DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits

Zhao, Li; Chen, Xiaoxuan; Shi, Shaoqiang; Zhang, Hongwei; Wang, Xi; Chen, Hong; Li, Weifu; Lin, Li

doi:10.1016/j.molp.2022.08.004

Cited by 16 publications

(11 citation statements)

References 41 publications

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“…The ED algorithm, also called MMAPPR, calculates the frequency distance of each mutant between different bulks, and uses the distance difference to reflect the linkage strength between marker and target interval [ 64 ]. DeepBSA software was used to calculate the Δ(SNP-index) of each mutation site and evaluate ED between mutation sites based on default parameters [ 65 ]. The LEVs in the candidate interval were focused on.…”

Section: Methodsmentioning

confidence: 99%

Identification of fertility restoration candidate genes from a restorer line R186 for Gossypium harknessii cytoplasmic male sterile cotton

et al. 2023

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Background The utilization of heterosis based on three-line system is an effective strategy in crop breeding. However, cloning and mechanism elucidation of restorer genes for cytoplasmic male sterility (CMS) in upland cotton have yet been realized. Results This research is based on CMS line 2074A with the cytoplasm from Gossypium harknessii (D2-2) and restorer line R186. The offspring of 2074A × R186 were used to conduct genetic analysis. The fertility mechanism of 2074A can be speculated to be governed by multiple genes, since neither the single gene model nor the double genes model could be used. The bulked segregant analysis (BSA) for (2074A × R186) F2 determined the genetic interval of restorer genes on a region of 4.30 Mb on chromosome D05 that contains 77 annotated genes. Four genes were identified as candidates for fertility restoration using the RNA-seq data of 2074A, 2074B, and R186. There are a number of large effect variants in the four genes between 2074A and R186 that could cause amino acid changes. Evolutionary analysis and identity analysis revealed that GH_D05G3183, GH_D05G3384, and GH_D05G3490 have high identity with their homologs in D2-2, respectively. Tissue differential expression analysis revealed that the genes GH_D05G3183, GH_D05G3384, and GH_D05G3490 were highly expressed in the buds of the line R186. The predicted results demonstrated that GH_D05G3183, GH_D05G3384 and GH_D05G3490 might interact with GH_A02G1295 to regulate orf610a in mitochondria. Conclusion Our study uncovered candidate genes for fertility restoration in the restorer line R186 and predicted the possible mechanism for restoring the male fertility in 2074A. This research provided valuable insight into the nucleoplasmic interactions.

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Section: Methodsmentioning

confidence: 99%

Identification of fertility restoration candidate genes from a restorer line R186 for Gossypium harknessii cytoplasmic male sterile cotton

et al. 2023

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“…The NGS-based BSA is becoming a popular approach to identifying candidate genes for various traits, such as the soybean mosaic virus [ 91 ], charcoal rot resistance [ 92 ], flowering time [ 93 ], phytophthora resistance [ 94 ], and powdery-mildew resistance [ 95 ]. Recently, the deep-learning algorithm for BSA (DeepBSA) has been developed for QTL mapping and functional gene cloning in maize [ 96 ].…”

Section: Linking Of Crop Genome To Phenome With Aimentioning

confidence: 99%

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Khan

Wang

et al. 2022

IJMS

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Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

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“…BSA-seq has gained popularity in mapping QTLs in crop species due to reduced sequencing costs and user-friendly software for BSA-seq analysis. Software tools like deep-BSA, QTL-seq, and BRM (block regression mapping) have facilitated BSA-seq analysis, making it easier for researchers to identify genomic regions associated with trait variation [22][23][24]. BSA-seq has been widely employed in various crop species, includ-ing eggplant, to analyze key traits.…”

Section: Introductionmentioning

confidence: 99%

QTL Mapping for Bacterial Wilt Resistance in Eggplant via Bulked Segregant Analysis Using Genotyping by Sequencing

Xiao,

Lin,

Nie

et al. 2024

Agronomy

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The bacterial wilt disease caused by Ralstonia solanacearum is a significant threat to eggplant production. Breeding and promoting resistant varieties is one of the most effective methods to manage bacterial wilt. Conducting QTL (quantitative trait locus) mapping of resistant genes can substantially enhance the breeding of plant resistance to bacterial wilt. In this study, a population of 2200 F2 individuals derived from resistant and susceptible materials was utilized to establish extreme resistance and susceptibility pools. Following resequencing analysis of the parents and extreme pools, the QTL were examined using the DEEP-BSA software and QTLseqr R package (version 0.7.5.2). The results revealed that the detection of 10 QTL sites on chromosomes 5, 8, 9, and 11 by the five algorithms of the DEEP-BSA software. Additionally, the candidate region of 62 Mb–72 Mb on chromosome 5 was identified in all five algorithms of the DEEP-BSA software, as well as by the QTLseqr R package. Subsequent gene annotation uncovered 276 genes in the candidate region of 62 Mb–72 Mb on chromosome 5. Additionally, RNA-seq results indicated that only 13 genes had altered expression levels following inoculation with R. solanacearum in the resistant materials. Based on the expression levels, SMEL4_05g015980.1 and SMEL4_05g016110.1 were identified as candidate genes. Notably, SNP annotation identified a non-synonymous mutation in the exonic region of SMEL4_05g015980.1 and a variant in the promoter region of SMEL4_05g016110.1. The research findings have practical significance for the isolation of bacterial wilt resistance genes in eggplant and the development of resistance to bacterial wilt varieties in eggplant.

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DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits

Cited by 16 publications

References 41 publications

Identification of fertility restoration candidate genes from a restorer line R186 for Gossypium harknessii cytoplasmic male sterile cotton

Identification of fertility restoration candidate genes from a restorer line R186 for Gossypium harknessii cytoplasmic male sterile cotton

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

QTL Mapping for Bacterial Wilt Resistance in Eggplant via Bulked Segregant Analysis Using Genotyping by Sequencing

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