Combined BSA-Seq Based Mapping and RNA-Seq Profiling Reveal Candidate Genes Associated with Plant Architecture in Brassica napus

Ye, Shenhua; Yan, Lei; Ma, Xiaowei; Chen, Yanping; Wu, Lumei; Ma, Tiantian; Zhao, Lun; Yi, Bin; Ma, Chaozhi; Tu, Jinxing; Fu, Tingdong; Wen, Jing

doi:10.3390/ijms23052472

Cited by 25 publications

(16 citation statements)

References 48 publications

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“…Moreover, integrating genetic and transcriptomic analysis in potato has fine mapped the Ro locus for tuber shape ( Fan et al, 2022 ). Combining BSA-seq and RNA-seq, a WOX gene associated with plant architecture in rapeseed was identified for the compact phenotype ( Ye et al, 2022 ).…”

Section: Integration Of Genomics and Transcriptomicsmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

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Section: Integration Of Genomics and Transcriptomicsmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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“…Integration of BSA-seq and RNA-seq is an effective strategy for gene fine-mapping [ 32 ]. To determine the candidate genes associated with fasciated ears, we performed an association analysis by combining the BSA-seq and transcriptomic data.…”

Section: Discussionmentioning

confidence: 99%

Integrating BSA-Seq with RNA-Seq Reveals a Novel Fasciated Ear5 Mutant in Maize

Zhou

et al. 2023

IJMS

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Increasing grain yield is required to meet the rapidly expanding demands for food, feed, and fuel. Inflorescence meristems are central to plant growth and development. However, the question concerning whether inflorescence development can be regulated to improve grain yield remains unclear. Here, we describe a naturally occurring single recessive mutation called fea5 that can increase grain yield in maize. Using bulk segregant analysis sequencing (BSA-seq), the candidate region was initially mapped to a large region on chromosome 4 (4.68 Mb–11.26 Mb). Transcriptome sequencing (RNA-seq) revealed a total of 1246 differentially expressed genes (DEGs), of which 835 were up-regulated and 411 were down-regulated. Further analysis revealed the enrichment of DEGs in phytohormone signal transduction. Consistently, phytohormone profiling indicated that auxin (IAA), jasmonic acid (JA), ethylene (ETH), and cytokinin (CK) levels increased significantly, whereas the gibberellin (GA) level decreased significantly in fea5. By integrating BSA-seq with RNA-seq, we identified Zm00001d048841 as the most likely candidate gene. Our results provide valuable insight into this new germplasm resource and the molecular mechanism underlying fasciated ears that produce a higher kernel row number in maize.

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“…Combining genetic data with prospective phenotyping technologies may offer information on complicated features to help improve crops [ 54 ]. The combination of BSA-seq and RNA-seq allowed the identification of the candidate for the agronomic trait [ 24 , 28 , 29 , 55 ]. This approach enhanced the accurate identification of gene candidates for the grain number in rice [ 26 ], where BSA-Seq alone would identify only the candidate interval.…”

Section: Discussionmentioning

confidence: 99%

“…New strategies which combine traditional methods and emerging technologies such as high-throughput whole-genome sequencing (NGS), alternative sequencing analysis, mapping by sequencing, bulked segregants analysis (BSA), RNA-sequencing (RNA-seq), and genome-wide association study (GWAS), have accelerated the identification of candidate genes for complex characters, and several studies have been successfully conducted to identify a new gene [ 2 , 24 , 25 , 26 , 27 ]. BSA-Seq and RNA profiling were coupled to reveal the pathway and genes associated with the heading type in Chinese cabbage [ 28 ], chilling tolerance in rice [ 24 ], and plant architecture in Brassica napus [ 29 ]. Likewise, significant gene candidates have been identified by combining sequencing and a metabolome analysis in albino jackfruit seedlings [ 30 ] and anthocyanin in cucumber fruit skin [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Mackon

et al. 2022

Biomolecules

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Rice grain yield is a complex and highly variable quantitative trait consisting of several key components, including the grain weight, the effective panicles per unit area, and the grain number per panicle (GNPP). The GNPP is a significant contributor to grain yield controlled by multiple genes (QTL) and is crucial for improvement. Attempts have been made to find genes for this trait, which has always been a challenging and arduous task through conventional methods. We combined a BSA analysis, RNA profiling, and a metabolome analysis in the present study to identify new candidate genes involved in the GNPP. The F2 population from crossing R4233 (high GNPP) and Ce679 (low GNPP) revealed a frequency distribution fitting two segregated genes. Three pools, including low, middle, and high GNPP, were constructed and a BSA analysis revealed six candidate regions spanning 5.38 Mb, containing 739 annotated genes. Further, a conjunctive analysis of BSA-Seq and RNA-Seq showed 31 differentially expressed genes (DEGs) in the candidate intervals. Subsequently, a metabolome analysis showed 1024 metabolites, with 71 significantly enriched, including 44 up and 27 downregulated in Ce679 vs. R4233. A KEGG enrichment analysis of these 31 DEGs and 71 differentially enriched metabolites (DEMs) showed two genes, Os12g0102100 and Os01g0580500, significantly enriched in the metabolic pathways’ biosynthesis of secondary metabolites, cysteine and methionine metabolism, and fatty acid biosynthesis. Os12g0102100, which encodes for the alcohol dehydrogenase superfamily and a zinc-containing protein, is a novel gene whose contribution to the GNPP is not yet elucidated. This gene coding for mitochondrial trans-2-enoyl-CoA reductase is involved in the biosynthesis of myristic acid, also known as tetradecanoic acid. The Os01g0580500 coding for the enzyme 1-aminoclopropane-1-carboxylate oxidase (OsACO7) is responsible for the final step of the ethylene biosynthesis pathway through the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) into ethylene. Unlike Os12g0102100, this gene was significantly upregulated in R4233, downregulated in Ce679, and significantly enriched in two of the three metabolite pathways. This result pointed out that these two genes are responsible for the difference in the GNPP in the two cultivars, which has never been identified. Further validation studies may disclose the physiological mechanisms through which they regulate the GNPP in rice.

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Combined BSA-Seq Based Mapping and RNA-Seq Profiling Reveal Candidate Genes Associated with Plant Architecture in Brassica napus

Cited by 25 publications

References 48 publications

Integration of multi-omics technologies for crop improvement: Status and prospects

Integration of multi-omics technologies for crop improvement: Status and prospects

Integrating BSA-Seq with RNA-Seq Reveals a Novel Fasciated Ear5 Mutant in Maize

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

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