Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binning method, 1059 bin-markers were identified, physically anchored on the genome. The total length of the RNA-seq genetic map spans 471.70 centimorgans (cM) with an average marker distance of 0.45 cM and a maximum marker distance of 4.81 cM. This high resolution genotyping revealed new recombination breakpoints in the population. To highlight the advantages of such high-density map, we compared it to two publicly available genetic maps for the same population, comprising 69 PCR-based markers and 497 gene expression markers derived from microarray data, respectively. In this study, we show that SNP markers can effectively be derived from RNA-seq data. The new RNA-seq map closes many existing gaps in marker coverage, saturating the previously available genetic maps. Quantitative trait locus (QTL) analysis for published phenotypes using the available genetic maps showed increased QTL mapping resolution and reduced QTL confidence interval using the RNA-seq map. The new high-density map is a valuable resource that facilitates the identification of candidate genes and map-based cloning approaches.

Environmental tuning of the genetic control of seed performance: a systems genetics approach, 214 pages.PhD thesis, Wageningen University, Wageningen, the Netherlands (2018) With references, with summary in English

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 the Arabidopsis thaliana germplasm, 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.