Genetic control of root morphology in response to nitrogen across rapeseed diversity

Rapeseed (Brassica napus L.) is an oil-containing crop of great economic value but with considerable nitrogen requirement. Breeding root systems that efficiently absorb nitrogen from the soil could be a driver to ensure genetic gains for more sustainable rapeseed production. The aim of this study is to identify genomic regions that regulate root morphology in response to nitrate availability. The natural variability offered by 300 inbred lines was screened at two experimental locations. Seedlings grew hydroponically with low or elevated nitrate levels. Fifteen traits related to biomass production and root morphology were measured. On average across the panel, a low nitrate level increased the root-to-shoot biomass ratio and the lateral root length. A large phenotypic variation was observed, along with important heritability values and genotypic effects, but low genotype-by-nitrogen interactions. Genome-wide association study and bulk segregant analysis were used to identify loci regulating phenotypic traits. The first approach nominated 319 SNPs that were combined into 80 QTLs. Three QTLs identified on the A07 and C07 chromosomes were stable across nitrate levels and/or experimental locations. The second approach involved genotyping two groups of individuals from an experimental F2 population created by crossing two accessions with contrasting lateral root lengths. These individuals were found in the tails of the phenotypic distribution. Co-localized QTLs found in both mapping approaches covered a chromosomal region on the A06 chromosome. The QTL regions contained some genes putatively involved in root organogenesis and represent selection targets for redesigning the root morphology of rapeseed.

QTL mapping reveals novel genes and mechanisms underlying variations in H2S production during alcoholic fermentation in Saccharomyces cerevisiae

Saccharomyces cerevisiae requirement for reduced sulfur to synthesize methionine and cysteine during alcoholic fermentation, is mainly fulfilled through the sulfur assimilation pathway. Saccharomyces cerevisiae reduces sulfate into sulfur dioxide (SO2) and sulfide (H2S), whose overproduction is a major issue in winemaking, due to its negative impact on wine aroma. The amount of H2S produced is highly strain-specific and also depends on SO2 concentration, often added to grape must. Applying a bulk segregant analysis to a 96-strain-progeny derived from two strains with different abilities to produce H2S, and comparing allelic frequencies along the genome of pools of segregants producing contrasting H2S quantities, we identified two causative regions involved in H2S production in the presence of SO2. A functional genetic analysis allowed the identification of variants in four genes able to impact H2S formation, viz; ZWF1, ZRT2, SNR2, and YLR125W, and involved in functions and pathways not associated with sulfur metabolism until now. These data point out that, in wine fermentation conditions, redox status, and zinc homeostasis are linked to H2S formation while providing new insights into the regulation of H2S production, and a new vision of the interplay between the sulfur assimilation pathway and cell metabolism.

Genome-Wide Association Analysis of Heat Tolerance in F2 Progeny from the Hybridization between Two Congeneric Oyster Species

As the world's largest farmed marine animal, oysters have enormous economic and ecological value. However, mass summer mortality caused by high temperature poses a significant threat to the oyster industry. To investigate the molecular mechanisms underlying heat adaptation and improve the heat tolerance ability in the oyster, we conducted genome-wide association analysis (GWAS) analysis on the F2 generation derived from the hybridization of relatively heat-tolerant Crassostrea angulata ♀ and heat-sensitive Crassostrea gigas ♂, which are the dominant cultured species in southern and northern China, respectively. Acute heat stress experiment (semi-lethal temperature 42 °C) demonstrated that the F2 population showed differentiation in heat tolerance, leading to extremely differentiated individuals (approximately 20% of individuals die within the first four days with 10% survival after 14 days). Genome resequencing and GWAS of the two divergent groups had identified 18 significant SNPs associated with heat tolerance, with 26 candidate genes located near these SNPs. Eleven candidate genes that may associate with the thermal resistance were identified, which were classified into five categories: temperature sensor (Trpm2), transcriptional factor (Gata3), protein ubiquitination (Ube2h, Usp50, Uchl3), heat shock subfamily (Dnajc17, Dnaja1), and transporters (Slc16a9, Slc16a14, Slc16a9, Slc16a2). The expressional differentiation of the above genes between C. gigas and C. angulata under sublethal temperature (37 °C) further supports their crucial role in coping with high temperature. Our results will contribute to understanding the molecular mechanisms underlying heat tolerance, and provide genetic markers for heat-resistance breeding in the oyster industry.

