**2.8. Metagenomics**

species can be accessed by TILLING and EcoTILLING by sequencing. TILLING may lead to the development of functional markers for screening-associated traits through marker-assisted selection (MAS). The technique of TILLING using high-throughput mutation discovery has

A wide spectrum of mutation detection assays, ranging from heteroduplex analysis with highpressure liquid chromatography (HPLC), screening with labeled primers, electrophoresis, microarray, the use of fluorescent dye-labeled primers assayed on ABI genetic analyzer have been used. However, these methods are generally slow, costly, and labor intensive. Applica‐ tion of NGS has been shown to be a cost-effective mutation detection system by re-sequencing the gene of interest in mutagenized plants [90,91]. The availability of genome sequence enables the use of reverse genetic approaches to identify mutations in specific target genes, thereby accelerating the generation of novel phenotypes. Comparative genome analysis methods offer the opportunity to select target genes involved in biosynthetic pathways and networks of traits/phenotypes of economic importance. The use of multidimensional pooling of DNA samples enables screening of DNA pools for multiple independent mutations in any target gene using NGS, which provides a cost-effective assay. This has led to the discovery of rare mutations in rice and wheat, termed TILLING by sequencing [92], tef [93], and in animals [94]. Different sample pooling schemes for NGS, which further enhance the power of NGS in processing multiple samples in parallel have been developed [95]. In light of the rapidly evolving sequencing technology together with a plethora of sample pooling schemes, com‐ bined with bar coding, it is feasible and imperative to apply TILLING by sequencing to understudied crops of Africa. A direct application of NGS to detect mutant regions in a segregating population of rice has been demonstrated in a method called MutMap [96].

This section discusses how NGS can be used to enhance QTL analysis. Following the advent of first-generation molecular markers such as restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), and amplified fragment length polymorphism (AFLP), numerous studies in many crop species were launched to identify QTL, but for quantitative traits, affected by polygenes with small effects, limited success was attained in terms of application [97]. One of the explanations [98] for the limited exploitation of QTLs is the issues associated with the acquisition and summarizing of plethora of QTL

The rapid advance in next-generation sequencing technologies and the wide array of ultrahighthroughput and cost-effective genotyping platforms have created a multitude of new possi‐ bilities for QTL mapping using large early-generation populations and high-density markers. Variants of NGS-based QTL identification methods, such as X-QTL, MutMap, QTL-seq, SHOREmap, and NGM, have been reviewed elsewhere [99]. Among the various NGS-based QTL mapping approaches, QTL-seq, the whole genome re-sequencing-based mapping of QTL [100], can successfully be applied to dissect key quantitative traits underlying biotic and abiotic stresses in major African staple food crops such as cassava, yam, tef, and legumes. One of the essential requirements for QTL-seq is the availability of a quality reference genome and

already been applied successfully to more than 20 plant species [89].

296 Next Generation Sequencing - Advances, Applications and Challenges

**2.7. QTL identification**

information.

Metagenomics is the direct genetic analysis of genomes contained within an entire community of organisms such as a microbial community, and makes use of NGS technologies and bioinformatics tools [104]. The advent of metagenomics has revolutionized the study of microbial ecology, evolution, and diversity. In plant pathology and virology, metagenomics has contributed to the sequencing of genomes within infected plants and has led to the detection of many RNA and DNA viruses and/or viroids. Other areas of application include ecology and epidemiology as well as functional genomics of pathogens, and the cultureindependent analysis of a mixture of microbial genomes [8,105,106].

The application of metagenomics in crop improvement is discussed below in the disease diagnostics section as the majority of plant metagenomics studies, as applied to agriculture, relate to virology. However, there are substantial shotgun metagenome sequencing studies that investigate microbial communities in soil and plants and other environmental samples [105,107–109]. The challenges of analysis are being addressed gradually [55,104]. The analysis pipeline for metagenomics follows major steps such as raw data quality checking, filtering, assembly, taxonomic classification, abundance estimation, and relative quantification of taxons [53,54].

With growing experience in NGS data analysis and a fledging bioinformatics critical mass, IITA and partners are moving toward the application of meta-omics (-genomics, -transcrip‐ tomics, and -proteomics). In the context of African agriculture, the rapidly evolving field of metagenomics will have a significant impact in revealing the diversity of microorganisms, and in describing the relationship between host-associated microbial communities and host phenotype. The declining cost of sequencing and the associated analytical tools will likely create the opportunity to develop cost-effective and efficient diagnostic kits to address the challenge of multiple infections (pathogenic races and strains) in the major crops such as cassava [110], banana [111], and yams [112]. Survey of the incidence and distribution of viruses infecting these crops makes it one of the important tools for understanding the microbial genetics, physiology, and community ecology. The benefit of metagenomics extends to agriculturally important microbes, both disease causing and beneficial, in plant and animal production.
