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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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Kristensen PS, Sarup P, Fé D, Orabi J, Snell P, Ripa L, Mohlfeld M, Chu TT, Herrström J, Jahoor A, Jensen J. Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet. FRONTIERS IN PLANT SCIENCE 2023; 14:1193433. [PMID: 38162304 PMCID: PMC10756082 DOI: 10.3389/fpls.2023.1193433] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/16/2023] [Indexed: 01/03/2024]
Abstract
Genomic models for prediction of additive and non-additive effects within and across different heterotic groups are lacking for breeding of hybrid crops. In this study, genomic prediction models accounting for incomplete inbreeding in parental lines from two different heterotic groups were developed and evaluated. The models can be used for prediction of general combining ability (GCA) of parental lines from each heterotic group as well as specific combining ability (SCA) of all realized and potential crosses. Here, GCA was estimated as the sum of additive genetic effects and within-group epistasis due to high degree of inbreeding in parental lines. SCA was estimated as the sum of across-group epistasis and dominance effects. Three models were compared. In model 1, it was assumed that each hybrid was produced from two completely inbred parental lines. Model 1 was extended to include three-way hybrids from parental lines with arbitrary levels of inbreeding: In model 2, parents of the three-way hybrids could have any levels of inbreeding, while the grandparents of the maternal parent were assumed completely inbred. In model 3, all parental components could have any levels of inbreeding. Data from commercial breeding programs for hybrid rye and sugar beet was used to evaluate the models. The traits grain yield and root yield were analyzed for rye and sugar beet, respectively. Additive genetic variances were larger than epistatic and dominance variances. The models' predictive abilities for total genetic value, for GCA of each parental line and for SCA were evaluated based on different cross-validation strategies. Predictive abilities were highest for total genetic values and lowest for SCA. Predictive abilities for SCA and for GCA of maternal lines were higher for model 2 and model 3 than for model 1. The implementation of the genomic prediction models in hybrid breeding programs can potentially lead to increased genetic gain in two different ways: I) by facilitating the selection of crossing parents with high GCA within heterotic groups and II) by prediction of SCA of all realized and potential combinations of parental lines to produce hybrids with high total genetic values.
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Affiliation(s)
| | - Pernille Sarup
- Research and Development, Nordic Seed A/S, Odder, Denmark
| | - Dario Fé
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Jihad Orabi
- Research and Development, Nordic Seed A/S, Odder, Denmark
| | - Per Snell
- Research and Development, DLF Beet Seed AB, Landskrona, Sweden
| | - Linda Ripa
- Research and Development, DLF Beet Seed AB, Landskrona, Sweden
| | | | - Thinh Tuan Chu
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | | | - Ahmed Jahoor
- Research and Development, Nordic Seed A/S, Odder, Denmark
- Breeding, Nordic Seed Germany GmbH, Nienstädt, Germany
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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Beugnot A, Mary-Huard T, Bauland C, Combes V, Madur D, Lagardère B, Palaffre C, Charcosset A, Moreau L, Fievet JB. Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:219. [PMID: 37816986 PMCID: PMC10564676 DOI: 10.1007/s00122-023-04431-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/25/2023] [Indexed: 10/12/2023]
Abstract
KEY MESSAGE An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.
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Affiliation(s)
- Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Valerie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | | | | | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Julie B Fievet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France.
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DoVale JC, Carvalho HF, Sabadin F, Fritsche-Neto R. Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4523-4539. [PMID: 36261658 DOI: 10.1007/s00122-022-04236-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
In genomic recurrent selection, the more markers, the better because they buffer the linkage disequilibrium losses caused by recombination over cycles, and consequently, provide higher responses to selection. Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, studies that focus on long selective breeding cycles generally do not consider GS models with the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models [additive, additive-dominant, general (GCA), and this plus specific combining ability (GCA + SCA)]. Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or GCA + SCA for reciprocal.
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Affiliation(s)
- Júlio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, CE, Brazil.
| | | | - Felipe Sabadin
- Virginia Tech: Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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