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.

Harnessing the potential of bulk segregant analysis sequencing and its related approaches in crop breeding

Most plant traits are governed by polygenes including both major and minor genes. Linkage mapping and positional cloning have contributed greatly to mapping genomic loci controlling important traits in crop species. However, they are low-throughput, time-consuming, and have low resolution due to which their efficiency in crop breeding is reduced. In this regard, the bulk segregant analysis sequencing (BSA-seq) and its related approaches, viz., quantitative trait locus (QTL)-seq, bulk segregant RNA-Seq (BSR)-seq, and MutMap, have emerged as efficient methods to identify the genomic loci/QTLs controlling specific traits at high resolution, accuracy, reduced time span, and in a high-throughput manner. These approaches combine BSA with next-generation sequencing (NGS) and enable the rapid identification of genetic loci for qualitative and quantitative assessments. Many previous studies have shown the successful identification of the genetic loci for different plant traits using BSA-seq and its related approaches, as discussed in the text with details. However, the efficiency and accuracy of the BSA-seq depend upon factors like sequencing depth and coverage, which enhance the sequencing cost. Recently, the rapid reduction in the cost of NGS together with the expected cost reduction of third-generation sequencing in the future has further increased the accuracy and commercial applicability of these approaches in crop improvement programs. This review article provides an overview of BSA-seq and its related approaches in crop breeding together with their merits and challenges in trait mapping.

Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

Background

Bulk segregant analysis (BSA) combined with next generation sequencing is a powerful tool to identify quantitative trait loci (QTL). The impact of the size of the study population and the percentage of extreme genotypes analysed have already been assessed. But a good comparison of statistical approaches designed to identify QTL regions using next generation sequencing (NGS) technologies for BSA is still lacking.

Results

We developed an R code to simulate QTLs in bulks of F2 contrasted lines. We simulated a range of recombination rates based on estimations using different crop species. The simulations were used to benchmark the ability of statistical methods identify the exact location of true QTLs. A single QTL led to a shift in allele frequency across a large fraction of the chromosome for plant species with low recombination rate. The smoothed version of all statistics performed best notably the smoothed Euclidean distance-based statistics was always found to be more accurate in identifying the location of QTLs. We propose a simulation approach to build confidence interval statistics for the detection of QTLs.

Conclusion

We highlight the statistical methods best suited for BSA studies using NGS technologies in crops even when recombination rate is low. We also provide simulation codes to build confidence intervals and to assess the impact of recombination for application to other studies. This computational study will help select NGS-based BSA statistics that are useful to the broad scientific community.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08718-y.

High-performance pipeline for MutMap and QTL-seq

Summary

Bulked segregant analysis implemented in MutMap and QTL-seq is a powerful and efficient method to identify loci contributing to important phenotypic traits. However, the previous pipelines were not user-friendly to install and run. Here, we describe new pipelines for MutMap and QTL-seq. These updated pipelines are approximately 5-8 times faster than the previous pipeline, are easier for novice users to use, and can be easily installed through bioconda with all dependencies.

Availability

The new pipelines of MutMap and QTL-seq are written in Python and can be installed via bioconda. The source code and manuals are available online (MutMap: https://github.com/YuSugihara/MutMap, QTL-seq: https://github.com/YuSugihara/QTL-seq).

