Genetic dissection of harvest index and related traits through genome-wide quantitative trait locus mapping in Brassica napus L

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches

Introduction

Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively.

Results

The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R² of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2).

Discussion

Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

Genome-wide association study identifies candidate genes and favorable haplotypes for seed yield in Brassica napus

Oilseed rape (Brassica napus L.) is one of the most essential oil crops. Genetic improvement of seed yield (SY) is a major aim of B. napus breeding. Several studies have been reported on the genetic mechanisms of SY of B. napus. Here, a genome-wide association study (GWAS) of SY was conducted using a panel of 403 natural accessions of B. napus, with more than five million high-quality single-nucleotide polymorphisms (SNPs). A total of 1773 significant SNPs were detected associated with SY, and 783 significant SNPs were co-located with previously reported QTLs. The lead SNPs chrA01__8920351 and chrA02__4555979 were jointly detected in Trial 2_2 and Trial 2_mean value, and in Trial 1_2 and Trial 1_mean value, respectively. Subsequently, two candidate genes of BnaA01g17200D and BnaA02g08680D were identified through combining transcriptome, candidate gene association analysis, and haplotype analysis. BnaA09g10430D detected through lead SNP chrA09__5160639 was associated with SY of B. napus. Our results provide valuable information for studying the genetic control of seed yield in B. napus and valuable genes, haplotypes, and cultivars resources for the breeding of high seed yield B. napus cultivars.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01332-6.

Further insight into decreases in seed glucosinolate content based on QTL mapping and RNA-seq in Brassica napus L

The QTL hotspots determining seed glucosinolate content instead of only four HAG1 loci and elucidation of a potential regulatory model for rapeseed SGC variation. Glucosinolates (GSLs) are amino acid-derived, sulfur-rich secondary metabolites that function as biopesticides and flavor compounds, but the high seed glucosinolate content (SGC) reduces seed quality for rapeseed meal. To dissect the genetic mechanism and further reduce SGC in rapeseed, QTL mapping was performed using an updated high-density genetic map based on a doubled haploid (DH) population derived from two parents that showed significant differences in SGC. In 15 environments, a total of 162 significant QTLs were identified for SGC and then integrated into 59 consensus QTLs, of which 32 were novel QTLs. Four QTL hotspot regions (QTL-HRs) for SGC variation were discovered on chromosomes A09, C02, C07 and C09, including seven major QTLs that have previously been reported and four novel major QTLs in addition to HAG1 loci. SGC was largely determined by superimposition of advantage allele in the four QTL-HRs. Important candidate genes directly related to GSL pathways were identified underlying the four QTL-HRs, including BnaC09.MYB28, BnaA09.APK1, BnaC09.SUR1 and BnaC02.GTR2a. Related differentially expressed candidates identified in the minor but environment stable QTLs indicated that sulfur assimilation plays an important rather than dominant role in SGC variation. A potential regulatory model for rapeseed SGC variation constructed by combining candidate GSL gene identification and differentially expressed gene analysis based on RNA-seq contributed to a better understanding of the GSL accumulation mechanism. This study provides insights to further understand the genetic regulatory mechanism of GSLs, as well as the potential loci and a new route to further diminish the SGC in rapeseed.

Transcriptome and Small RNA Sequencing Reveal the Mechanisms Regulating Harvest Index in Brassica napus

Harvest index (HI), the ratio of harvested seed weight to total aboveground biomass weight, is an economically critical value reflecting the convergence of complex agronomic traits. HI values in rapeseed (Brassica napus) remain much lower than in other major crops, and the underlying regulatory network is largely unknown. In this study, we performed mRNA and small RNA sequencing to reveal the mechanisms shaping HI in B. napus during the seed-filling stage. A total of 8,410 differentially expressed genes (DEGs) between high-HI and low-HI accessions in four tissues (silique pericarp, seed, leaves, and stem) were identified. Combining with co-expression network, 72 gene modules were identified, and a key gene BnaSTY46 was found to participate in retarded establishment of photosynthetic capacity to influence HI. Further research found that the genes involved in circadian rhythms and response to stimulus may play important roles in HI and that their transcript levels were modulated by differentially expressed microRNAs (DEMs), and we identified 903 microRNAs (miRNAs), including 46 known miRNAs and 857 novel miRNAs. Furthermore, transporter activity-related genes were critical to enhancing HI in good cultivation environments. Of 903 miRNAs, we found that the bna-miR396-Bna.A06SRp34a/Bna.A01EMB3119 pair may control the seed development and the accumulation of storage compounds, thus contributing to higher HI. Our findings uncovered the underlying complex regulatory network behind HI and offer potential approaches to rapeseed improvement.

A major yellow-seed QTL on chromosome A09 significantly increases the oil content and reduces the fiber content of seed in Brassica napus

A major yellow-seed QTL on chromosome A09 significantly increases the oil content and reduces the fiber content of seed in Brassica napus. The yellow-seed trait (YST) has always been a main breeding objective for rapeseed because yellow-seeded B. napus generally contains higher oil contents, fewer pigments and polyphenols and lower fiber content than black-seeded B. napus, although the mechanism controlling this correlation remains unclear. In this study, QTL mapping was implemented for YST based on a KN double haploid population derived from the hybridization of yellow-seeded B. napus N53-2 with a high oil content and black-seeded Ken-C8 with a relatively low oil content. Ten QTLs were identified, including four stable QTLs that could be detected in multiple environments. A major QTL, cqSC-A09, on chromosome A09 was identified by both QTL mapping and BSR-Seq technology, and explained more than 41% of the phenotypic variance. The major QTL cqSC-A09 for YST not only controls the seed color but also affects the oil and fiber contents in seeds. More importantly, the advantageous allele could increase the oil content and reduce the pigment and fiber content at the same time. This is the first QTL reported to control seed color, oil content and fiber content simultaneously with a large effect and has great application value for breeding high oil varieties with high seed quality. Important candidate genes, including BnaA09. JAZ1, BnaA09. GH3.3 and BnaA09. LOX3, were identified for cqSC-A09 by combining sequence variation annotation, expression differences and an interaction network, which lays a foundation for further cloning and breeding applications in the future.

