Trait Mapping Approaches Through Linkage Mapping in Plants

Biparental Crossing and QTL Mapping for Validation of Genome-Wide Association Studies

Association mapping (AM), also known as genome-wide association studies (GWAS), is increasingly being employed in crop plants for the identification of QTL/genes and marker-trait associations (MTAs) in natural populations. Large numbers of such associations have been identified for variety of traits in different crop plants. However, not many of these associations have been used practically in the crop improvement program due to lack of validation. Although there are different ways through which the results of AM/GWAS could be validated, the best approach is to develop a biparental population for the trait of interest. An overview of the steps involved in the validation of results of AM using biparental mapping population in plants is provided in this chapter.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

Fine Mapping of a Major Pleiotropic QTL Associated with Sesamin and Sesamolin Variation in Sesame (Sesamum indicum L.)

Deciphering the genetic basis of quantitative agronomic traits is a prerequisite for their improvement. Herein, we identified loci governing the main sesame lignans, sesamin and sesamolin variation in a recombinant inbred lines (RILs, F8) population under two environments. The content of the two lignans in the seeds was investigated by HPLC. The sesamin and sesamolin contents ranged from 0.33 to 7.52 mg/g and 0.36 to 2.70 mg/g, respectively. In total, we revealed 26 QTLs on a linkage map comprising 424 SSR markers, including 16 and 10 loci associated with sesamin and sesamolin variation, respectively. Among them, qSmin_11.1 and qSmol_11.1 detected in both the two environments explained 67.69% and 46.05% of the phenotypic variation of sesamin and sesamolin, respectively. Notably, qSmin11-1 and qSmol11-1 were located in the same interval of 127-127.21cM on LG11 between markers ZMM1776 and ZM918 and acted as a pleiotropic locus. Furthermore, two potential candidate genes (SIN_1005755 and SIN_1005756) at the same locus were identified based on comparative transcriptome analysis. Our results suggest the existence of a single gene of large effect that controls expression, both of sesamin and sesamolin, and provide genetic information for further investigation of the regulation of lignan biosynthesis in sesame.

Association Mapping in Plants

Quantitative trait loci mapping has become a common practice in crop plants and can be accomplished using either biparental populations following interval mapping or natural populations following the approach of association mapping. Because of its ability to use the natural diversity and to search for functional variants in a broader germplasm, association mapping is becoming popular among researchers. An overview of the different steps involved in association mapping in plants is provided in this chapter.