Bulk segregation analysis in the NGS era: a review of its teenage years

Bulk segregation analysis (BSA) utilizes a strategy of pooling individuals with extreme phenotypes to conduct economical and rapidly linked marker screening or quantitative trait locus (QTL) mapping. With the development of next-generation sequencing (NGS) technology in the past 10 years, BSA methods and technical systems have been gradually developed and improved. At the same time, the ever-decreasing costs of sequencing accelerate NGS-based BSA application in different species, including eukaryotic yeast, grain crops, economic crops, horticultural crops, trees, aquatic animals, and insects. This paper provides a landscape of BSA methods and reviews the BSA development process in the past decade, including the sequencing method for BSA, different populations, different mapping algorithms, associated region threshold determination, and factors affecting BSA mapping. Finally, we summarize related strategies in QTL fine mapping combining BSA.

Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes

Genomic regions that control traits of interest can be rapidly identified using BSA-Seq, a technology in which next-generation sequencing is applied to bulked segregant analysis (BSA). We recently developed the significant structural variant method for BSA-Seq data analysis that exhibits higher detection power than standard BSA-Seq analysis methods. Our original algorithm was developed to analyze BSA-Seq data in which genome sequences of one parent served as the reference sequences in genotype calling and, thus, required the availability of high-quality assembled parental genome sequences. Here, we modified the original script to effectively detect the genomic region–trait associations using only bulk genome sequences. We analyzed two public BSA-Seq datasets using our modified method and the standard allele frequency and G-statistic methods with and without the aid of the parental genome sequences. Our results demonstrate that the genomic region(s) associated with the trait of interest could be reliably identified via the significant structural variant method without using the parental genome sequences.

Optimization of BSA-seq experiment for QTL mapping

Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F2 to F3. These findings enable researchers to optimize the experimental design for BSA-seq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/ and https://github.com/huanglikun/BSA-seqDesignTool.

Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement

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Due to reduced diversity, it is essential to map domesticated and improved genes.

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13 known and 442 candidate genes were mined for seed size- and oil-related traits.

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All the genes were used to explain trait changes in domestication and improvement.

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56 domesticated and 15 improved genes may be valuable for future soybean breeding.

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This study provides useful gene resources for future breeding and biology research.

To address domestication and improvement studies of soybean seed size- and oil-related traits, a series of domesticated and improved regions, loci, and candidate genes were identified in 286 soybean accessions using domestication and improvement analyses, genome-wide association studies, quantitative trait locus (QTL) mapping and bulked segregant analyses in this study. As a result, 534 candidate domestication regions (CDRs) and 458 candidate improvement regions (CIRs) were identified in this study and integrated with those in five and three previous studies, respectively, to obtain 952 CDRs and 538 CIRs; 1469 loci for soybean seed size- and oil-related traits were identified in this study and integrated with those in Soybase to obtain 433 QTL clusters. The two results were intersected to obtain 245 domestication and 221 improvement loci for the above traits. Around these trait-related domestication and improvement loci, 7 domestication and 7 improvement genes were found to be truly associated with these traits, and 372 candidate domestication and 87 candidate improvement genes were identified using gene expression, SNP variants in genome, miRNA binding, KEGG pathway, DNA methylation, and haplotype analysis. These genes were used to explain the trait changes in domestication and improvement. As a result, the trait changes can be explained by their frequencies of elite haplotypes, base mutations in coding region, and three factors affecting their expression levels. In addition, 56 domestication and 15 improvement genes may be valuable for future soybean breeding. This study can provide useful gene resources for future soybean breeding and molecular biology research.

