Files and links (2)
Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms742.13 kB
Published VersionCC BY V4.0, Open Access
Journal article
Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms
G3 , Vol.12(1), pp.1-13
2022
Abstract
Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318–400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3–15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.
Details
- Title
- Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms
- Authors
- Nguyen Thanh Vu (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringTran Huu Phuc (Author) - Research Institute for Aquaculture No. 2 (Vietnam)Kim Thi Phuong Oanh (Author) - Vietnam Academy of Science and TechnologyNguyen Van Sang (Author) - Research Institute for Aquaculture No. 2 (Vietnam)Trinh Thi Trang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringNguyen Hong Nguyen (Corresponding Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
- Publication details
- G3 , Vol.12(1), pp.1-13
- Publisher
- Oxford University Press
- Date published
- 2022
- DOI
- 10.1093/g3journal/jkab361
- ISSN
- 2160-1836
- Copyright note
- (C) The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Organisation Unit
- Faculty of Science, Health, Education and Engineering; Faculty of Arts, Business and Law; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99585407202621
- Output Type
- Journal article
Metrics
1 File views/ downloads
48 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Genetics & Heredity
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites