bioRxivPlant Biology
Validation of a novel associative transcriptomics pipeline in Brassica oleracea: Identifying candidates for vernalisation response
S. M. Woodhouse, Z. He, H. Woolfenden, B. Steuernagel, W. ` Haerty, I. Bancroft, J. ` Irwin, R. J. Morris, R. Wells
Abstract
Associative transcriptomics has been used extensively in Brassica napus to enable the rapid identification of markers correlated with traits of interest. However, within the important vegetable crop species, Brassica oleracea, the use of associative transcriptomics has been limited due to a lack of fixed genetic resources and the difficulties in generating material due to self-incompatibility. Within Brassica vegetables, the harvestable product can be vegetative or floral tissues and therefore synchronisation of the floral transition is an important goal for growers and breeders. Vernalisation is known to be a key determinant of the floral transition, yet how different vernalisation treatments influence flowering in B. oleracea is not well understood. Here, we present results from phenotyping a diverse set of 69 B. oleracea accessions for heading and flowering traits under different environmental conditions. We developed a new associative transcriptomics pipeline, and inferred and validated a population structure, for the phenotyped accessions. A genome-wide association study identified miR172D as a candidate for the vernalisation response. Gene expression marker association identified variation in expression of BoFLC.C2 as a further candidate for vernalisation response. This study provides insights into the genetic basis of vernalisation response in B. oleracea through associative transcriptomics and confirms its characterisation as a complex G x E trait. Candidate leads were identified in miR172D and BoFLC.C2. These results could facilitate marker-based breeding efforts to produce B. oleracea lines with more synchronous heading dates, potentially leading to improved yields.
- organism: Arabidopsis
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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