Genomic selection and its research progress in aquaculture breeding

Song, Huihui; Tian, Dong; Yan, Xiaoyu; Wang, Wei; Tian, Zhaohui; Sun, Ai; Dong, Ying; Zhu, Hua; Hu, Hongxia

doi:10.1111/raq.12716

Cited by 36 publications

(16 citation statements)

References 211 publications

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“…The extensive use of genomic selection in both livestock and crops has led to a series of algorithms, though none of the algorithms suit all the species and traits under study [ 58 ]. Studies that have applied GP algorithms in aquaculture species have mainly focused on comparison of traditional algorithms [ 62 , 63 ]. To date, no studies have compared the machine-learning algorithms with traditional algorithms in a variety of aquatic animals.…”

Section: Discussionmentioning

confidence: 99%

Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals

Wang

Yang

et al. 2022

Genes

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The extensive use of genomic selection (GS) in livestock and crops has led to a series of genomic-prediction (GP) algorithms despite the lack of a single algorithm that can suit all the species and traits. A systematic evaluation of available GP algorithms is thus necessary to identify the optimal GP algorithm for selective breeding in aquaculture species. In this study, a systematic comparison of ten GP algorithms, including both traditional and machine-learning algorithms, was conducted using publicly available genotype and phenotype data of eight traits, including weight and disease resistance traits, from five aquaculture species. The study aimed to provide insights into the optimal algorithm for GP in aquatic animals. Notably, no algorithm showed the best performance in all traits. However, reproducing kernel Hilbert space (RKHS) and support-vector machine (SVM) algorithms achieved relatively high prediction accuracies in most of the tested traits. Bayes A and random forest (RF) better prevented noise interference in the phenotypic data compared to the other algorithms. The prediction performances of GP algorithms in the Crassostrea gigas dataset were improved by using a genome-wide association study (GWAS) to select subsets of significant SNPs. An R package, “ASGS,” which integrates the commonly used traditional and machine-learning algorithms for efficiently finding the optimal algorithm, was developed to assist the application of genomic selection breeding of aquaculture species. This work provides valuable information and a tool for optimizing algorithms for GP, aiding genetic breeding in aquaculture species.

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Section: Discussionmentioning

confidence: 99%

Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals

Wang

Yang

et al. 2022

Genes

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“…GS enables the calculation of breeding values by estimating the effects of markers at a high density covering the whole genome, allowing for higher genetic variation in given traits to be explained. 27,210 GS is an effective method for predicting individual phenotypes using high-density genetic markers, which can enhance selection accuracy, particularly for traits with low heritability or that are costly to measure. It has been estimated that the accuracy of genome-based prediction of breeding values for growth-related traits is 25%-30% higher than that of pedigree-based prediction in a C. gigas population.…”

Section: Development and Current Status Of Molecular Breedingmentioning

confidence: 99%

“…technologies, coupled with related advances in statistical methods and computational efficiency, will further promote the use of GS in oysters. 210…”

Section: Genomic Selectionmentioning

confidence: 99%

Genetic improvement of oysters: Current status, challenges, and prospects

Jiang,

Chen,

Jiang

et al. 2023

Reviews in Aquaculture

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Oysters are one of the most commercially important shellfish species and have been cultured for thousands of years. Oyster aquaculture supports the major aquaculture industries in many countries. Over the last few decades, the oyster breeding and aquaculture industries have developed rapidly to meet the continually growing demand. Many researchers have made significant efforts toward the genetic improvement of commercially important traits in oysters. Some strains with fast‐growing, disease‐resistant, and stable shell‐colours have been developed through selective breeding. Some hybrid varieties have been developed by crossing different geographical populations or cultivated strains. Several hybrids exhibit considerable genetic variation and improved productive performance. Additionally, polyploid induction technologies have been applied in the oyster aquaculture industry, which provides a useful tool for performance improvement and genetic containment of cultured stocks. At present, the development of molecular breeding also provides a great opportunity for oyster genetic improvement. These advances in oyster breeding have improved the quality of oysters, brought great economic benefits, and been conducive to the sustainability of oyster production. Nonetheless, there are still some limitations and obstacles in oyster breeding, such as infectious diseases, summer mortality, conservation of germplasm resources, environmental contamination, and climate change. The present review provides an overview of the current status, challenges, and prospects in oyster breeding.

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“…These so-called sib traits are the perfect target for the implementation of genomic selection because it captures the within-family genetic variation in addition to the between-family genetic variation. Over the recent years, a large number of studies have demonstrated that the application of genomic selection significantly improves the response to selection of aquaculture breeding programmes (2, [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…To date, many studies have investigated the potential use of low-density (LD) SNP panels for genomic selection in various aquaculture species and they concluded that LD-panels containing 1,000-2,000 SNPs (17,18) to 6,000 SNP (19). Depending on the species and the trait (reviewed in Song et al, 2022) (11) are sufficient to achieve an accuracy of genomic prediction similar to that obtained with a medium or high-density panel. In those studies, further reduction of the density to hundreds of SNPs resulted in a significant drop in accuracy (17,20).…”

Section: Introductionmentioning

confidence: 99%

Potential of imputation for cost-efficient genomic selection for resistance toFlavobacterium columnarein rainbow trout (Oncorhynchus mykiss)

Fraslin

Robledo

Kause

et al. 2023

Preprint

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Background: Flavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease affecting rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of host resistance. However, genomic selection is expensive partly due to the cost of genotyping high numbers of animals using high-density SNP arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2,874 challenged fish and 469 fish from the parental generation (n=81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD-panels was assessed for panels of 10 different densities, created in silico using two sampling methods, random and equally spaced. All LD-panels were also imputed to the full 28K HD-panel using the parental generation as the reference population, and genomic predictions were reevaluated. The potential of prioritizing SNPs showing association with resistance to F. columnare was also tested for the six lower densities. Results: Similar results were obtained with random and equally spaced sampling of SNPs for accuracy of both imputation and genomic predictions. Using LD-panels of at least 3,000 makers or lower density panels (as low as 300 markers) combined with imputation resulted in comparable accuracy to the 28K HD-panel and 11% higher accuracy than pedigree-based predictions. Conclusions: Compared to using the commercial HD-panel, LD-panels with imputation may provide a more affordable route to genomic prediction of breeding values, supporting wider adoption of genomic selection in aquaculture breeding programmes.

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Genomic selection and its research progress in aquaculture breeding

Cited by 36 publications

References 211 publications

Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals

Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals

Genetic improvement of oysters: Current status, challenges, and prospects

Potential of imputation for cost-efficient genomic selection for resistance toFlavobacterium columnarein rainbow trout (Oncorhynchus mykiss)

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