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Dwivedi SL, Heslop-Harrison P, Amas J, Ortiz R, Edwards D. Epistasis and pleiotropy-induced variation for plant breeding. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38875130 DOI: 10.1111/pbi.14405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- Department of Genetics and Genome Biology, Institute for Environmental Futures, University of Leicester, Leicester, UK
| | - Junrey Amas
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - David Edwards
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
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2
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Kess T, Lehnert SJ, Bentzen P, Duffy S, Messmer A, Dempson JB, Newport J, Whidden C, Robertson MJ, Chaput G, Breau C, April J, Gillis C, Kent M, Nugent CM, Bradbury IR. Variable parallelism in the genomic basis of age at maturity across spatial scales in Atlantic Salmon. Ecol Evol 2024; 14:e11068. [PMID: 38584771 PMCID: PMC10995719 DOI: 10.1002/ece3.11068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 01/31/2024] [Indexed: 04/09/2024] Open
Abstract
Complex traits often exhibit complex underlying genetic architectures resulting from a combination of evolution from standing variation, hard and soft sweeps, and alleles of varying effect size. Increasingly, studies implicate both large-effect loci and polygenic patterns underpinning adaptation, but the extent that common genetic architectures are utilized during repeated adaptation is not well understood. Sea age or age at maturation represents a significant life history trait in Atlantic Salmon (Salmo salar), the genetic basis of which has been studied extensively in European Atlantic populations, with repeated identification of large-effect loci. However, the genetic basis of sea age within North American Atlantic Salmon populations remains unclear, as does the potential for a parallel trans-Atlantic genomic basis to sea age. Here, we used a large single-nucleotide polymorphism (SNP) array and low-coverage whole-genome resequencing to explore the genomic basis of sea age variation in North American Atlantic Salmon. We found significant associations at the gene and SNP level with a large-effect locus (vgll3) previously identified in European populations, indicating genetic parallelism, but found that this pattern varied based on both sex and geographic region. We also identified nonrepeated sets of highly predictive loci associated with sea age among populations and sexes within North America, indicating polygenicity and low rates of genomic parallelism. Despite low genome-wide parallelism, we uncovered a set of conserved molecular pathways associated with sea age that were consistently enriched among comparisons, including calcium signaling, MapK signaling, focal adhesion, and phosphatidylinositol signaling. Together, our results indicate parallelism of the molecular basis of sea age in North American Atlantic Salmon across large-effect genes and molecular pathways despite population-specific patterns of polygenicity. These findings reveal roles for both contingency and repeated adaptation at the molecular level in the evolution of life history variation.
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Affiliation(s)
- Tony Kess
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Sarah J. Lehnert
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Paul Bentzen
- Department of BiologyDalhousie UniversityHalifaxNova ScotiaCanada
| | - Steven Duffy
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Amber Messmer
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - J. Brian Dempson
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Jason Newport
- Marine Environmental Research Infrastructure for Data Integration and Application NetworkHalifaxNova ScotiaCanada
| | | | - Martha J. Robertson
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Gerald Chaput
- Fisheries and Oceans CanadaGulf Fisheries CentreMonctonNew BrunswickCanada
| | - Cindy Breau
- Fisheries and Oceans CanadaGulf Fisheries CentreMonctonNew BrunswickCanada
| | - Julien April
- Ministère des Forêts de la Faune et des ParcsQuebecQuebecCanada
| | - Carole‐Anne Gillis
- Gespe'gewa'gi, Mi'gma'qi, ListugujGespe'gewa'gi Institute of Natural UnderstandingQuebecQuebecCanada
| | - Matthew Kent
- Centre for Integrative GeneticsNorwegian University of Life SciencesÅsNorway
| | - Cameron M. Nugent
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
| | - Ian R. Bradbury
- Northwest Atlantic Fisheries CentreFisheries and Oceans CanadaSt. John'sNewfoundland and LabradorCanada
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3
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García-Barrios G, Crespo-Herrera L, Cruz-Izquierdo S, Vitale P, Sandoval-Islas JS, Gerard GS, Aguilar-Rincón VH, Corona-Torres T, Crossa J, Pacheco-Gil RA. Genomic Prediction from Multi-Environment Trials of Wheat Breeding. Genes (Basel) 2024; 15:417. [PMID: 38674352 PMCID: PMC11049976 DOI: 10.3390/genes15040417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.
