Deep learning for genomic selection of aquatic animals

Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intel...

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Published in:Marine life science & technology Vol. 6; no. 4; pp. 631 - 650
Main Authors: Wang, Yangfan, Ni, Ping, Sturrock, Marc, Zeng, Qifan, Wang, Bo, Bao, Zhenmin, Hu, Jingjie
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.11.2024
Subjects:
Biodiversity
Biological Techniques
Biomedical and Life Sciences
Fish & Wildlife Biology & Management
Food Microbiology
Freshwater & Marine Ecology
Life Sciences
Review
Deep learning
Breeding
Genomic selection
Future direction
Aquaculture
Aquatic animals
ISSN:2662-1746, 2662-1746
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Abstract Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.
AbstractList Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future. The online version contains supplementary material available at 10.1007/s42995-024-00252-y.
Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.
Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.The online version contains supplementary material available at 10.1007/s42995-024-00252-y.Supplementary InformationThe online version contains supplementary material available at 10.1007/s42995-024-00252-y.
Author Wang, Bo
Zeng, Qifan
Sturrock, Marc
Bao, Zhenmin
Ni, Ping
Hu, Jingjie
Wang, Yangfan
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  organization: Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland
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  organization: MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution, Ocean University of China
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  givenname: Bo
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  givenname: Zhenmin
  surname: Bao
  fullname: Bao, Zhenmin
  organization: MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Southern Marine Science and Engineer Guangdong Laboratory
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  givenname: Jingjie
  surname: Hu
  fullname: Hu, Jingjie
  email: hujingjie@ouc.edu.cn
  organization: MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Southern Marine Science and Engineer Guangdong Laboratory
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Issue 4
Keywords Deep learning
Breeding
Genomic selection
Future direction
Aquaculture
Aquatic animals
Language English
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Snippet Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic...
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SubjectTerms Biodiversity
Biological Techniques
Biomedical and Life Sciences
Fish & Wildlife Biology & Management
Food Microbiology
Freshwater & Marine Ecology
Life Sciences
Review
Title Deep learning for genomic selection of aquatic animals
URI https://link.springer.com/article/10.1007/s42995-024-00252-y
https://www.ncbi.nlm.nih.gov/pubmed/39620094
https://www.proquest.com/docview/3140920987
Volume 6
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