%0 Journal Article %1 Hay20151 %A Hay, El Hamidi %A Rekaya, Romdhane %D 2015 %J Livestock Science %K 2015 genomics methodology %P 1 - 7 %R http://dx.doi.org/10.1016/j.livsci.2015.03.027 %T A multi-compartment model for genomic selection in multi-breed populations %U http://www.sciencedirect.com/science/article/pii/S1871141315001754 %V 177 %X Abstract Genome wide evaluation methods are often conducted using purebred populations. Estimation and often validation are carried out using primarily select elite animals. This process is successful when estimated \SNP\ effects are used to predict genomic breeding values of animals of similar breed. This approach fails when \SNP\ estimates in one breed are used for genomic prediction in other breeds. In this study, we proposed a multi-compartment model where the effect of an \SNP\ marker could differ between breeds. Two simulation scenarios were carried out using an admixed population of two divergent lines (A and B), first using a low density panel (300 SNPs) and second using a high density panel (60 k SNPs). Divergence between the two lines was artificially created by multiplying marker effects in one line by a variable α which was sampled from different uniform or normal distributions. The proposed method was compared to the pooled data approach based on the accuracy of predicting the true breeding values. In the first simulation scenario, the prediction accuracy using the pooled data approach for line A, was 0.40, 0.39 and 0.38 when α was generated from a uniform distribution between −2, 2, −4, 4 and −8, 8 respectively. Using our proposed method, the corresponding accuracies were 0.47, 0.46 and 0.46, respectively. A similar trend was observed for line B with a clear superiority of the multi-compartment model over the pooled data approach with an increase ranging from 17 to 47% and increases as the divergence between lines increases. In the second scenario, when α was sampled from a uniform −2.2, accuracy for line A (B) was 0.32 (0.30) using pooled data model, and 0.33 (0.32) using the multi-compartment model. Although smaller than in first simulation scenario, the proposed method still has a superiority of 3 to 7%. Similar performance was observed when α was sampled from uniform −4,4.

@article{Hay20151, abstract = {Abstract Genome wide evaluation methods are often conducted using purebred populations. Estimation and often validation are carried out using primarily select elite animals. This process is successful when estimated \{SNP\} effects are used to predict genomic breeding values of animals of similar breed. This approach fails when \{SNP\} estimates in one breed are used for genomic prediction in other breeds. In this study, we proposed a multi-compartment model where the effect of an \{SNP\} marker could differ between breeds. Two simulation scenarios were carried out using an admixed population of two divergent lines (A and B), first using a low density panel (300 SNPs) and second using a high density panel (60 k SNPs). Divergence between the two lines was artificially created by multiplying marker effects in one line by a variable α which was sampled from different uniform or normal distributions. The proposed method was compared to the pooled data approach based on the accuracy of predicting the true breeding values. In the first simulation scenario, the prediction accuracy using the pooled data approach for line A, was 0.40, 0.39 and 0.38 when α was generated from a uniform distribution between [−2, 2], [−4, 4] and [−8, 8] respectively. Using our proposed method, the corresponding accuracies were 0.47, 0.46 and 0.46, respectively. A similar trend was observed for line B with a clear superiority of the multi-compartment model over the pooled data approach with an increase ranging from 17 to 47% and increases as the divergence between lines increases. In the second scenario, when α was sampled from a uniform [−2.2], accuracy for line A (B) was 0.32 (0.30) using pooled data model, and 0.33 (0.32) using the multi-compartment model. Although smaller than in first simulation scenario, the proposed method still has a superiority of 3 to 7%. Similar performance was observed when α was sampled from uniform [−4,4]. }, added-at = {2016-05-15T19:36:15.000+0200}, author = {Hay, El Hamidi and Rekaya, Romdhane}, biburl = {https://www.bibsonomy.org/bibtex/206ec764a338c24c34e8e1129fa1e653a/uga.abgg}, doi = {http://dx.doi.org/10.1016/j.livsci.2015.03.027}, interhash = {b8d6f085aaabfd610d34b3a9233f83d7}, intrahash = {06ec764a338c24c34e8e1129fa1e653a}, issn = {1871-1413}, journal = {Livestock Science }, keywords = {2015 genomics methodology}, pages = {1 - 7}, timestamp = {2016-05-15T19:36:15.000+0200}, title = {A multi-compartment model for genomic selection in multi-breed populations }, url = {http://www.sciencedirect.com/science/article/pii/S1871141315001754}, volume = 177, year = 2015 }