Refining bulk segregant analyses: ontology-mediated discovery of flowering time genes in Brassica oleracea

Abstract

Abstract Background Bulk segregant analysis (BSA) can help identify quantitative trait loci (QTLs), but this may result in substantial bycatch of functionally irrelevant genes. Results Here we develop a Gene Ontology-mediated approach to zoom in on specific genes located inside QTLs identified by BSA as implicated in a continuous trait. We apply this to a novel experimental system: flowering time in the giant woody Jersey kale, which we phenotyped in four bulks of flowering onset. Our inferred QTLs yielded tens of thousands of candidate genes. We reduced this by two orders of magnitude by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. A pathway enrichment test then led to the circadian rhythm pathway. The genes that enriched this pathway are attested from previous research as regulating flowering time. Within that pathway, the genes CCA1, FT, and TSF were identified as having functionally significant variation compared to Arabidopsis. We validated and confirmed our ontology-mediated results through genome sequencing and homology-based SNP analysis. However, our ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery. Conclusions Our method is potentially applicable to the study of other complex traits and we therefore make our workflows available as open-source code and a reusable Docker container.

Keywords

• Bulk segregant analysis
• Quantitative trait locus
• Gene Ontology
• Pathway analysis
• Enrichment analysis
• SNP effects