ArticleFrontiers in genetics2020
Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 100 citations in OpenAlex.
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- A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean.Plants (Basel, Switzerland) · 2026Article
- Evaluation of genomic selection to improve survival of eastern oysters infected withFrontiers in genetics · 2026Article
- Article
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- Genome-wide association study and genomic selection for growth-related traits in Eastern oyster (Crassostrea virginica).BMC genomics · 2025Article
- Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass.Plant physiology · 2025Article
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- Optimization of high-throughput marker systems for genomic prediction in alfalfa family bulks.The plant genome · 2025Article
- Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species.BMC genomics · 2025Article
- Genome-Assisted Gene-Flow Rescued Genetic Diversity Without Hindering Growth Performance in an Inbred Coho Salmon (Oncorhynchus kisutch) Population Selected for High Growth Phenotype.Marine biotechnology (New York, N.Y.) · 2025Article
- Genome-wide association analysis of resistance to bacterial cold-water disease in an important rainbow trout aquaculture breeding population.Frontiers in genetics · 2025Article
- Genomic selection optimization in blueberry: Data-driven methods for marker and training population design.The plant genome · 2024Article
- Genomic selection for resistance to one pathogenic strain ofFrontiers in genetics · 2024Article
- Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss).Genetics, selection, evolution : GSE · 2023Article
- Developing Methods for Maintaining Genetic Diversity in Novel Aquaculture Species: The Case ofAnimals : an open access journal from MDPI · 2023Article
- Applying genetic technologies to combat infectious diseases in aquaculture.Reviews in aquaculture · 2023Review
- Genomic selection for survival under naturally occurring Saprolegnia oomycete infection in farmed European whitefish Coregonus lavaretus.Journal of animal science · 2023Article
- Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species.Frontiers in genetics · 2023Article
- Accurate prediction of quantitative traits with failed SNP calls in canola and maize.Frontiers in plant science · 2023Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic selection increases the rate of genetic gain in breeding programs, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals, which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors, more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9,000 SNPs) to accurately predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). The traits show heritabilities between 0.19-0.49, and genomic prediction accuracies using the full density panel of 0.55-0.87. A consistent pattern of genomic prediction accuracy was observed across species with little or no accuracy reduction until SNP density was reduced below 1,000 SNPs (prediction accuracies of 0.44-0.75). Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93% of maximum accuracy achieved with 1,000 SNPs, 89% with 500 SNPs, and 70% with 100 SNPs). A notable drop in accuracy was observed between 200 SNP panels (0.44-0.75) and 100 SNP panels (0.39-0.66). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.
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