Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives.

Crops feed the world's population and shape human civilization. The improvement of crop productivity has been ongoing for almost 10,000 years and has evolved from an experience-based to a knowledge-driven practice over the past three decades. Natural alleles and their reshuffling are long-standing genetic changes that affect how crops respond to various environmental conditions and agricultural practices. Decoding the genetic basis of natural variation is central to understanding crop evolution and, in turn, improving crop breeding. Here, we review current advances in the approaches used to map the causal alleles of natural variation, provide refined insights into the genetics and evolution of natural variation, and outline how this knowledge promises to drive the development of sustainable agriculture under the dome of emerging technologies.

Natural Variation in Crops: Realized Understanding, Continuing Promise.

Improvement in traits of agronomic importance is the top breeding priority of crop improvement programs. Majority of these agronomic traits show complex quantitative inheritance. Identification of quantitative trait loci (QTLs) followed by fine mapping QTLs and cloning of candidate genes/QTLs is central to trait analysis. Advances in genomic technologies revolutionized our understanding of genetics of complex traits, and genomic regions associated with traits were employed in marker-assisted breeding or cloning of QTLs/genes. Next-generation sequencing (NGS) technologies have enabled genome-wide methodologies for the development of ultra-high-density genetic linkage maps in different crops, thus allowing placement of candidate loci within few kbs in genomes. In this review, we compare the marker systems used for fine mapping and QTL cloning in the pre- and post-NGS era. We then discuss how different NGS platforms in combination with advanced experimental designs have improved trait analysis and fine mapping. We opine that efficient genotyping/sequencing assays may circumvent the need for cumbersome procedures that were earlier used for fine mapping. A deeper understanding of the trait architectures of agricultural significance will be crucial to accelerate crop improvement.

/pdf/fine-mapping-and-gene-cloning-in-the-post-ngs-era-advances-4m68qkdmea.pdf

Fine mapping and gene cloning in the post-NGS era: advances and prospects

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. 

Bulk segregation analysis in the <scp>NGS</scp> era: a review of its teenage years

Salt stress is one of the main abiotic stresses that limits rice production worldwide. Rice salt tolerance at the bud burst stage directly affects the seedling survival rate and the final yield in the direct seeding cultivation model. However, the reports on quantitative trait locus (QTL) mapping and map-based cloning for salt tolerance at the bud burst stage are limited. Here, an F2:3 population derived from a cross between IR36 (salt-sensitive) and Weiguo (salt-tolerant) was used to identify salt-tolerant QTL interval at the bud burst stage using a whole-genome sequencing-based QTL-seq containing 40 extreme salt-tolerant and 40 extreme salt-sensitive individuals. A major QTL, qRSL7, related to relative shoot length (RSL) was detected on chromosome 7 using ΔSNP index algorithms and Euclidean Distance (ED) algorithms. According to single nucleotide polymorphisms (SNPs) between the parents, 25 Kompetitive allele-specific PCR (KASP) markers were developed near qRSL7, and regional QTL mapping was performed using 199 individuals from the F2:3 population. We then confirmed and narrowed down qRSL7 to a 222 kb genome interval. Additionally, RNA sequencing (RNA-seq) was performed for IR36 and Weiguo at 36 h after salt stress and control condition at the bud burst stage, and 5 differentially expressed genes (DEGs) were detected in the candidate region. The qRT-PCR results showed the same expression patterns as the RNA-seq data. Furthermore, sequence analysis revealed a 1 bp Indel difference in Os07g0569700 (OsSAP16) between IR36 and Weiguo. OsSAP16 encodes a stress-associated protein whose expression is increased under drought stress. These results indicate that OsSAP16 was the candidate gene of qRSL7. The results is useful for gene cloning of qRSL7 and for improving the salt tolerance of rice varieties by marker assisted selection (MAS).

/pdf/identification-of-a-major-qtl-and-candidate-gene-analysis-of-33kdfn331y.pdf

Identification of a Major QTL and Candidate Gene Analysis of Salt Tolerance at the Bud Burst Stage in Rice (Oryza sativa L.) Using QTL-Seq and RNA-Seq.

Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F 2 population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, G w, for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F 2 plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate G w to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of G w in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html , has been provided in the supplemental materials for the users’ convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F 2 population. This study proposes a combinatorial strategy for detecting various types of QTLs in an F 2 population. The numbers of marker alleles in each extreme pool are used to predict the numbers of marker genotypes, which are used to construct the G w statistic. dQTG-seq1 has significantly higher power than existing methods, and dQTG-seq2 has the highest power, especially for detecting extremely over-dominant and small-effect genes. 

https://doi.org/10.1016/j.xplc.2022.100319

A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F2 population

Although methodologies and software packages for bulked segregant analysis (BSA) are well established, it is difficult to detect extremely over-dominant and small-effect genes for quantitative traits in F2 population. To address this issue, we proposed a combinatorial strategy to identify all types of quantitative trait loci (QTLs) using extreme phenotype individuals in F2. To popularize this strategy, we developed an R software package dQTG.seq v1.0.1. It has some features not found in other BSA software packages: 1) new (dQTG-seq1 and dQTG-seq2) and existing (G', deltaSNP, Euclidean distance (ED), and SmoothLOD) methods are available to identify all types of QTLs in bi-parental segregation populations, one data file with two BSA and three QTL-mapping data formats was inputted, and two *.csv files and one figure were outputted; 2) main smoothing methods (AIC, Window size, and Block) have been incorporated into each of the above-mentioned methods; 3) the threshold value of LOD score for significant QTLs is determined by permutation experiments. To save running time, vroom function was used to read the dataset, and parallel operation was used to estimate parameters. In real data analyses, users should select a suitable initial value of window size, depending on the species, and appropriate smoothing methods to obtain the best result. dQTG-seq2 detects more known loci and genes for rice grain number per panicle than composite interval mapping (CIM) and inclusive CIM, especially extremely over-dominant and small-effect genes. A handbook for our software package (https://cran.r-project.org/web/packages/dQTG.seq/index.html) has been provided in the supplemental materials for the users' convenience. 

https://doi.org/10.1016/j.csbj.2022.05.009

dQTG.seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations

QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples

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A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F2 population

dQTG.seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations

QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples