Genome‐wide association and genomic selection in aquaculture

Reviews in Aquaculture, Journal Year: 2022, Volume and Issue: 15(2), P. 645 - 675

Published: Nov. 17, 2022

Abstract Recent advancements in genomic technologies have led to the discovery and application of DNA‐markers [e.g. single nucleotide polymorphisms (SNPs)] for genetic improvement several aquaculture species. The identification specific regions associated with economically important traits, using, example, genome‐wide association studies (GWAS), has allowed incorporation markers linked quantitative trait loci (QTL) into breeding programs through marker‐assisted selection (MAS). However, most traits economic relevance are expected be controlled by many QTLs, each one explaining only a small proportion variation. For under polygenic control, prediction merit animals based on sum effects at positions across entire genome (i.e. estimated values, GEBV, which used what become known as selection), been demonstrated speed rate gain breeding. aim this review was provide an overview development uncovering basis complex accelerating progress species, well providing future perspectives about deployment novel molecular selective coming years.

Language: Английский

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How useful is genomic data for predicting maladaptation to future climate?

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(4)

Published: April 1, 2024

Abstract Methods using genomic information to forecast potential population maladaptation climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare estimates derived from two methods—Gradient Forests (GF offset ) risk non‐adaptedness (RONA)—using exome capture pool‐seq data 35 39 populations across three conifer taxa: Douglas‐fir varieties jack pine. We evaluate sensitivity these algorithms source input loci (markers selected genotype–environment associations [GEA] those at random). validate methods against 2‐ 52‐year growth mortality measured in independent transplant experiments. Overall, find that both often better predict performance than climatic geographic distances. also GF RONA models surprisingly not improved GEA candidates. Even with promising results, variation projections future climates makes it difficult identify most maladapted either method. Our work advances understanding applicability approaches, discuss recommendations for use.

Language: Английский

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Genomic selection and its research progress in aquaculture breeding

Reviews in Aquaculture, Journal Year: 2022, Volume and Issue: 15(1), P. 274 - 291

Published: Aug. 1, 2022

Abstract Since its introduction in 2001, genomic selection (GS) has progressed rapidly. As a research and application hot topic, GS led to revolution the field of animal plant breeding. Thanks ability overcome shortcomings traditional breeding methods, garnered increasing attention. Both theoretical practical studies have revealed higher accuracy than that breeding, which can accelerate genetic gain. In recent years, many been conducted on aquaculture species, shown produces prediction pedigree‐based method. The present study reviews principles processes, preconditions, advantages, analytical methods factors influencing as well progress into these aspects. Furthermore, future directions are also discussed, should expand more species.

Language: Английский

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Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions

The Plant Genome, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 8, 2025

Abstract Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of regions can help prioritize polymorphisms by considering underlying processes, making models robust accurate. Gene ontology (GO) terms be used for this purpose, the information integrated into through marker categorization. It allows likely causal to account a certain portion genetic variance independently from remaining markers. We systematically tested list 5110 GO predictive performance physiological (platform traits) productivity (field grain yield) in maize ( Zea mays L.) panel using features best linear unbiased (GFBLUP) model. Predictive abilities were compared classical (GBLUP). gains with categorizing based given term strongly depend trait growth conditions, as useful condition or somewhat similar conditions but not same different condition. Overall, results all GFBLUP GBLUP show that former might less efficient latter. Even though we could identify prior criterion determine which offer benefit trait, posteriori find interpretations results, meaning helpful if more about gene functions relationships was known.

Language: Английский

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Single cell genomics as a transformative approach for aquaculture research and innovation

Reviews in Aquaculture, Journal Year: 2023, Volume and Issue: 15(4), P. 1618 - 1637

Published: March 7, 2023

Abstract Single cell genomics encompasses a suite of rapidly maturing technologies that measure the molecular profiles individual cells within target samples. These approaches provide large up‐step in biological information compared to long‐established ‘bulk’ methods profile average all sample, and have led transformative advances understanding cellular biology, particularly humans model organisms. The application single is fast expanding non‐model taxa, including aquaculture species, where numerous research applications are underway with many more envisaged. In this review, we highlight potential research, considering barriers solutions broad uptake these technologies. Focusing on transcriptomics, outline considerations for experimental design, essential requirement obtain high quality cells/nuclei sequencing ectothermic aquatic species. We further data analysis bioinformatics considerations, tailored studies under‐characterized genomes our knowledge heterogeneity marker genes immature. Overall, review offers useful source researchers aiming apply address challenges faced by global sector though an improved biology.

