Variation in the flowering time orthologs BrFLC and BrSOC1 in a natural population of Brassica rapa
Published Web Locationhttps://doi.org/10.7717/peerj.1339
Understanding the genetic basis of natural phenotypic variation is of great importance, particularly since selection can act on this variation to cause evolution. We examined expression and allelic variation in candidate flowering time loci in Brassica rapa plants derived from a natural population and showing a broad range in the timing of first flowering. The loci of interest were orthologs of the Arabidopsis genes FLC and SOC1 (BrFLC and BrSOC1, respectively), which in Arabidopsis play a central role in the flowering time regulatory network, with FLC repressing and SOC1 promoting flowering. In B. rapa, there are four copies of FLC and three of SOC1. Plants were grown in controlled conditions in the lab. Comparisons were made between plants that flowered the earliest and latest, with the difference in average flowering time between these groups ∼30 days. As expected, we found that total expression of BrSOC1 paralogs was significantly greater in early than in late flowering plants. Paralog-specific primers showed that expression was greater in early flowering plants in the BrSOC1 paralogs Br004928, Br00393 and Br009324, although the difference was not significant in Br009324. Thus expression of at least 2 of the 3 BrSOC1 orthologs is consistent with their predicted role in flowering time in this natural population. Sequences of the promoter regions of the BrSOC1 orthologs were variable, but there was no association between allelic variation at these loci and flowering time variation. For the BrFLC orthologs, expression varied over time, but did not differ between the early and late flowering plants. The coding regions, promoter regions and introns of these genes were generally invariant. Thus the BrFLC orthologs do not appear to influence flowering time in this population. Overall, the results suggest that even for a trait like flowering time that is controlled by a very well described genetic regulatory network, understanding the underlying genetic basis of natural variation in such a quantitative trait is challenging.