Integrating Chromosome 3D Structure And Association Studies To Unravel The Genetic Regulation Of Plant Complex Traits

Analyzing flowering traits in flowering plants is important not only for clearly understanding the flowering mechanism and biological function but also for improving economic crops and rotation systems. Since the last century, researchers have used various experimental or theoretical methods such as gene mutation, knock down and GWAS to study how a single gene or SNP site participates in regulating the flowering time of Arabidopsis thaliana time. However, these methods have always been constrained to the regulatory effects of single sites.In 2000, the Arabidopsis thaliana genome draft was published; since then, functional genome studies have enjoyed rapid development. Researchers combined SNPs two by two to study the effect flowering time in two dimensions. Due to gene function uncertainties, gene combination can generate a large number of groups, thus making it inaccessible for current experiments and theoretical methods and stagnating multigene combination studies. Therefore, the central problem is how to filter site combination.Current studies of chromatin spatial organization have demonstrated that adjacent genomic sites tend to have relatively close genetics and biochemical relationships. Chromatin conformation capture methods have revealed that spatially adjacent sites tend to have strong coexpression and that their biological functions show interactions or synergistic effects. Our association analysis was based on Hi-C data combined with SNP tagging and linkage disequilibrium to build high-density haplotype blocks coupled with four different phenotypes. The results are shown below:1. Using 29,083 haplotype blocks, we identified four different phenotype-related sites by Phe WAS. The Phe WAS results compensated effectively for GWAS when making a comparison between Phe WAS and GWAS.2. Using the Hi-C data features, we identified four different phenotype-related combined loci through Phe WAS.3. With the above result, we identified a combined site that had associated effects, thus explaining the reason for a combination effect at the metabolic network level.