Trait Mapping Approaches

Linkage mapping and association mapping are two supportive approaches, critical to identifying candidate loci underlying the plant response to abiotic or biotic stresses. Powerful approaches have evolved to understand interactions in complex signaling pathways, genes and regulatory regions to exploit in plant breeding. Genomic regions linked to quantitative traits called QTL investigations have successfully identified candidate genes conferring resistance to many plant stressors like heat, cold pathogens and pests in many crop species. To improve the identification of higher-resolution traits associated with genes, genome-wide association studies (GWAS) utilize the recombination of diverse association panels. Genotyping-by-sequencing (GBS) and SNP arrays have been successfully implemented in the GWAS approach for various crops such as rice, maize and soybean (Zhao et al. 2011; Hui Li et al. 2013; Hwang et al. 2014).

Progress in genomics has guided the advancement of NGS-based trait mapping approaches. Such approaches are not time-consuming as they increase the rate of trait mapping programs. Whole genome sequencing-based high-resolution mapping is a rapid method that has been improvised from the bulk-segregant analysis (BSA). It permits the mapping of target genomic regions, avoiding linkage map construction (Varshney et al. 2018). Nowadays, re-sequencing thousands of lines from different crop species is feasible in view of the fact that sequencing cost is reduced and abundant draft genome sequences of plants are available. The re-sequencing of thousands of individuals of the genetic population is advantageous in comprehending genetic relationships among individuals and provides a platform for high-resolution trait mapping as it imparts genome-wide SNPs at a large-scale (Pandey et al. 2016).

Pre-Breeding for Capturing Abiotic/Biotic Stress-Responsive Alleles

Pre-breeding is necessary for identifying and transferring traits and genes of interest from unadapted to intermediate materials. Such intermediate materials are used for the production of new varieties. It is a preliminary step in associating genetic variability that has been identified from crop wild relatives and un-adapted materials. The intermediate materials so obtained are used for the production of new varieties. Pre-breeding methods can be advantageous in the following ways (Varshney et al. 2018):

  • • Widening the genetic base
  • • Identification/characterization of stress-relevant traits
  • • Identification/introgression of stress-responsive genes into breeding populations
  • • Identification and transfer of stress-responsive genes using genetic transformation experiments

It has been seen that crop wild relatives in pre-breeding pose problems because of linkage drag introduction to the new elite gene pool and crossing the barrier between wild and cultivated species. However, for the identification of markers linked to genomic regions of desired traits, genomic technologies are beneficial in minimizing these issues. Deleterious effects of alleles in crop wild relatives can be repaired using genome-editing technologies (Scheben and Edwards 2017).

Genomics-Assisted Breeding to Develop Stress-Tolerant Crops

Several molecular breeding approaches have been applied to the introgression of candidate genomic regions into elite lines (Varshney et al. 2012). Following the identification of markers associated with traits, they are usually employed for marker-assisted selection (MAS) or marker-assisted back- crossing (MABC). The use of marker-assisted selection allows introgression of fewer than ten loci, and it has been implemented in various crops to transfer desired traits into elite cultivars. To target around 40 loci controlling complex traits, the marker-assisted recurrent selection is adopted. By repeating intercrossing, the marker-assisted selection is better suited for the development of better lines through the incorporation of a selective combination of alleles. Recently, the forward breeding approach has been used for early generation selection that involves the application of a marker set for agronomically important traits to test early generation. Genomic selection is one of the most potentially beneficial advancements in the area of genomics-assisted breeding. It assists in the faster improvement of the crop without the need for exhaustive study of individual loci. It has been popularly used in animal breeding programs; nonetheless, there have been reports in plants in the last ten years (Varshney et al. 2018). It depends on the genomics estimated breeding values (GEBVs) for individual lines of the genotyped and phenotyped training population. Individuals can be selected to develop a breeding population that could breed over several generations, avoiding further time-consuming phenotyping (Meuwissen, Hayes, and Goddard 2001). The genomic selection approach improves the selection efficiency by reducing breeding cycles and is favorable for quantitative traits (Varshney et al. 2018). In addition to that, it can furnish a complex trait selection of abiotic/biotic stress tolerance, holding the potentiality to develop stress-tolerant crops. In many crops, genes of agronomical-relevant traits have been cloned, and detailed studies on such genes have identified quantitative trait nucleotides (QTN) that possess considerable phenotypic effects. These QTNs can be altered using genome-editing technology. Another strategy called the promotion of allele by genome editing (PAGE) is a combination of genome editing and genomic selection; it is a modern genomics approach in plant molecular breeding. On account of these, it is expected that approaches like MABC and forward breeding can be used to transfer alleles holding positive phenotypic traits, whereas lethal alleles can be rectified using genome editing (PAGE) (Varshney et al. 2018). Consequently, the evaluation of the superior lines developed in target population environments (TPEs) plays a pivotal part in the selection of lines that will perform better under abiotic/biotic stressed conditions.

