Full epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction

Carré, Clément; Carluer, Jean-Baptiste; Chaux, Christian; Roche, Nicolás; Mas, André; Krouk, Gabriel

doi:10.1101/2022.07.20.500572

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Epistasis is pervasive and has been recognized as a key aspect of plant genetic architecture (Brachi et al, 2011;Korte and Farlow, 2013;Doust et al, 2014;Mackay, 2014). Recent studies have demonstrated that identifying epistatic interactions in diverse plant germplasm resources can retrieve a large proportion of the "missing heritability", thereby improving phenotypic prediction accuracy and providing a comprehensive genetic basis for complex traits (Maurer et al, 2015;Luo et al, 2017;Carréet al, 2022). With the rapid population size and marker density increase, GWAS faces statistical and computational challenges in detecting robust epistatic interactions.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Zhang

Jiang

et al. 2023

Front. Plant Sci.

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IntroductionSaline-alkali stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve rice saline-alkali tolerance at the germination stage. MethodsTo understand the genetic basis of saline-alkali tolerance and facilitate breeding efforts for developing saline-alkali tolerant rice varieties, the genetic basis of rice saline-alkali tolerance was dissected by phenotyping seven germination-related traits of 736 diverse rice accessions under the saline-alkali stress and control conditions using genome-wide association and epistasis analysis (GWAES).ResultsTotally, 165 main-effect quantitative trait nucleotides (QTNs) and 124 additional epistatic QTNs were identified as significantly associated with saline-alkali tolerance, which explained a significant portion of the total phenotypic variation of the saline-alkali tolerance traits in the 736 rice accessions. Most of these QTNs were located in genomic regions either harboring saline-alkali tolerance QTNs or known genes for saline-alkali tolerance reported previously. Epistasis as an important genetic basis of rice saline-alkali tolerance was validated by genomic best linear unbiased prediction in which inclusion of both main-effect and epistatic QTNs showed a consistently better prediction accuracy than either main-effect or epistatic QTNs alone. Candidate genes for two pairs of important epistatic QTNs were suggested based on combined evidence from the high-resolution mapping plus their reported molecular functions. The first pair included a glycosyltransferase gene LOC_Os02g51900 (UGT85E1) and an E3 ligase gene LOC_Os04g01490 (OsSIRP4), while the second pair comprised an ethylene-responsive transcriptional factor, AP59 (LOC_Os02g43790), and a Bcl-2-associated athanogene gene, OsBAG1 (LOC_Os09g35630) for salt tolerance. Detailed haplotype analyses at both gene promoter and CDS regions of these candidate genes for important QTNs identified favorable haplotype combinations with large effects on saline-alkali tolerance, which can be used to improve rice saline-alkali tolerance by selective introgression. DiscussionOur findings provided saline-alkali tolerant germplasm resources and valuable genetic information to be used in future functional genomic and breeding efforts of rice saline-alkali tolerance at the germination stage.

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Section: Introductionmentioning

confidence: 99%

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Zhang

Jiang

et al. 2023

Front. Plant Sci.

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Predicting Gene Regulatory Interactions Using Natural Genetic Variation

John,

Grimm,

Korte

2023

Methods in Molecular Biology

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Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

Larue,

Rouan,

Pot

et al. 2024

Front. Plant Sci.

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IntroductionPredicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.MethodsIn this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits.ResultsThe results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used.DiscussionThese results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

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Full epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction

Cited by 3 publications

References 24 publications

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Predicting Gene Regulatory Interactions Using Natural Genetic Variation

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

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