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

Quantitative Trait Loci Mapping and Comparative Transcriptome Analysis of Fruit Weight (FW) in Watermelon (Citrullus lanatus L.)

The watermelon (Citrullus lanatus L.) holds substantial economic value as a globally cultivated horticultural crop. However, the genetic architecture of watermelon fruit weight (FW) remains poorly understood. In this study, we used sh14-11 with small fruit and N14 with big fruit to construct 100 recombinant inbred lines (RILs). Based on whole-genome resequencing (WGR), 218,127 single nucleotide polymorphisms (SNPs) were detected to construct a high-quality genetic map. After quantitative trait loci (QTL) mapping, a candidate interval of 31-38 Mb on chromosome 2 was identified for FW. Simultaneously, the bulked segregant analysis (BSA) in the F2 population corroborated the identification of the same interval, encompassing the homologous gene linked to the known FW-related gene fas. Additionally, RNA-seq was carried out across 11 tissues from sh14-11 and N14, revealing expression profiles that identified 1695 new genes and corrected the annotation of 2941 genes. Subsequent differential expression analysis unveiled 8969 differentially expressed genes (DEGs), with 354 of these genes exhibiting significant differences across four key developmental stages. The integration of QTL mapping and differential expression analysis facilitated the identification of 14 FW-related genes, including annotated TGA and NAC transcription factors implicated in fruit development. This combined approach offers valuable insights into the genetic basis of FW, providing crucial resources for enhancing watermelon cultivation.

Mapping and Omics Integration: Towards Precise Rice Disease Resistance Breeding

Rice (Oryza sativa), as a staple crop feeding a significant portion of the global population, particularly in Asian countries, faces constant threats from various diseases jeopardizing global food security. A precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain an accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. The integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with a better understanding of their functional roles in disease resistance. The improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because a better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to a more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can result in a transformative impact on rice breeding for enhancing disease resistance.

OcBSA: An NGS-based bulk segregant analysis tool for outcross populations

Constructing inbred lines for self-incompatible species and species with long generation times is challenging, making the use of F1 outcross/segregating populations the main strategy for genetic studies of such species. However, there is a lack of dedicated algorithms/tools for rapid quantitative trait locus (QTL) mapping using the F1 populations. To this end, we have designed and developed an algorithm/tool called OcBSA specifically for QTL mapping of F1 populations. OcBSA transforms the four-haplotype inheritance problem from the two heterozygous diploid parents of the F1 population into the two-haplotype inheritance problem common in current genetic studies by removing the two haplotypes from the heterozygous parent that do not contribute to phenotype segregation in the F1 population. Testing of OcBSA on 1800 simulated F1 populations demonstrated its advantages over other currently available tools in terms of sensitivity and accuracy. In addition, the broad applicability of OcBSA was validated by QTL mapping using seven reported F1 populations of apple, pear, peach, citrus, grape, tea, and rice. We also used OcBSA to map the QTL for flower color in a newly constructed F1 population of potato generated in this study. The OcBSA mapping result was verified by the insertion or deletion markers to be consistent with a previously reported locus harboring the ANTHOCYANIN 2 gene, which regulates potato flower color. Taken together, these results highlight the power and broad utility of OcBSA for QTL mapping using F1 populations and thus a great potential for functional gene mining in outcrossing species. For ease of use, we have developed both Windows and Linux versions of OcBSA, which are freely available at: https://gitee.com/Bioinformaticslab/OcBSA.

Next-generation bulked segregant analysis for Breeding 4.0

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

Investigation of SNP markers for the melatonin production trait in the Hu sheep with bulked segregant analysis

BACKGROUND

As an important reproductive hormone, melatonin plays an important role in regulating the reproductive activities of sheep and other mammals. Hu sheep is a breed favoring for meat, with prolific traits. In order to explore the relationship between melatonin and reproductive function of Hu sheep, 7,694,759 SNPs were screened out through the whole genome sequencing analysis from high and low melatonin production Hu sheep.

RESULTS

A total of 68,673 SNPs, involving in 1126 genes, were identified by ED association analysis. Correlation analysis of SNPs of AANAT/ASMT gene and MTNR1A/MTNR1B gene were carried out. The melatonin level of CG genotype 7,981,372 of AANAT, GA genotype 7,981,866 of ASMT and GG genotype 17,355,171 of MTNR1A were higher than the average melatonin level of 1.64 ng/mL. High melatonin Hu sheep appear to have better multiple reproductive performance.

CONCLUSIONS

By using different methods, three SNPs which are associated with high melatonin production trait have been identified in Hu sheep. These 3 SNPs are located in melatonin synthetase AANAT/ASMT and receptor MTNR1A, respectively. Considering the positive association between melatonin production and reproductive performance in ruminants, these three SNPs can be served as the potential molecular markers for breading Hu sheep with the desirable reproductive traits.

The genetic architecture of prolificacy in maize revealed by association mapping and bulk segregant analysis

KEY MESSAGE

Here, we revealed maize prolificacy highly correlated with domestication and identified a causal gene ZmEN1 located in one novel QTL qGEN261 that regulating maize prolificacy by using multiple-mapping methods. The development of maize prolificacy (EN) is crucial for enhancing yield and breeding specialty varieties. To achieve this goal, we employed a genome-wide association study (GWAS) to analyze the genetic architecture of EN in maize. Using 492 inbred lines with a wide range of EN variability, our results demonstrated significant differences in genetic, environmental, and interaction effects. The broad-sense heritability (H2) of EN was 0.60. Through GWAS, we identified 527 significant single nucleotide polymorphisms (SNPs), involved 290 quantitative trait loci (QTL) and 806 genes. Of these SNPs, 18 and 509 were classified as major effect loci and minor loci, respectively. In addition, we performed a bulk segregant analysis (BSA) in an F2 population constructed by a few-ears line Zheng58 and a multi-ears line 647. Our BSA results identified one significant QTL, qBEN1. Importantly, combining the GWAS and BSA, four co-located QTL, involving six genes, were identified. Three of them were expressed in vegetative meristem, shoot tip, internode and tip of ear primordium, with ZmEN1, encodes an unknown auxin-like protein, having the highest expression level in these tissues. It suggested that ZmEN1 plays a crucial role in promoting axillary bud and tillering to encourage the formation of prolificacy. Haplotype analysis of ZmEN1 revealed significant differences between different haplotypes, with inbred lines carrying hap6 having more EN. Overall, this is the first report about using GWAS and BSA to dissect the genetic architecture of EN in maize, which can be valuable for breeding specialty maize varieties and improving maize yield.

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

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 (D_2-2) 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 D_2-2, 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.

Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat (Capra hircus) as the molecular breeding markers

Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study.

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

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.