Language: Английский

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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials

Theoretical and Applied Genetics, Journal Year: 2024, Volume and Issue: 137(8)

Published: July 23, 2024

Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional such as climate soil information becoming a common practice in breeding programs. This study explored new ways combine non-genetic using learning. Using the multi-environment trial from Genomes To Fields initiative, different predict maize grain yield were adjusted various inputs: genetic, environmental, or combination of both, either additive (genetic-and-environmental; G+E) multiplicative (genotype-by-environment interaction; GEI) manner. When including data, mean accuracy learning increased up 7% over well-established Factor Analytic Multiplicative Mixed Model among three cross-validation scenarios evaluated. Moreover, G+E was more advantageous than GEI given superior, at least comparable, accuracy, lower usage computational memory time, flexibility accounting for interactions by construction. Our results illustrate provided ML framework, particularly feature engineering. We show that engineering stage offers viable option envirotyping generates valuable models. Furthermore, we verified may be considered tree-based approaches without explicitly model. These findings support growing interest merging genotypic predictive modeling.

Language: Английский

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The study of the genomic selection of white gill disease resistance in large yellow croaker (Larimichthys crocea)

Aquaculture, Journal Year: 2023, Volume and Issue: 574, P. 739682 - 739682

Published: May 15, 2023

Language: Английский

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Single-step genomic prediction for body weight and maturity age in Finnish rainbow trout (Oncorhynchus mykiss)

Aquaculture, Journal Year: 2024, Volume and Issue: 585, P. 740677 - 740677

Published: Feb. 18, 2024

The use of genomic information has been proven to be a highly effective in predicting breeding values (GEBV) across various species, including aquatic organisms. In the Finnish national rainbow trout programme, integration selection holds particular significance for traits recorded on sibling fish reared main commercial sea production environment, given occurs among candidates freshwater nucleus. family tanks allow maintain pedigree large number fish, and genotyping portion accompanied with single-step evaluation (ssGBLUP) would high intensity simultaneously make possibilities selection. this study we used three different statistical approaches quantify accuracy ssGBLUP body weight maturity age, relative based traditional sire-dam-offspring (PBLUP). data included 600,409 which 214,410 4573 were phenotyped reported genotyped, respectively. Firstly, phenotypic cross validation showed that had slightly better prediction power age at sea, an average 2.7% increase compared PBLUP. Secondly, linear regression (LR) GEBVs computed using either full or reduced dataset demonstrated model consistently lower bias dispersion PBLUP model, underscoring its efficacy dealing complex datasets like ours. When considering reliability [G]EBV predictions, resulted significant improvement. There is, average, notable 50% predictions sea-recorded traits. Thirdly, enhancement was further evidenced by individual assessment [G]EBVs reverse methodology. Notably, genotyped individuals experienced 0.27 units reliability, while ungenotyped corresponding 0.03 units. results show method higher both developed will instrumental Finland's breeding, facilitating precise efficient new candidates.

Language: Английский

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Comparative analysis of genomic prediction models based on body weight trait in large yellow croaker (Larimichthys crocea)

Aquaculture, Journal Year: 2025, Volume and Issue: 599, P. 742125 - 742125

Published: Jan. 7, 2025

Language: Английский

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Genetic diversity of Olive flounder (Paralichthys olivaceus) and the impact of selective breeding on Korean populations

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0318672 - e0318672

Published: April 16, 2025

This study aimed to identify the population structure and genetic diversity of olive flounder ( Paralichthys olivaceus ) in Korea examine potential for improvement aquaculture populations. PCA showed NIFS FarmA as closely related clusters, while FarmB exhibited moderate differentiation with greater variability. Fst analysis indicated high similarity between farmed populations (0.021–0.043) but significant from wild (0.274–0.295). Admixture highlighted a shared ancestral component (over 70%) among populations, contrasting unique makeup The phylogenetic tree confirmed these patterns, forming close branches, showing intermediate placement, clustering separately. Additionally, genomic estimated breeding values body weight no differences FarmB, prediction accuracy was higher (47%) compared (45%), indicating closer relationship FarmA. These findings emphasize critical role selective gene flow shaping offering valuable insights improving growth traits maintaining aquaculture.

Language: Английский

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