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Chu TT, Kristensen PS, Jensen J. Simulation of functional additive and non-additive genetic effects using statistical estimates from quantitative genetic models. Heredity (Edinb) 2024; 133:33-42. [PMID: 38822133 PMCID: PMC11222558 DOI: 10.1038/s41437-024-00690-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 06/02/2024] Open
Abstract
Stochastic simulation software is commonly used to aid breeders designing cost-effective breeding programs and to validate statistical models used in genetic evaluation. An essential feature of the software is the ability to simulate populations with desired genetic and non-genetic parameters. However, this feature often fails when non-additive effects due to dominance or epistasis are modeled, as the desired properties of simulated populations are estimated from classical quantitative genetic statistical models formulated at the population level. The software simulates underlying functional effects for genotypic values at the individual level, which are not necessarily the same as effects from statistical models in which dominance and epistasis are included. This paper provides the theoretical basis and mathematical formulas for the transformation between functional and statistical effects in such simulations. The transformation is demonstrated with two statistical models analyzing individual phenotypes in a single population (common in animal breeding) and plot phenotypes of three-way hybrids involving two inbred populations (observed in some crop breeding programs). We also describe different methods for the simulation of functional effects for additive genetics, dominance, and epistasis to achieve the desired levels of variance components in classical statistical models used in quantitative genetics.
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Affiliation(s)
- Thinh Tuan Chu
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- Vietnam National University of Agriculture, Faculty of Animal Science, Trâu Quỳ, Gia Lâm, Hanoi, Vietnam.
| | | | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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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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Madsen MD, Kristensen PS, Mahmood K, Thach T, Mohlfeld M, Orabi J, Sarup P, Jahoor A, Hovmøller MS, Rodriguez-Algaba J, Jensen J. Scald resistance in hybrid rye ( Secale cereale): genomic prediction and GWAS. FRONTIERS IN PLANT SCIENCE 2024; 15:1306591. [PMID: 38304738 PMCID: PMC10830712 DOI: 10.3389/fpls.2024.1306591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024]
Abstract
Rye (Secale cereale L.) is an important cereal crop used for food, beverages, and feed, especially in North-Eastern Europe. While rye is generally more tolerant to biotic and abiotic stresses than other cereals, it still can be infected by several diseases, including scald caused by Rhynchosporium secalis. The aims of this study were to investigate the genetic architecture of scald resistance, to identify genetic markers associated with scald resistance, which could be used in breeding of hybrid rye and to develop a model for genomic prediction for scald resistance. Four datasets with records of scald resistance on a population of 251 hybrid winter rye lines grown in 2 years and at 3 locations were used for this study. Four genomic models were used to obtain variance components and heritabilities of scald resistance. All genomic models included additive genetic effects of the parental components of the hybrids and three of the models included additive-by-additive epistasis and/or dominance effects. All models showed moderate to high broad sense heritabilities in the range of 0.31 (SE 0.05) to 0.76 (0.02). The model without non-additive genetic effects and the model with dominance effects had moderate narrow sense heritabilities ranging from 0.24 (0.06) to 0.55 (0.08). None of the models detected significant non-additive genomic variances, likely due to a limited data size. A genome wide association study was conducted to identify markers associated with scald resistance in hybrid winter rye. In three datasets, the study identified a total of twelve markers as being significantly associated with scald resistance. Only one marker was associated with a major quantitative trait locus (QTL) influencing scald resistance. This marker explained 11-12% of the phenotypic variance in two locations. Evidence of genotype-by-environment interactions was found for scald resistance between one location and the other two locations, which suggested that scald resistance was influenced by different QTLs in different environments. Based on the results of the genomic prediction models and GWAS, scald resistance seems to be a quantitative trait controlled by many minor QTL and one major QTL, and to be influenced by genotype-by-environment interactions.
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Affiliation(s)
- Mette Dam Madsen
- Centre for Quantitative Genetic and Genomics, Faculty of Science and Technology, Arhus University, Aarhus, Denmark
| | - Peter Skov Kristensen
- Centre for Quantitative Genetic and Genomics, Faculty of Science and Technology, Arhus University, Aarhus, Denmark
| | - Khalid Mahmood
- Research and Development Department, Nordic Seed A/S, Dyngby, Denmark
| | - Tine Thach
- Department of Agroecology, Faculty of Technical Sciences, Aarhus University, Slagelse, Denmark
| | | | - Jihad Orabi
- Research and Development Department, Nordic Seed A/S, Dyngby, Denmark
| | - Pernille Sarup
- Research and Development Department, Nordic Seed A/S, Dyngby, Denmark
| | - Ahmed Jahoor
- Research and Development Department, Nordic Seed A/S, Dyngby, Denmark
| | | | - Julian Rodriguez-Algaba
- Department of Agroecology, Faculty of Technical Sciences, Aarhus University, Slagelse, Denmark
| | - Just Jensen
- Centre for Quantitative Genetic and Genomics, Faculty of Science and Technology, Arhus University, Aarhus, Denmark
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