روش های کارآمد برآورد ارزش اصلاحی ژنومی و مکان یابی QTNها در راهبردهای به نژادی گاو شیری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم دامی، دانشکده کشاورزی، دانشگاه زابل، زابل، ایران

2 هبات علمی- دانشگاه زابل

3 دانشگاه زابل

4 مرکز بهبود ژنتیکی دام، دانشگاه گوئلف، انتاریو، کانادا. و شرکت سیمکس، انتاریو، کانادا.

چکیده

سابقه و هدف: در دسترس بودن هزاران تک‌نکلئوتید چندشکل که در سراسر ژنوم گسترش یافته، امکان استفاده از اطلاعات نشانگرهای ژنوم گسترده در انتخاب ژنومی برای پیش‌بینی ارزش اصلاحی کل را فراهم نمودند. اثر متقابل بین چند جایگاه ژن، به تغییرات فنوتیپی مرتبط با بیان صفات پیچیده چند ژنی کمک می‌کند. مطالعه حاضر به منظور مقایسه عملکرد مدل‌های مختلف با اثر ژنتیکی افزایشی و غیرافزایشی به ویژه اثر اپیستاتیک در پیش‌بینی اجزای واریانس و ارزش‌های اصلاحی ژنومی و همچنین مکان‌یابی نوکلئوتیدهای کنترل کننده صفت کمی در یک جمعیت گاو شیری بر اساس مدل بیز لاسو انجام شد.
مواد و روش‏ها: یک جمعیت گاو شیری بر اساس انتخاب چهار مسیره به منظور بیشینه نمودن سرعت پیشرفت ژنتیکی در طی ۱۰ نسل با اندازه مؤثر 100 فرد در جمعیت تاریخی شبیه‌سازی شدند. ساختار ژنومی شامل 3 کروموزوم با طول 100 سانتی مورگان فرض شد که بر روی هر کدام 1000 نشانگر 2 آللی با فراوانی 5/0 جانمایی شدند. 50 جایگاه کنترل کننده صفت کمی دو آللی با فراوانی برابر که به صورت تصادفی در روی هر کروموزوم قرار داشتند، شبیه‌سازی شدند. برای اثرات آللی QTL از یک توزیع آماری گاما با شیب پارامتر 4/0 در نرم افزار QMSim استفاده شد و نمونه‌برداری انجام گرفت. اطلاعات فنوتیپی و ژنوتیپی 10 نسل آخر برای تجزیه و تحلیل‌ها استفاده شدند. اثرات نشانگری، مؤلفه‌های واریانس و پارامترهای ژنتیکی با روش بیز لاسو در قالب 6 مدل آماری برآورد شدند که مدل 1 شامل تنها آثار افزایشی نشانگرها، مدل 2 شامل اثرات چندژنی حیوان، مدل 3 شامل اثرات توأم افزایشی نشانگرها و چند‌ژنی حیوان، مدل 4 شامل اثرات غالبیت نشانگرها و آثار چندژنی حیوان، مدل 5 شامل اثرات افزایشی و غالبیت نشانگرها بعلاوه آثار چند‌ژنی حیوان و مدل 6 شامل اثرات افزایشی، غالبیت و اپیستاتیک نشانگرها بعلاوه آثار چند‌ژنی حیوان می‌باشند. به منظور کنترل خطای اول در برآورد آثار نشانگری از آزمون بنفرونی در سطح احتمال یک درصد استفاده شد.
یافته‏ها: نتایج این مطالعه نشان داد که مدل افزایشی (یا مدل چند ‌ژن) به تنهایی نمی‌تواند مقادیر واریانس‌های گمشده یا مخفی را نمایان سازد، به نحوی که افزودن آثار ژنتیکی غیر‌افزایشی شامل غالبیت و اپیستاتیک نشانگرها منجر به کاهش واریانس باقیمانده شد. همچنین در مدل کامل شامل آثار افزایشی و غیر‌افزایشی، واریانس ژنتیکی کل افزایش یافت. با اضافه شدن آثار غیرافزایشی در مدل آماری میزان وراثت‌پذیری عام افزایش و وراثت‌پذیری خاص صفت کاهش یافت. صحت برآورد ارزش‌های اصلاحی در مدل یک کمترین و در مدل-های 3، 4، 5 و 6 مشابه هم بودند. مدل 5 با کمترین QTN مثبت کاذب و به نسبت بیشترین مثبت واقعی مدل مناسبی در ارزیابی ژنتیکی صفت مورد مطالعه بود. با قرار دادن آثار چندژنی و آثار غیر‌افزایشی در مدل از تعداد خطاهای مثبت کاذب کاسته شد.
نتیجه‏گیری: نتایج این تحقیق نشان داد اجزای واریانس افزایشی و غالبیت نشانگرها بر میزان تنوع ژنتیکی صفت مورد مطالعه سهم کم داشت، اما اثرات چندژنی حیوان و اپیستاتیک بیشترین سهم را در بروز تنوع صفت داشتند. همچنین نتایج نشان داد که سهم عمده جایگاه های ژنی در بروز صفات مربوط به ژن های افزایشی با اثر افزایشی هستند که توزیع نرمال دارند. نادیده گرفتن آثار غیر‌افزایشی موجب التهاب در واریانس افزایشی صفت می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Effective methods for estimating genomic breeding values and QTN mapping in dairy cows breeding strategies

