ArticleFrontiers in plant science2018
Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Persistent and transient QTLs underlying growth trajectory of plant height in sorghum.Journal of experimental botany · 2026Article
- An omnigenic interactome model to chart the genetic architecture of individual plants.Horticulture research · 2026Article
- Genetic dynamics drive maize growth and breeding.Genome biology · 2026Article
- MAGIC populations: a next-generation framework for dissecting complex quantitative traits and accelerating molecular breeding in crops.Frontiers in plant science · 2026Review
- Comparison of linkage and association mapping in MAGIC lines identifies AtMTP3 as a new gene controlling natural variation in leaf zinc concentration in Arabidopsis.Journal of experimental botany · 2025Article
- Genetic Foundation of Leaf Senescence: Insights from Natural and Cultivated Plant Diversity.Plants (Basel, Switzerland) · 2024Review
- An eight-founder wheat MAGIC population allows fine-mapping of flowering time loci and provides novel insights into the genetic control of flowering time.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Natural variation in the prolyl 4-hydroxylase gene PtoP4H9 contributes to perennial stem growth in Populus.The Plant cell · 2023Article
- Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat.Plant biotechnology journal · 2023Article
- Dissecting the genetic basis of drought responses in common bean using natural variation.Frontiers in plant science · 2023Article
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
- Comparative Phenotyping of Two Commonly UsedPlants (Basel, Switzerland) · 2022Article
- The Genetic Architecture of Juvenile Growth Traits in the ConiferFrontiers in plant science · 2022Article
- Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits.Frontiers in plant science · 2022Article
- FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.STAR protocols · 2021Article
- Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat.Genes · 2021Article
- Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence.PLoS genetics · 2021Article
- Genetic dissection of growth trajectories in forest trees: From FunMap to FunGraph.Forestry research · 2021Review
- Phenology and related traits for wheat adaptation.Heredity · 2020Review
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9 authors.
Funding
Abstract
In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.
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