A GLABRA1 ortholog on LG A9 controls trichome number in the Japanese leafy vegetables Mizuna and Mibuna (Brassica rapa L. subsp. nipposinica L. H. Bailey): evidence from QTL analysis.
Yaichi KawakatsuHokuto NakayamaKaori KaminoyamaKaori IgarashiMasaki YasugiHiroshi KudohAtsushi J NaganoKentaro YanoNakao KuboSeisuke KimuraPublished in: Journal of plant research (2017)
Brassica rapa show a wide range of morphological variations. In particular, the leaf morphologies of the Japanese traditional leafy vegetables Mizuna and Mibuna (Brassica rapa L. subsp. nipposinica L. H. Bailey) are distinctly different, even though they are closely related cultivars that are easy to cross. In addition to the differences in the gross morphology of leaves, some cultivars of Mibuna (Kyo-nishiki) have many trichomes on its leaves, whereas Mizuna (Kyo-mizore) does not. To identify the genes responsible for the different number of trichomes, we performed a quantitative trait loci (QTL) analysis of Mizuna and Mibuna. To construct linkage maps for these cultivars, we used RNA-seq data to develop cleaved amplified polymorphic sequence (CAPS) markers. We also performed a restriction site-associated DNA sequencing (RAD-seq) analysis to detect single-nucleotide polymorphisms (SNPs). Two QTL analyses were performed in different years, and both analyses indicated that the largest effect was found on LG A9. Expression analyses showed that a gene homologous to GLABRA1 (GL1), a transcription factor implicated in trichome development in Arabidopsis thaliana, and the sequences 3'-flanking (downstream) of BrGL1, differed considerably between Mizuna (Kyo-mizore) and Mibuna (Kyo-nishiki). These results indicate that BrGL1 on LG A9 is one of the candidate genes responsible for the difference in trichome number between Mizuna and Mibuna. Detecting genes that are responsible for morphological variations allows us to better understand the breeding history of Mizuna and Mibuna.
Keyphrases
- genome wide
- genome wide analysis
- arabidopsis thaliana
- genome wide identification
- rna seq
- single cell
- transcription factor
- dna methylation
- dna damage
- copy number
- dna repair
- gene expression
- health risk
- big data
- amino acid
- human health
- oxidative stress
- machine learning
- electronic health record
- hepatitis c virus
- long non coding rna
- essential oil
- drinking water
- human immunodeficiency virus
- hiv testing