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Affiliation(s)
- Guillermo García-Barrios
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | - Serafín Cruz-Izquierdo
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Paolo Vitale
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | | | - Guillermo Sebastián Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | - Víctor Heber Aguilar-Rincón
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Tarsicio Corona-Torres
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
- Posgrado en Socioeconomía Estadística e Informática, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico
| | - Rosa Angela Pacheco-Gil
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
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4
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Richards TJ, McGuigan K, Aguirre JD, Humanes A, Bozec YM, Mumby PJ, Riginos C. Moving beyond heritability in the search for coral adaptive potential. GLOBAL CHANGE BIOLOGY 2023; 29:3869-3882. [PMID: 37310164 DOI: 10.1111/gcb.16719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 06/14/2023]
Abstract
Global environmental change is happening at unprecedented rates. Coral reefs are among the ecosystems most threatened by global change. For wild populations to persist, they must adapt. Knowledge shortfalls about corals' complex ecological and evolutionary dynamics, however, stymie predictions about potential adaptation to future conditions. Here, we review adaptation through the lens of quantitative genetics. We argue that coral adaptation studies can benefit greatly from "wild" quantitative genetic methods, where traits are studied in wild populations undergoing natural selection, genomic relationship matrices can replace breeding experiments, and analyses can be extended to examine genetic constraints among traits. In addition, individuals with advantageous genotypes for anticipated future conditions can be identified. Finally, genomic genotyping supports simultaneous consideration of how genetic diversity is arrayed across geographic and environmental distances, providing greater context for predictions of phenotypic evolution at a metapopulation scale.
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Affiliation(s)
- Thomas J Richards
- School of Biological Sciences, The University of Queensland, Queensland, St Lucia, Australia
| | - Katrina McGuigan
- School of Biological Sciences, The University of Queensland, Queensland, St Lucia, Australia
| | - J David Aguirre
- School of Natural Sciences, Massey University, Auckland, New Zealand
| | - Adriana Humanes
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Yves-Marie Bozec
- School of Biological Sciences, The University of Queensland, Queensland, St Lucia, Australia
| | - Peter J Mumby
- School of Biological Sciences, The University of Queensland, Queensland, St Lucia, Australia
| | - Cynthia Riginos
- School of Biological Sciences, The University of Queensland, Queensland, St Lucia, Australia
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5
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Lasky JR, Josephs EB, Morris GP. Genotype-environment associations to reveal the molecular basis of environmental adaptation. THE PLANT CELL 2023; 35:125-138. [PMID: 36005926 PMCID: PMC9806588 DOI: 10.1093/plcell/koac267] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/23/2022] [Indexed: 06/14/2023]
Abstract
A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.
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Affiliation(s)
- Jesse R Lasky
- Department of Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Emily B Josephs
- Department of Plant Biology; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan 48824, USA
| | - Geoffrey P Morris
- Department of Soil and Crop Sciences; Cell and Molecular Biology Program, Colorado State University, Fort Collins, Colorado 80526, USA
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6
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Abstract
We organized this special issue to highlight new work and review recent advances at the cutting edge of 'wild quantitative genomics'. In this editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.
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Affiliation(s)
- Susan E Johnston
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, Edinburgh EH9 3FL, UK
| | - Nancy Chen
- Department of Biology, University of Rochester, Rochester, 14627, NY, USA
| | - Emily B Josephs
- Department of Plant Biology and Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, 48824, MI, USA
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7
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Ashraf B, Hunter DC, Bérénos C, Ellis PA, Johnston SE, Pilkington JG, Pemberton JM, Slate J. Genomic prediction in the wild: A case study in Soay sheep. Mol Ecol 2022; 31:6541-6555. [PMID: 34719074 DOI: 10.1111/mec.16262] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/13/2021] [Accepted: 10/25/2021] [Indexed: 01/13/2023]
Abstract
Genomic prediction, the technique whereby an individual's genetic component of their phenotype is estimated from its genome, has revolutionised animal and plant breeding and medical genetics. However, despite being first introduced nearly two decades ago, it has hardly been adopted by the evolutionary genetics community studying wild organisms. Here, genomic prediction is performed on eight traits in a wild population of Soay sheep. The population has been the focus of a >30 year evolutionary ecology study and there is already considerable understanding of the genetic architecture of the focal Mendelian and quantitative traits. We show that the accuracy of genomic prediction is high for all traits, but especially those with loci of large effect segregating. Five different methods are compared, and the two methods that can accommodate zero-effect and large-effect loci in the same model tend to perform best. If the accuracy of genomic prediction is similar in other wild populations, then there is a real opportunity for pedigree-free molecular quantitative genetics research to be enabled in many more wild populations; currently the literature is dominated by studies that have required decades of field data collection to generate sufficiently deep pedigrees. Finally, some of the potential applications of genomic prediction in wild populations are discussed.
