Predictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive
محورهای موضوعی : Camelم. مومن 1 , ا. آیت‌آللهی مهرجردی 2 , ا. شیخی 3 , ع. اسماعیلی‌زاده 4 , م. اسدی فوزی 5
1 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
3 - Department of Animal Science, Faculty of Mathematics and Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
4 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
5 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
کلید واژه: genomic statistical method, non parametric, parametric, predictive ability,
چکیده مقاله :
A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used genomic prediction models based on four criteria (mean squared error (MSE), Bias, γy,GEBV and γGEBV,TBV). Results suggested that parametric genomic prediction models have greater superiority over non parametric genomic models under a simple purely additive genetic architecture. Our result also showed that, all parametric methods, other than ridge-regression BLUP (RR-BLUP), could explain most of phenotypic variation because they showed lower MSE, higher predictive correlation (γy,GEBV), the least amount of bias (by,GEBV) and the higher correlations between true breeding values and the estimated genomic breeding values (γTBV,GEBV). Random forest regression had the worst performance among non parametric methods. The simulation results suggested that there is a large difference between performances of non parametric methods in comparison with parametric methods when underlying architecture is purely additive. But this may not happen when dominance and epistatic genetic effects contributing to both additive and non-additive genetic variances.
یک مطالعه شبیه سازی شده به منظور بررسی قابلیت پیشبینی روشهای پارامتری و ناپارامتری پیشبینی ژنومی، هنگامی که صفت کمی تحت تأثیر معماری ژنتیکی کاملا افزایشی صورت گرفت. بدین منظور یک صفت کمّی با وراثتپذیری کاملاً افزایشی (h2=0.3)، تحت تأثیر 300 جایگاه کنترل کننده صفت کمّی (QTL)، شبیهسازی شد. قابلیت پیشبینی 14 مدل آماری براساس چهار معیار اریبی، مجموع مربعات خطا، همبستگی بین مقدار فنوتیپ مشاهده شده و ارزش اصلاحی ژنومی برآورد شده وهمچنین، همبستگی ارزش اصلاحی برآورد شده و ارزش اصلاحی واقعی برآورد گردید. نتایچ نشان داد که مدلهای پیشبینی ژنومی پارامتری قابلیت پیشبینی بهتری نسبت به مدلهای غیرپارامتری دارند. همچنین، تمامی مدلهای پارامتری به غیر از روش RR-BLUP بیشتر واریانس فنوتیپی را میتوانند توجیه کنند و مجموع مربعات خطای کمتر، همبستگی پیشبینی و همبستگی ارزش اصلاحی برآورد شده و ارزش اصلاحی واقعی بالاتری برآورد گردید. همچنین این روشها کمترین اریبی را نشان دادند و مقادیر پیشبینی شده حاصل از آنها نااریبتر بود. روش ناپارامتری Random forest بدترین عملکرد را نشان داد. به طور کلی نتایج این شبیهسازی نشان داد که، تفاوت بسیار زیادی بین روشهای ناپارامتری هنگامیکه صفت تحت تأثیر معماری ژنتیکی غیر افزایشی میباشد وجود دارد. این اتفاق ممکن است زمانیکه اثرات غلبه و اپیستاتیک به عنوان واریانس غیر افزایشی در معماری ژنتکی صفت دخیل باشند وجود نداشته باشد.
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