Genomic mapping and identification of candidate genes encoding nulliplex-branch trait in sea-island cotton (Gossypium barbadense L.) by multi-omics analysis

Nulliplex branch is a key architectural trait in sea-island cotton (Gossypium barbadense L.), but its genetic basis is not well understood. Here we investigated the genetic basis of the nulliplex-branch trait in cotton by combining newly created bulked segregant analysis (BSA)-seq data, published RNA-seq data, and published whole-genome resequencing (WGR) data. We delimited the nulliplex-branch locus (qD07-NB) to D07, region 14.8-17.1 Mb, using various BSA methods and markers. We integrated our BSA data with WGR data of sea-island cotton varieties and detected a missense single-nucleotide polymorphism in the candidate gene (Gbar_D07G011870) of qD07-NB. This gene was under positive selection during sea-island cotton breeding in the Xinjiang Uygur Autonomous Region, China. Notably, the nulliplex-branch varieties possessed a better fiber quality than the long-branch varieties, and a set of high-quality molecular markers was identified for molecular breeding of the nulliplex-branch trait in cotton. We combined BSA-seq and RNA-seq data to compare gene expression profiles between two elite sea-island cotton varieties during three developmental stages. We identified eleven relevant candidate genes, five downregulated and six upregulated, in the qD07-NB locus. This research will expand our understanding of the genetic basis of the nulliplex-branch trait and provide guidance for architecture-focused breeding in cotton.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01229-w.

A combined BSA-Seq and linkage mapping approach identifies genomic regions associated with Phytophthora root and crown rot resistance in squash

Two QTL mapping approaches were used to identify a total of six QTL associated with Phytophthora root and crown rot resistance in a biparental squash population. Phytophthora root and crown rot, caused by the soilborne oomycete pathogen Phytophthora capsici, leads to severe yield losses in squash (Cucurbita pepo). To identify quantitative trait loci (QTL) involved in resistance to this disease, we crossed a partially resistant squash breeding line with a susceptible zucchini cultivar and evaluated over 13,000 F₂ seedlings in a greenhouse screen. Bulked segregant analysis with whole genome resequencing (BSA-Seq) resulted in the identification of five genomic regions-on chromosomes 4, 5, 8, 12, and 16-featuring significant allele frequency differentiation between susceptible and resistant bulks in each of two independent replicates. In addition, we conducted linkage mapping using a population of 176 F₃ families derived from individually genotyped F₂ individuals. Variation in disease severity among these families was best explained by a four-QTL model, comprising the same loci identified via BSA-Seq on chromosomes 4, 5, and 8 as well as an additional locus on chromosome 19, for a combined total of six QTL identified between both methods. Loci, whether those identified by BSA-Seq or linkage mapping, were of small-to-moderate effect, collectively accounting for 28-35% and individually for 2-10% of the phenotypic variance explained. However, a multiple linear regression model using one marker in each BSA-Seq QTL could predict F_2:3 disease severity with only a slight drop in cross-validation accuracy compared to genomic prediction models using genome-wide markers. These results suggest that marker-assisted selection could be a suitable approach for improving Phytophthora crown and root rot resistance in squash.

Identifying a melanogenesis-related candidate gene by a high-quality genome assembly and population diversity analysis in Hypsizygus marmoreus

Hypsizygus marmoreus is one of the most important edible fungi in Basidiomycete division and includes white and gray strains. However, very limited knowledge is known about the genomic structures and the genetic basis for the white/gray diversity of this mushroom. Here, we report the near-complete high-quality H. marmoreus genome at the chromosomal level. Comparative genomics analysis indicates that chromosome structures were relatively conserved, and variations in collinearity and chromosome number were mainly attributed by chromosome split/fusion events in Aragicales, whereas the fungi genome experienced many genomic chromosome fracture, fusion, and genomic replication events after the split of Aragicales from Basidiomycetes. Resequencing of 57 strains allows us to classify the population into four major groups and associate genetic variations with morphological features, indicating that white strains were not originated independently. We further generated genetic populations and identified a cytochrome P450 as the candidate causal gene for the melanogenesis in H. marmoreus based on bulked segregant analysis (BSA) and comparative transcriptome analysis. The high-quality H. marmoreus genome and diversity data compiled in this study provide new knowledge and resources for the molecular breeding of H. marmoreus as well as the evolution of Basidiomycete.

