Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

Autoři: Robert L. Baker aff001;  Wen Fung Leong aff002;  Marcus T. Brock aff003;  Matthew J. Rubin aff003;  R. J. Cody Markelz aff004;  Stephen Welch aff002;  Julin N. Maloof aff004;  Cynthia Weinig aff003
Působiště autorů: Department of Biology, Miami University, Oxford, Ohio, United States of America aff001;  Department of Agronomy, Kansas State University, Manhattan, Kansas, United States of America aff002;  Department of Botany, University of Wyoming, Laramie, Wyoming, United States of America aff003;  Department of Plant Biology, University of California Davis, Davis, California, United States of America aff004
Vyšlo v časopise: Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development. PLoS Genet 15(9): e1008367. doi:10.1371/journal.pgen.1008367
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008367


Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find that the shape of growth curves is relatively plastic across environments compared to final height, which is comparatively robust. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

Klíčová slova:

Biology and life sciences – Genetics – Genetic loci – Quantitative trait loci – Gene expression – Gene regulation – Gene identification and analysis – Genetic networks – Phenotypes – Plant genetics – Molecular biology – Molecular biology techniques – Gene mapping – Plant science – Computer and information sciences – Network analysis – Research and analysis methods


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