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Affiliation(s)
- Bilal Ashraf
- School of Biosciences, University of Sheffield, Sheffield, UK.,Department of Anthropology, Durham University, Durham, UK
| | - Darren C Hunter
- School of Biosciences, University of Sheffield, Sheffield, UK.,School of Biology, University of St Andrews, St Andrews, UK
| | - Camillo Bérénos
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Philip A Ellis
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Susan E Johnston
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Jill G Pilkington
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | | | - Jon Slate
- School of Biosciences, University of Sheffield, Sheffield, UK
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8
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Diwan D, Rashid MM, Vaishnav A. Current understanding of plant-microbe interaction through the lenses of multi-omics approaches and their benefits in sustainable agriculture. Microbiol Res 2022; 265:127180. [PMID: 36126490 DOI: 10.1016/j.micres.2022.127180] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 11/28/2022]
Abstract
The success of sustainable agricultural practices has now become heavily dependent on the interactions between crop plants and their associated microbiome. Continuous advancement in high throughput sequencing platforms, omics-based approaches, and gene editing technologies has remarkably accelerated this area of research. It has enabled us to characterize the interactions of plants with associated microbial communities more comprehensively and accurately. Furthermore, the genomic and post-genomic era has significantly refined our perspective toward the complex mechanisms involved in those interactions, opening new avenues for efficiently deploying the knowledge in developing sustainable agricultural practices. This review focuses on our fundamental understanding of plant-microbe interactions and the contribution of existing multi-omics approaches, including those under active development and their tremendous success in unraveling different aspects of the complex network between plant hosts and microbes. In addition, we have also discussed the importance of sustainable and eco-friendly agriculture and the associated outstanding challenges ahead.
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Affiliation(s)
- Deepti Diwan
- Washington University School of Medicine, Saint Louis, MO 63110, USA.
| | - Md Mahtab Rashid
- Department of Plant Pathology, Bihar Agricultural University, Sabour, Bhagalpur, Bihar 813210, India; Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Anukool Vaishnav
- Department of Biotechnology, GLA University, Mathura, Uttar Pradesh 281121, India; Department of Plant and Microbial Biology, University of Zürich, Zollikerstrasse 107, Zürich CH-8008, Switzerland; Plant-Soil Interaction Group, Agroscope (Reckenholz), Reckenholzstrasse 191, Zürich 8046, Switzerland
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9
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Hunter DC, Ashraf B, Bérénos C, Ellis PA, Johnston SE, Wilson AJ, Pilkington JG, Pemberton JM, Slate J. Using genomic prediction to detect microevolutionary change of a quantitative trait. Proc Biol Sci 2022; 289:20220330. [PMID: 35538786 PMCID: PMC9091855 DOI: 10.1098/rspb.2022.0330] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/12/2022] [Indexed: 12/31/2022] Open
Abstract
Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep (Ovis aries). Comparisons are made with a traditional pedigree-based approach. During the study period, adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Genomic and pedigree-based approaches gave largely consistent results. Thus, using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.
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Affiliation(s)
- D. C. Hunter
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
- School of Biology, University of St Andrews, St Andrews KY16 9ST, UK
| | - B. Ashraf
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
- Department of Anthropology, Durham University, Durham DH1 3LE, UK
| | - C. Bérénos
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - P. A. Ellis
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - S. E. Johnston
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - A. J. Wilson
- Centre of Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn TR10 9FE, UK
| | - J. G. Pilkington
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - J. M. Pemberton
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - J. Slate
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
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Sandoval-Castillo J, Beheregaray LB, Wellenreuther M. Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus). G3 (BETHESDA, MD.) 2022; 12:jkac015. [PMID: 35100370 PMCID: PMC8896003 DOI: 10.1093/g3journal/jkac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
Growth is one of the most important traits of an organism. For exploited species, this trait has ecological and evolutionary consequences as well as economical and conservation significance. Rapid changes in growth rate associated with anthropogenic stressors have been reported for several marine fishes, but little is known about the genetic basis of growth traits in teleosts. We used reduced genome representation data and genome-wide association approaches to identify growth-related genetic variation in the commercially, recreationally, and culturally important Australian snapper (Chrysophrys auratus, Sparidae). Based on 17,490 high-quality single-nucleotide polymorphisms and 363 individuals representing extreme growth phenotypes from 15,000 fish of the same age and reared under identical conditions in a sea pen, we identified 100 unique candidates that were annotated to 51 proteins. We documented a complex polygenic nature of growth in the species that included several loci with small effects and a few loci with larger effects. Overall heritability was high (75.7%), reflected in the high accuracy of the genomic prediction for the phenotype (small vs large). Although the single-nucleotide polymorphisms were distributed across the genome, most candidates (60%) clustered on chromosome 16, which also explains the largest proportion of heritability (16.4%). This study demonstrates that reduced genome representation single-nucleotide polymorphisms and the right bioinformatic tools provide a cost-efficient approach to identify growth-related loci and to describe genomic architectures of complex quantitative traits. Our results help to inform captive aquaculture breeding programs and are of relevance to monitor growth-related evolutionary shifts in wild populations in response to anthropogenic pressures.
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Affiliation(s)
- Jonathan Sandoval-Castillo
- Molecular Ecology Laboratory, College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Luciano B Beheregaray
- Molecular Ecology Laboratory, College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Maren Wellenreuther
- School of Biological Sciences, The New Zealand Institute for Plant and Food Research Limited, Nelson 7010, New Zealand
- Seafood Production Group, The School of Biological Sciences, University of Auckland, Auckland 1010, New Zealand
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