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Regulation of gene expression plays a crucial role in the development and adaptation of organisms to changing environments. A population-based procedure used to investigate the genetic regulation of gene expression is eQTL mapping. Typically, the age of the population used for eQTL mapping at the time of sampling is strictly controlled. This is necessary because the developmental process causes changes in gene expression, complicating the interpretation of eQTL mapping experiments. However, organisms can differ in their “developmental age”, even if they are of the same chronological age. As a result, eQTL patterns are affected by uncontrolled developmental variation in gene expression. The model organismC. elegansis particularly suited for studying the effect of developmental variation on eQTL mapping patterns. In a span of daysC. eleganstransitions from embryo through four larval stages to adult while undergoing massive changes to its transcriptome. Here we useC. elegansto investigate the effect of developmental age variation on eQTL patterns and an available normalization procedure. We used dynamical eQTL mapping, which includes developmental age as a cofactor, to separate the variation in development from genotypic variation and explain variation in gene expression levels. We compare classical single marker eQTL mapping and dynamical eQTL mapping using RNA-seq data of ∼200 multi-parental recombinant inbred lines ofC. elegans. The results show that many eQTLs are actually caused by developmental variation, that most trans-bands are associated with developmental age and that dynamical eQTL mapping detects additional eQTLs not found with classical eQTL mapping.

The quality of seeds contributes to plant performance, especially during germination and in the young seedling stage, and hence affects the economic value of seed crops. A seed’s innate quality is determined during seed development and the following seed maturation phase. It is tightly controlled by the genetic make-up of the mother plant and further shaped by the environmental conditions of the mother plant. The interaction between genotype and environment can result in substantial quantitative variation in seed traits like dormancy and viability.Making use of naturally occurring variation within theArabidopsis thalianagermplasm, we studied the interaction between seed production environments and the genetic architecture of mother plants on diverse seed quality traits. An Arabidopsis Bayreuth-0 x Shahdara recombinant inbred line (RIL) population was grown in four different seed production environments: high temperature, high light, low phosphate, and control conditions. The seeds harvested from the mother plants that were exposed to these environments from flowering until seed harvest were subsequently subjected to germination assays under standard and mild stress conditions (cold, heat, osmotic stress and added phytohormone ABA). Quantitative trait locus (QTL) analysis identified many environmental-sensitive QTLs (QTL x E) as well as several interactions between the maternal and germination environments. Variation in the number and position of the QTLs was largely determined by the germination conditions, however effects of the maternal environment were clearly present regarding the genomic location as well as significance of the individual QTLs.Together, our findings uncover the extensive environmental modulation of the genetic influence on seed performance and how this is shaped by the genetic make-up of the mother plant. Our data provides a systems-view of the complex genetic basis of genotype-by-environment interactions determining seed quality.

Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (RIL) population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments, i.e., on high phosphorus or low nitrogen. The obtained single-nucleotide polymorphisms (SNPs) were subsequently used to construct a genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.

Expression quantitative trait locus mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of expression quantitative trait locus data has been generated for this model plant; however, only a few causal expression quantitative trait locus genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal expression quantitative trait locus genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype quantitative trait loci to be used for expression quantitative trait loci by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize 16 out of 25 potential expression quantitative trait locus causal genes within the top 20% rank. Several new features are important in prioritizing causal expression quantitative trait locus genes, including the number of protein–protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate expression quantitative trait locus causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results show that ML methods are a better choice for nonlinear phenotypes and still comparable to Bayesian methods for linear phenotypes in the case of large effect QTNs. Furthermore, we find that ML methods are susceptible to confounding due to population structure and less sensitive to low linkage disequilibrium than linear parametric methods. Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.