نویسندگان [English]

  • Hossein Abdollahy 1
  • Gholam Reza Dashab 2
  • Mohammad Rokouei 3
  • Mehdi Sargolzaei 4
1 Department of Animal Science, University of Zabol, Zabol, Iran
2 department of Animal Science, University of Zabol
3 Department of Animal Science, University of Zabol, Zabol, Iran
4 Mehdi Sargolzaei, Department of Pathobiology, University of Guelph, Guelph, Canada, HiggsGene Solutions Inc., Guelph, Canada
چکیده [English]

Background and objective: The availability of thousands of polymorphic single nucleotides (SNPs) spread across the genomes made it possible to use genome-wide marker information to predict total breeding value in the implementation of genomic selection. The interaction between several gene loci contributes to the phenotypic changes associated with the expression of complex polygenic traits. The present study aimed to compare the performance of different models with additive and non-additive genetic effects, especially epistasis effects in predicting variance components and genomic breeding values as well as QTN in a dairy cow population based on Bayes Lasso model.
Materials and methods: A population of dairy cattle was simulated based on a choice of four pathways to maximize the rate of genetic progress over ten generations with an effective size of 100 individuals in the base population. The genomic structure was assumed consisting of 3 chromosomes with a length of 100 cM and On each chromosome were located 1000 markers 2 allelic markers with a frequency of 0.5. 50 QTL double alleles with equal frequency were randomly assigned to each chromosome. For QTL allelic effects, a gamma distribution with a parameter slope of 0.4 was used in QMSim software and sampling was performed. Phenotypic and genotypic data of the last 10 generations were used for analysis. Marker effects, variance components and genetic parameters were estimated using the Bayes Lasso method in the form of 6 statistical models, that model 1 includes only the marker additive effects, model 2 includes the polygenic effects of animal, model 3 includes the combined effects of additive marker effects and polygenic effects of animal, model 4 includes the dominance effect of markers and polygenic effects of animal, model 5 includes the dominance and additive effects of markers in addition to the polygenic effects of animal, and finally, model 6 includes the additive, dominance and epistatic effects of markers in addition to the polygenic effects of animal. To control the type I error in estimating marker effects, Bonferroni test was used at 1% probability level.
Results: The results of this study showed that the additive model or the polygenic model alone could not show the missing or hidden variance, in such a way that adding non-additive genetic effects including dominance and epistatic effects reduced the residual variance effects. Also in the complete model including additive and non-additive effects, the total genetic variance increased. With the addition of non-additive effects in the statistical model the Hertability in the broad sense was increased and the Hertabilitity in the narrow sense of trait decreased. The accuracy of breeding value estimation in model 1 was the lowest and in models 3 to 6 was the same. Model 5 with the lowest false positive QTN and the highest true positive proportion was the appropriate model for genetic evaluation of the studied traits. By including polygenic and non-additive effects in the model, the number of false positive errors was reduced.
Conclusion: The results of this study showed that the components of additive and dominance variance markers had a small share in the genetic diversity of the studied trait, but the polygenic effects of animal and epistatic had the largest share in the exploring of genetic diversity of trait. The results also showed that the major contribution of gene loci in the exploring of traits is related to additive genes with an additive effect that have a normal distribution. Ignoring non-incremental effects causes inflammation in the additive variance of the trait.

کلیدواژه‌ها [English]

  • Dairy Cattle
  • Genomic selection
  • Variance components
  • Bayes LASSO
  • Nonadditive effects
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