Plant Genetics and Molecular Biology

The rapidly evolving technologies can serve as a potential growth engine in agriculture as many of these technologies have revolutionized several industries in the recent past. The tremendous advancements in biotechnology methods, cost-effective sequencing technology, refinement of genomic tools, and standardization of modern genomics-assisted breeding methods hold great promise in taking the global agriculture to the next level through development of improved climate-smart seeds. These technologies can dramatically increase our capacity to understand the molecular basis of traits and utilize the available resources for accelerated development of stable high-yielding, nutritious, input-use efficient, and climate-smart crop varieties. This book aimed to document the monumental advances witnessed during the last decade in multiple fields of plant biotechnology such as genetics, structural and functional genomics, trait and gene discovery, transcriptomics, proteomics, metabolomics, epigenomics, nanotechnology, and analytical tools. This book will serve to update the scientific community, academicians, and other stakeholders in global agriculture on the rapid progress in various areas of agricultural biotechnology. This chapter provides a summary of the book, “Plant Genetics and Molecular Biology.”

DNA sequencing technologies have changed the face of biological research over the last 20 years. From reference genomes to population level resequencing studies, these technologies have made significant contributions to our understanding of plant biology and evolution. As the technologies have increased in power, the breadth and complexity of the questions that can be asked has increased. Along with this, the challenges of managing unprecedented quantities of sequence data are mounting. This chapter describes a few aspects of the journey so far and looks forward to what may lie ahead.

In the past decade, the application of high-throughput sequencing to crop genotyping has given rise to novel platforms capable of genotyping tens of thousands of genome-wide DNA markers. Coupled with the decreasing costs of sequencing, this rapid increase in markers allows accelerated and highly accurate genotyping of entire crop populations and diversity sets using single nucleotide polymorphisms (SNPs). These revolutionary advances accelerate crop improvement by facilitating a more precise connection of phenotype to genotype through association studies, linkage mapping and diversity analysis. The platforms driving the advances in genotyping are array technologies and genotyping by sequencing (GBS) methods, which include both low-coverage whole genome resequencing (skim sequencing) and reduced representation sequencing (RRS) approaches. Here, we outline and compare these genotyping platforms and provide a perspective on the promising future of crop genotyping. While SNP arrays provide high quality, simple handling, and unchallenging analysis, the lower cost of RRS and the greater data volume produced by skim sequencing suggest that use of GBS will become more prevalent in crop genomics as sequencing costs decrease and data analysis becomes more streamlined.

Quantitative trait loci (QTL) mapping in crop plants has now become a common practice due to the advances made in the area of molecular markers as well as that of statistical genomics. Consequently, large numbers of QTLs have been identified in different crops for a variety of traits. Several computational tools are now available that suit the type of mapping population and the trait(s) to be studied for QTL analyses as well as the objective of the program. These methods are comprised of simpler approaches like single marker analysis and simple interval mapping to relatively exhaustive inclusive composite interval mapping and Bayesian interval mapping. The relative significance of each of these methods varies considerably. The progress made in the area of computational analysis involving the identification of QTLs either through interval mapping or association mapping is unprecedented, and it is expected that it will continue to evolve over the coming years. An overview of the different methods of linkage-based QTL analysis is provided in this chapter.

