Back to the articles list | Back to browse issues page

XML Print

Comparing machine learning algorithms and linear model for detecting significant SNPs for genomic evaluation of growth traits in F2 chickens
Hossein Bani Saadat1 , Rasoul Vaez Torshizi 2, Gader Manafiazar3 , Ali Akbar Masoudi1 , Alireza Ehsani1 , Saleh Shahinfar4
1- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University
2- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University , rasoult@modares.ac.ir
3- Department of Animal Science and Aquaculture, Dalhousie University
4- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience
Abstract:   (259 Views)
 High-density single nucleotide polymorphisms (SNP) panels are expensive, especially in developing countries, but methods have been developed to detect critical SNPs from these panels and design low-density chips for genomic evaluation at lower cost. This study aimed to determine the efficiency of random forest (RF) and gradient boosting machine (GBM) algorithms, and Linear Model (LM) in identification of SNPs subsets to predict genomic estimated breeding values (GEBVs) for body weights at 6 (BW6) and 9 (BW9) weeks in broiler chickens and compare the predicted GEBVs with those obtained by the 60k SNP panel. The data were collected on 312 F2 chickens that genotyped with 60K Illumina SNP BeadChip. After applying quality control, the remaining 45,512 SNPs were ranked based on p-values, mean square error percentage, and relative influence, obtained by LM, RF and GBM methods, respectively. Then subsets of top 400, 1000, 3000 and 5000 SNPs, selected by each method, employed to construct genomic relationship matrices for the prediction of GEBVs with genomic best linear unbiased prediction model. Results indicated that predicted accuracies by RF and GBM were generally higher than LM. A Subset of 1000 SNPs selected by RF and GBM algorithms compared to the total SNPs increased accuracy from 0.38 to 0.64 and 0.66 for BW6, and from 0.42 to 0.60 and 0.66 for BW9, respectively. The findings of the present study provide that machine learning methods, especially GBM, can perform better than LM in selecting important SNPs and increase the accuracy of genomic prediction in broiler chickens.  
Keywords: genomic evaluation, body weight, broilers, machine learning
Full-Text [PDF 1026 kb]   (115 Downloads)    
Article Type: Original Research | Subject: Animal Genetics
Received: 2023/05/24 | Accepted: 2023/11/18 | Published: 2024/03/31
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.