Dataset Information

Genetic and genomic resources to study natural variation in Brassica rapa.

ABSTRACT: The globally important crop Brassica rapa, a close relative of Arabidopsis, is an excellent system for modeling our current knowledge of plant growth on a morphologically diverse crop. The long history of B. rapa domestication across Asia and Europe provides a unique collection of locally adapted varieties that span large climatic regions with various abiotic and biotic stress-tolerance traits. This diverse gene pool provides a rich source of targets with the potential for manipulation toward the enhancement of productivity of crops both within and outside the Brassicaceae. To expand the genetic resources available to study natural variation in B. rapa, we constructed an Advanced Intercross Recombinant Inbred Line (AI-RIL) population using B. rapa subsp. trilocularis (Yellow Sarson) R500 and the B. rapa subsp. parachinensis (Cai Xin) variety L58. Our current understanding of genomic structure variation across crops suggests that a single reference genome is insufficient for capturing the genetic diversity within a species. To complement this AI-RIL population and current and future B. rapa genomic resources, we generated a de novo genome assembly of the B. rapa subsp. trilocularis (Yellow Sarson) variety R500, the maternal parent of the AI-RIL population. The genetic map for the R500 x L58 population generated using this de novo genome was used to map Quantitative Trait Loci (QTL) for seed coat color and revealed the improved mapping resolution afforded by this new assembly.

SUBMITTER: Lou P

PROVIDER: S-EPMC7755128 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Genetic and genomic resources to study natural variation in Brassica rapa.

Lou Ping P Woody Scott S Greenham Kathleen K VanBuren Robert R Colle Marivi M Edger Patrick P PP Sartor Ryan R Zheng Yakun Y Levendoski Nathan N Lim Jan J So Calvin C Stoveken Brian B Woody Timothy T Zhao Jianjun J Shen Shuxing S Amasino Richard M RM McClung C Robertson CR

Plant direct 20201222 12

The globally important crop Brassica rapa, a close relative of Arabidopsis, is an excellent system for modeling our current knowledge of plant growth on a morphologically diverse crop. The long history of B. rapa domestication across Asia and Europe provides a unique collection of locally adapted varieties that span large climatic regions with various abiotic and biotic stress-tolerance traits. This diverse gene pool provides a rich source of targets with the potential for manipula ...[more]

PMID: 33364543

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Project description: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.

Project description:BackgroundAssociation mapping is a statistical approach combining phenotypic traits and genetic diversity in natural populations with the goal of correlating the variation present at phenotypic and allelic levels. It is essential to separate the true effect of genetic variation from other confounding factors, such as adaptation to different uses and geographical locations. The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration.Methodology/principal findingsA core collection of 168 Brassica rapa accessions of different morphotypes and origins was explored to find genetic association between markers and metabolites: tocopherols, carotenoids, chlorophylls and folate. A widely used linear model with modifications to account for population structure and kinship was followed for association mapping. In addition, a machine learning algorithm called Random Forest (RF) was used as a comparison. Comparison of results across methods resulted in the selection of a set of significant markers as promising candidates for further work. This set of markers associated to the metabolites can potentially be applied for the selection of genotypes with elevated levels of these metabolites.Conclusions/significanceThe incorporation of the kinship correction into the association model did not reduce the number of significantly associated markers. However incorporation of the STRUCTURE correction (Q matrix) in the linear regression model greatly reduced the number of significantly associated markers. Additionally, our results demonstrate that RF is an interesting complementary method with added value in association studies in plants, which is illustrated by the overlap in markers identified using RF and a linear mixed model with correction for kinship and population structure. Several markers that were selected in RF and in the models with correction for kinship, but not for population structure, were also identified as QTLs in two bi-parental DH populations.

Dataset Information

Genetic and genomic resources to study natural variation in Brassica rapa.

Publications

Genetic and genomic resources to study natural variation in <i>Brassica rapa</i>.

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