Previously, association mapping (AM) methodology was used to unravel genetic complications in animal science by measuring the complex traits for candidate and non-candidate genes. Nowadays, this statistical approach is widely used to clarify the complexity in plant breeding program-based genome-wide breeding strategies, marker development, and diversity analysis. This chapter is particularly focused on methodologies with limitations and provides an overview of AM models and software used up to now. Association or linkage disequilibrium mapping has become a very popular method for discovering candidate and non-candidate genes and confirmation of quantitative trait loci (QTL) on various parts of the genome and in marker-assisted selection for breeding. Previously, various QTL investigations were carried out for different plants exclusively by linkage mapping. To help to understand the basics of modern molecular genetic techniques, in this chapter we summarize previous studies done on different crops. AM offers high-resolution power when there is large genotypic diversity and low linkage disequilibrium (LD) for the germplasm being investigated. The benefits of AM, compared with traditional QTL mapping, include a relatively detailed mapping resolution and a far less time-consuming approach since no mapping populations need to be generated. The advancements in genotyping and computational techniques have encouraged the use of AM. AM provides a fascinating approach for genetic investigation of QTLs, due to its resolution and the possibility to study the various genomic areas at the same time without construction of mapping populations. In this chapter we also discuss the advantages and disadvantages of AM, especially in the dicotyledonous crops Fabaceae and Solanaceae, with various genome-size reproductive strategies (clonal vs. sexual), and statistical models. The main objective of this chapter is to highlight the uses of association genetics in major and minor crop species that have trouble being analyzed for dissection of complex traits by identification of the factor responsible for controlling the effect of trait.

Fine mapping of quantitative trait loci (QTL) is the route to more detailed molecular characterization and functional studies of the relationship between polymorphism and trait variation. It is also of direct relevance to breeding since it makes QTL more easily integrated into marker-assisted breeding and into genomic selection. Fine mapping requires that marker-trait associations are tested in populations in which large numbers of recombinations have occurred. This can be achieved by increasing the size of mapping populations or by increasing the number of generations of crossing required to create the population. We review the factors affecting the precision and power of fine mapping experiments and describe some contemporary experimental approaches, focusing on the use of multi-parental or multi-founder populations such as the multi-parent advanced generation intercross (MAGIC) and nested association mapping (NAM). We favor approaches such as MAGIC since these focus explicitly on increasing the amount of recombination that occurs within the population. Whatever approaches are used, we believe the days of mapping QTL in small populations must come to an end. In our own work in MAGIC wheat populations, we started with a target of developing 1,000 lines per population: that number now looks to be on the low side.

Gene space: the final frontier in plant functional genomics. These are the voyages of TILLING, the reverse-genetics strategy that sought to boldly go where no-one had gone before by combining high-density chemical mutagenesis with high-throughput mutation discovery. Its 18-year mission has been to explore new technologies such as next generation sequencing and to seek out new strategies like in silico databases of catalogued EMS-induced mutations from entire mutant plant populations. This chapter is a clip show highlighting key milestones in the development of TILLING. Use of different technologies for the discovery of induced mutations, establishment of TILLING in different plant species, what has been learned about the effect of chemical mutagens on the plant genome, development of exome capture sequencing in wheat, and a look to the future of reverse-genetics with targeted genome editing are discussed.

The current global population of 7.3 billion is estimated to reach 9.7 billion in the year 2050. Rapid population growth is driving up global food demand. Additionally, global climate change, environmental degradation, drought, emerging diseases, and salty soils are the current threats to global food security. In order to mitigate the adverse effects of these diverse agricultural productivity constraints and enhance crop yield and stress-tolerance in plants, we need to go beyond traditional and molecular plant breeding. The powerful new tools for genome editing, Transcription Activator-Like Effector Nucleases (TALENs) and Clustered Regulatory Interspaced Short Palindromic Repeats (CRISPR)/Cas systems (CRISPR-Cas9), have been hailed as a quantum leap forward in the development of stress-resistant plants. Plant breeding techniques, however, have several drawbacks. Hence, identification of transcriptional regulatory elements and deciphering mechanisms underlying transcriptional regulation are crucial to avoiding unintended consequences in modified crop plants, which could ultimately have negative impacts on human health. RNA splicing as an essential regulated post-transcriptional process, alternative polyadenylation as an RNA-processing mechanism, along with non-coding RNAs (microRNAs, small interfering RNAs and long non-coding RNAs) have been identified as major players in gene regulation. In this chapter, we highlight new findings on the essential roles of alternative splicing and alternative polyadenylation in plant development and response to biotic and abiotic stresses. We also discuss biogenesis and the functions of microRNAs (miRNAs) and small interfering RNAs (siRNAs) in plants and recent advances in our knowledge of the roles of miRNAs and siRNAs in plant stress response.

