Transcriptional regulation of gene expression in higher organisms is fundamental for numerous biological processes. These processes are mainly controlled by a special class of regulatory proteins, the transcription factors (TFs), and their combinatorial interplay. Various genetic programs, such as environmental adaptation, tissue development, or disease control, are governed by the binding of TFs to short DNA motifs, called transcription factor binding sites (TFBS), in the regulatory regions of their target genes. Single nucleotide polymorphisms (SNPs) located in promoter regions can alter TFBSs leading to a change in the binding affinity of TFs and, thus, affect gene expression. Such SNPs are referred to as regulatory SNPs (rSNPs). In recent years, rSNPs have come into the focus of research, and the underlying mechanisms resulting in a differential gene expression have been studied for many specific traits and diseases mainly in humans or model organisms, but also in agricultural species. However, these studies mostly concentrate on single regulatory variants and do not include systematic analyses. Thus, there is still a lack of such comprehensive analyses and genome-wide collections of rSNPs, and to date, only few tools and databases are available for livestock or crop species. In this work, I developed a pipeline for the detection of rSNPs and created the databases agReg-SNPdb and agReg-SNPdb-Plants, storing genome-wide collections of rSNPs and their predicted effects on TF binding for agricultural animal and plant species, respectively. agReg-SNPdb includes seven livestock and domestic species, namely cattle, pig, chicken, sheep, horse, goat, and dog and agReg-SNPdb-Plants includes 13 crop species and subspecies, namely African rice, Asian rice (with its subspecies Indica and Japonica), barley, bread wheat, durum wheat, grape, maize, rapeseed, sorghum, sunflower, tomato, and wild rice. Out of all species stored in agReg-SNPdb-Plants, rapeseed holds a special role. In contrast to the remaining species, where I used the data from Ensembl Plants as basis, in rapeseed, to date, there is no genome-wide collection of SNPs available. Therefore, I used a previously published data set based on different resequenced Brassica napus L. cultivars for the identification of rSNPs in agReg-SNPdb-Plants. Based on this data set, I investigated the regulatory mechanisms in two cultivars, namely Zhongshuang11 (ZS11), a so-called double-low accession with low content of erucic acid and glucosinolate, which is characterized by high oil content, and Zhongyou821 (ZY821), a so-called double-high accession with high content of erucic acid and glucosinolate, which is characterized by low oil content. In this way, I demonstrate the application of rSNPs together with multi-omics data to perform a systematic analysis of the complex interplay between rSNPs, TFs, and differentially expressed genes (DEGs) in four tissues (flower, leaf, stem, and root) which are underlying the oil content and -quality in rapeseed. viii Es hat mir großen Spaß ...