Microarray and RNA-Seq-Based Expression Profiling

The latest genomic tools are being developed to expedite interest in global gene expression studies. Gene expression analysis allows the extraction of abundant biological information that permits breeders to decipher the molecular intricacies of complex plant processes during stress as it points to the identification of new targets for crop improvement. For any genomics experiment, it is required to prepare comparable data for identification of differentially expressed genes (DEG), biological processes/metabolic pathways and gene families under stress conditions, establish gene regulatory networks (GRN) and trace vital regulators concerned with particular biological processes and pathways (Yang and Wei 2015). For instance, comparative transcriptomic studies in tomato have shown alteration in gene expression levels due to selection pressure as most of these are stress-responsive genes involved in imparting stress tolerance (Koenig et al. 2013).

Approaches like RT-PCR, differential display and cDNA-AFLPs are limited to the capture of low-abundance transcripts, but the serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS) methods overcome these pitfalls (Anisimov 2008; Reinartz et al.

2002). Even then, the current genomics era is dominated by microarray and RNA-seq technologies as they can profile >10,000 of transcripts and are sensitive to lowly expressed genes. Microarray is a hybridization-based approach based on the preparation of fluorescently labelled cDNA that can be either commercial high-density microarrays or custom made. To detect and quantify specific spliced isoforms, microarrays consisting of probes covering the exon junction are used. Microarrays are less expensive except for certain approaches such as high-resolution tiling arrays that are implemented to examine larger genomes. Unlike microarray, which relies upon previous sequence information, the RNA-seq approach does not always require reference genome information. In addition to that, RNA-seq analysis yields precise locations of transcript boundaries and possesses low background signal. Thus, RNA-seq is the only method adopted to capture the entire transcriptome in both a quantitative and high-throughput manner. It has been successfully applied in various crops with distinct breeding objectives, leading to the identification of genes in various abiotic stress responses (Ong et al. 2016). RNA-seq has shown immense potential for breeding complex traits.

Reverse Genetics

To induce genetic variation for improved crop yield, plant breeders have been applying mutagenesis. Earlier, X-ray radiation and gamma-ray radiation were used to induce mutations, however, the use of chemical mutagens is a research routine in mutation breeding nowadays. Chemical mutagens cause substitutions rather than chromosomal mutations; therefore, ethyl methanesulfonate (EMS) is used to induce random mutations to develop mutated lines. Targeting Induced Local Lesions in Genomes (TILLING) technology is a reverse genetics, low-cost strategy that can be applied to any plant irrespective of its ploidy level and genome size. It involves the mutagenesis of plants followed by the tracking of SNPs based on mismatch detection in the mutant population generated. It is a popular plant functional genomics technique that could be used in pre-breeding as well. It is essential to detect SNP in the mutant and wild-type in the specified gene of interest. The TILLING strategy is well-suited for most plant species as seeds can be stored for long periods after self-fertilization. In a general TILLING procedure, EMS-treated seeds are grown to produce Ml plants, which are further self-fertilized to generate an М2 population. DNA isolated from the leaves of М2 plants is subject to mutational screening (Barkley and Wang 2008). Gene-specific infrared dye-labeled primers are used amplify the target fragment, where the forward and reverse primers are IRDye700 and IRDye800 respectively. Following PCR amplification, denaturation of samples and annealing is done to generate heteroduplexes between wild-type and mutant DNA strands. Sample incubation in single-strand specific nucleases such as CEL1 endonucleases promotes the digestion of mismatches and individual samples loaded onto polyacrylamide gel. Fluorescently labeled DNA is visualized using a LICOR-DNA analyzer providing two gel images, holding data extracted from the 700 nm and 800 nm channels respectively. The gel data can be analyzed using data analysis programs; however, the exact changes caused by mutation are detected from DNA sequencing (Perry et al.

2003). With an effective pooling strategy, many samples can be investigated, and high throughput is achieved by employing many thermal cyclers and LICOR analyzers (Till et al. 2006). Since the expression of natural allelic variants is more robust than induced mutations, these variants are stabilized over their period of evolution (Jiang and Ramachandran 2010). Such variants occur at an exceedingly low frequency; thus, allele mining is conducted using the EcoTILLING method in the detection of natural variations in the gene of interest in many plant individuals, similar to the conventional TILLING technology (Simsek and Kacar 2010). A combination of forward and reverse genetic screening can be used to recover alleles that would have a drastic impact on the cost compared to whole genome sequencing methods (Perry et al. 2003; Hu et al. 2018).

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