ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2020
Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.PLoS genetics · 2025Pooled it
- Genetic dissection of grain yield and correlated proxy traits under suboptimal conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Deciphering the genetic basis of agronomic, yield, and nutritional traits in rice (Oryza sativa L.) using a saturated GBS-based SNP linkage map.Scientific reports · 2024Article
- Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield stability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Article
- Modeling QTL-by-environment interactions for multi-parent populations.Frontiers in plant science · 2024Article
- Introgression of a Danbaekkong high-protein allele across different genetic backgrounds in soybean.Frontiers in plant science · 2023Article
- Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci.G3 (Bethesda, Md.) · 2022Article
- Developing Novel Rice Genotypes Harboring Specific QTL Alleles Associated with High Grain Yield under Water Shortage Stress.Plants (Basel, Switzerland) · 2021Article
- Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.Heredity · 2020Review
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3 authors.
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Abstract
key messageMulti-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments. Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values 'averaged' across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.
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