Construction of a Quantitative Genomic Map, Identification and Expression Analysis of Candidate Genes for Agronomic and Disease-Related Traits in Brassica napus

Rapeseed is the second most important oil crop in the world. Improving seed yield and seed oil content are the two main highlights of the research. Unfortunately, rapeseed development is frequently affected by different diseases. Extensive research has been made through many years to develop elite cultivars with high oil, high yield, and/or disease resistance. Quantitative trait locus (QTL) analysis has been one of the most important strategies in the genetic deciphering of agronomic characteristics. To comprehend the distribution of these QTLs and to uncover the key regions that could simultaneously control multiple traits, 4,555 QTLs that have been identified during the last 25 years were aligned in one unique map, and a quantitative genomic map which involved 128 traits from 79 populations developed in 12 countries was constructed. The present study revealed 517 regions of overlapping QTLs which harbored 2,744 candidate genes and might affect multiple traits, simultaneously. They could be selected to customize super-rapeseed cultivars. The gene ontology and the interaction network of those candidates revealed genes that highly interacted with the other genes and might have a strong influence on them. The expression and structure of these candidate genes were compared in eight rapeseed accessions and revealed genes of similar structures which were expressed differently. The present study enriches our knowledge of rapeseed genome characteristics and diversity, and it also provided indications for rapeseed molecular breeding improvement in the future.

Fine-mapping and transcriptome analysis of a candidate gene controlling plant height in Brassica napus L

BACKGROUND

Brassica napus provides approximately 13-16% of global vegetable oil for human consumption and biodiesel production. Plant height (PH) is a key trait that affects plant architecture, seed yield and harvest index. However, the genetic mechanism of PH in B. napus is poorly understood.

RESULTS

A dwarf mutant df59 was isolated from a large-scale screening of an ethyl methanesulphonate-mutagenized rapeseed variety Ningyou 18. A genetic analysis showed that the dwarfism phenotype was controlled by one semi-dominant gene, which was mapped on C9 chromosome by quantitative trait loci sequencing analysis and designated as BnaDwf.C9. To fine-map BnaDwf.C9, two F2 populations were constructed from crosses between conventional rapeseed cultivars (Zhongshuang 11 and Holly) and df59. BnaDwf.C9 was fine-mapped to the region between single-nucleotide polymorphism (SNP) markers M14 and M4, corresponding to a 120.87-kb interval of the B. napus 'Darmor-bzh' genome. Within this interval, seven, eight and nine annotated or predicted genes were identified in "Darmor-bzh", "Ningyou 7" and "Zhongshuang 11" reference genomes, respectively. In addition, a comparative transcriptome analysis was performed using stem tips from Ningyou 18 and df59 at the stem elongation stage. In total, 3995 differentially expressed genes (DEGs) were identified. Among them, 118 DEGs were clustered in plant hormone-related signal transduction pathways, including 81 DEGs were enriched in auxin signal transduction. Combining the results of fine-mapping and transcriptome analyses, BnaC09g20450D was considered a candidate gene for BnaDwf.C9, which contains a SNP that co-segregated in 4746 individuals. Finally, a PCR-based marker was developed based on the SNP in BnaC09g20450D.

CONCLUSIONS

The combination of quantitative trait loci sequencing, fine-mapping and genome-wide transcriptomic analysis revealed one candidate gene located within the confidence interval of 120.87-kb region. This study provides a new genetic resource for semi-dwarf breeding and new insights into understanding the genetic architecture of PH in B. napus.

ddRAD sequencing-based identification of inter-genepool SNPs and association analysis in Brassica juncea

BACKGROUND

Narrow genetic base, complex allo-tetraploid genome and presence of repetitive elements have led the discovery of single nucleotide polymorphisms (SNPs) in Brassica juncea (AABB; 2n = 4x = 36) at a slower pace. Double digest RAD (ddRAD) - a genome complexity reduction technique followed by NGS was used to generate a total of 23 million paired-end reads from three genotypes each of Indian (Pusa Tarak, RSPR-01 and Urvashi) and Exotic (Donskaja IV, Zem 1 and EC287711) genepools.

RESULTS

Sequence data analysis led to the identification of 10,399 SNPs in six genotypes at a read depth of 10x coverage among the genotypes of two genepools. A total of 44 hyper-variable regions (nucleotide variation hotspots) were also found in the genome, of which 93% were found to be a part of coding genes/regions. The functionality of the identified SNPs was estimated by genotyping a subset of SNPs on MassARRAY® platform among a diverse set of B. juncea genotypes. SNP genotyping-based genetic diversity and population studies placed the genotypes into two distinct clusters based mostly on the place of origin. The genotypes were also characterized for six morphological traits, analysis of which revealed a significant difference in the mean values between Indian and Exotic genepools for six traits. The association analysis for six traits identified a total of 45 significant marker-trait associations on 11 chromosomes of A- and B- group of progenitor genomes.

CONCLUSIONS

Despite narrow diversity, the ddRAD sequencing was able to identify large number of nucleotide polymorphisms between the two genepools. Association analysis led to the identification of common SNPs/genomic regions associated between flowering and maturity traits, thereby underscoring the possible role of common chromosomal regions-harboring genes controlling flowering and maturity in Brassica juncea.