Mapping of QTLs conferring high grain length-breadth relative expansion during cooking in rice cultivar Paw San Hmwe

Paw San Hmwe (PSH) is a high-quality rice cultivar from Myanmar. PSH has short and broad grains, but the grains become slender after cooking. This desirable feature can be described as a high value of grain length-breadth relative expansion index (GREI). To understand the genetic basis of high GREI in PSH, we crossed PSH with Guang 8B (G8B), a rice cultivar from China with low GREI, to develop an F₂ population and a subsequent F_2:3 population. Based on the phenotypes of these two populations measured in two years and using the method of sequencing-based bulked segregant analysis followed by verification with conventional linkage-based QTL mapping method, we mapped three QTLs for GREI. The three QTLs were located on chromosomes 3, 5 and 12, respectively, with the trait-increasing alleles all from PSH, and could explain a total of 62.5% of the phenotypic variance and 84.1% of the additive genetic variance. The results suggest that the three QTLs would be useful for the genetic improvement of GREI in rice, and the linked markers will facilitate the selection of the favorable alleles from PSH in breeding.

A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

The development of double haploids (DHs) is a straightforward path for obtaining pure lines but has multiple bottlenecks. Among them is the determination of the optimal stage of pollen induction for androgenesis. In this work, we developed Microscan, a deep learning-based system for the detection and recognition of the stages of pollen development. In a first experiment, the algorithm was developed adapting the RetinaNet predictive model using microspores of different eggplant accessions as samples. A mean average precision of 86.30% was obtained. In a second experiment, the anther range to be cultivated in vitro was determined in three eggplant genotypes by applying the Microscan system. Subsequently, they were cultivated following two different androgenesis protocols (Cb and E6). The response was only observed in the anther size range predicted by Microscan, obtaining the best results with the E6 protocol. The plants obtained were characterized by flow cytometry and with the Single Primer Enrichment Technology high-throughput genotyping platform, obtaining a high rate of confirmed haploid and double haploid plants. Microscan has been revealed as a tool for the high-throughput efficient analysis of microspore samples, as it has been exemplified in eggplant by providing an increase in the yield of DHs production.

Bulked Segregant Analysis Coupled with Whole-Genome Sequencing (BSA-Seq) Mapping Identifies a Novel pi21 Haplotype Conferring Basal Resistance to Rice Blast Disease

Basal or partial resistance has been considered race-non-specific and broad-spectrum. Therefore, the identification of genes or quantitative trait loci (QTLs) conferring basal resistance and germplasm containing them is of significance in breeding crops with durable resistance. In this study, we performed a bulked segregant analysis coupled with whole-genome sequencing (BSA-seq) to identify QTLs controlling basal resistance to blast disease in an F₂ population derived from two rice varieties, 02428 and LiXinGeng (LXG), which differ significantly in basal resistance to rice blast. Four candidate QTLs, qBBR-4, qBBR-7, qBBR-8, and qBBR-11, were mapped on chromosomes 4, 7, 8, and 11, respectively. Allelic and genotypic association analyses identified a novel haplotype of the durable blast resistance gene pi21 carrying double deletions of 30 bp and 33 bp in 02428 (pi21-2428) as a candidate gene of qBBR-4. We further assessed haplotypes of Pi21 in 325 rice accessions, and identified 11 haplotypes among the accessions, of which eight were novel types. While the resistant pi21 gene was found only in japonica before, three Chinese indica varieties, ShuHui881, Yong4, and ZhengDa4Hao, were detected carrying the resistant pi21-2428 allele. The pi21-2428 allele and pi21-2428-containing rice germplasm, thus, provide valuable resources for breeding rice varieties, especially indica rice varieties, with durable resistance to blast disease. Our results also lay the foundation for further identification and functional characterization of the other three QTLs to better understand the molecular mechanisms underlying rice basal resistance to blast disease.