نتیجه می­توان بیان کرد که با لحاظ کردن اثر متقابل ژنوتیپ و محیط، صحت پیش­­بینی ژنومی بیشتر و فهم بهتری از تنوع ژنتیکی صفات کمّی حاصل می­شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigation of the genomic accuracy of single-trait and multiple-trait animal models in the presence of interaction between genotype and environment

نویسنده [English]

  • Y. Naderi
Assistant professor, Department of Animal Science, Young Researchers Club, Islamic Azad University, Astara Branch, Astara, Iran
چکیده [English]

The objective of this study was to evaluate different animal models in different genomic scenarios to estimate the breeding values and to detect genotype × environment (G × E) interaction. Genomic data were simulated to reflect variations in number of QTL (100 and 500) and linkage disequilibrium (low and high) using 10K SNP panel for 30 chromosomes. On this genome, the trait simulated in three different environments with heritabilities 0.10, 0.25 and 0.50, respectively. In the next phase, low (0.47) and high (0.83) genetic correlations were assigned between third environment (h2=0.50) with first (h2=0.10) and second (h2=0.25) environments. The results indicated that the accuracy of genomic prediction increased with increasing the heritability, linkage disequilibrium and the genetic correlation between the traits. Comparing to single trait animal model, multiple trait animal model increased accuracy of genomic prediction. Accuracies of genomic predictions were generally high when the genomic breeding values of third environment were estimated using information of their relatives in the second environment, and the highest accuracy (0.422) obtained from the scenario with high linkage disequilibrium and low QTL. Also, accuracies of genomic prediction increased with increasing percentage of animals from 25 to 75. Generally, the level of LD, type of animals in training set, number of phenotypic records in validation set and genetic correlation across environments play important roles if G × E interaction exists. In conclusion, considering the G × E interaction contributes to understanding variations of quantitative trait and increasing accuracy of genomic prediction.

کلیدواژه‌ها [English]

  • Simulation
  • Accuracy of genomic prediction
  • Linkage disequilibrium
  • Animal model
  • Genetic correlation

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تحقیقات تولیدات دامی
دوره 7، شماره 4
اسفند 1397
صفحه 1-12

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