Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep.

Mei Han,Shuangbao Gun,Guoyan Qiao,Tingting Guo,Bohui Yang,Chao Yuan,Jianbin Liu,Hongchang Zhao,Tianxiang Wang,Yaojing Yue,Shaohua Zhu,Fanwen Li

doi:10.3389/fvets.2020.573692

Abstract

Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.

Highlights

In classical models of quantitative or complex trait genetics, the phenotypic value of each trait is controlled by a large number of loci; the interaction and alternative splicing of genes play an extremely essential role [1]
Samples were of different genders and from different herds, factors that altered phenotypes in a fixed manner, so system environmental effects were added to the framework. resiTdhuealcoeffmepcot n(σeEn2)tswoefrveaersiatinmceatoefdaadddoitpivtienggetnheetiMc eAffGecmtl.and y = Xb + Zu + e where y represents the phenotypic value of the individual and b refers to the vector of fixed effects, since the individuals involved in the current study were from different herds; the fixed effects include the gender of each individual and the different herds (H1–H7). u is the vector of breeding values of the individual, e is the vector of residual effects, X is the design matrix corresponding to fixed effects, and Z is the design matrix corresponding to breeding values
A number of studies have indicated that, as the level of heritability increases, the accuracy of genome prediction increases [39, 56, 57]. This was found in the current study: traits with high heritability, such as staple length (SL) and fiber diameter (FD), showed higher accuracy of prediction than fleece extension rate (FER) and red blood cell count (RBC), which, with low heritability, suggested that the level of additive genetic variance has a positive effect on the accuracy of prediction

Summary

Introduction

In classical models of quantitative or complex trait genetics, the phenotypic value of each trait is controlled by a large number of loci; the interaction and alternative splicing of genes play an extremely essential role [1]. The phenotypic value is affected by non-genetic and environmental factors [2]. The selection is based on the predicted total effect of the loci within an individual or their. Genomic selection (GS) adopts markers covering the entire genome, so that these markers can be used to explain all genetic variations [3]. Compared with traditional selection methods, it has higher prediction accuracy; in addition, it could reduce the generation interval and increase the genetic progress [4, 5]

Objectives

Methods

Results

Discussion

Conclusion