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies.

The genetic material DNA in association with histone proteins forms the complex structure called chromatin, which is prone to undergo modification through certain epigenetic mechanisms including cytosine DNA methylation, histone modifications, and small RNA-mediated methylation. Alterations in chromatin structure lead to inaccessibility of genomic DNA to various regulatory proteins such as transcription factors, which eventually modulates gene expression. Advancements in high-throughput sequencing technologies have provided the opportunity to study the epigenetic mechanisms at genome-wide levels. Epigenomic studies using high-throughput technologies will widen the understanding of mechanisms as well as functions of regulatory pathways in plant genomes, which will further help in manipulating these pathways using genetic and biochemical approaches. This technology could be a potential research tool for displaying the systematic associations of genetic and epigenetic variations, especially in terms of cytosine methylation onto the genomic region in a specific cell or tissue. A comprehensive study of plant populations to correlate genotype to epigenotype and to phenotype, and also the study of methyl quantitative trait loci (QTL) or epiGWAS, is possible by using high-throughput sequencing methods, which will further accelerate molecular breeding programs for crop improvement.

The integration of nanotechnology in medicine has had a tremendous impact in the past few decades. The discovery of synthesis of nanomaterials (NMs) and their functions as versatile tools promoted various applications in nano-biotechnology and nanomedicine. Although the physical and chemical methods are still considered as commonly used methods, they introduce several drawbacks such as the use of toxic chemicals (solvent, reducing, and capping agents) and poor control of size, size distribution, and morphology, respectively. Additionally, the NMs synthesized in organic solvents and hydrophobic surfactants rapidly aggregate in aqueous solutions or under physiologic conditions, limiting their applications in medicine. Many of the phase-transfer strategies were developed and applied for the transfer of NMs into aqueous solutions. Although great efforts have been put into phase transfers, they mostly include expensive, time-consuming, intensive labor work, multi steps, and complicated procedures.

Use of plant extracts in the biological synthesis method offers stark advantages over other biomolecules (protein, enzyme, peptide, and DNA). Plant extracts have been commonly used for food, medicine, NM synthesis, and biosensing. There are many viable techniques developed for the production of plant extracts with various contents based on their simplicity, cost, and the type of extract content. In this chapter, we conduct a comparative study for extract preparation techniques, the use of extracts for metallic single and hybrid nanoparticle (NP) synthesis, and their antimicrobial properties against pathogenic and plant-based bacteria.

Agricultural disciplines are becoming data intensive and the agricultural research data generation technologies are becoming sophisticated and high throughput. On the one hand, high-throughput genotyping is generating petabytes of data; on the other hand, high-throughput phenotyping platforms are also generating data of similar magnitude. Under modern integrated crop breeding, scientists are working together by integrating genomic and phenomic data sets of huge data volumes on a routine basis. To manage such huge research data sets and use them appropriately in decision making, Data Management Analysis & Decision Support Tools (DMASTs) are a prerequisite. DMASTs are required for a range of operations including generating the correct breeding experiments, maintaining pedigrees, managing phenotypic data, storing and retrieving high-throughput genotypic data, performing analytics, including trial analysis, spatial adjustments, identifications of MTAs, predicting Genomic Breeding Values (GEBVs), and various selection indices. DMASTs are also a prerequisite for understanding trait dynamics, gene action, interactions, biology, GxE, and various other factors contributing to crop improvement programs by integrating data generated from various science streams. These tools have simplified scientists’ lives and empowered them in terms of data storage, data retrieval, data analytics, data visualization, and sharing with other researchers and collaborators. This chapter focuses on availability, uses, and gaps in present-day DMASTs.