**2.7. QTL identification**

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 information.

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 mapping populations. The technique has been applied to rice where the whole genomes of two pooled rice DNA samples with contrasting phenotypes each in F2 and recombinant inbred line (RIL) populations were re-sequenced, after which the short reads were aligned to the reference sequence to calculate an SNP index. QTL were declared at positions where the SNP were different from the reference and had an SNP index value of 1. The analysis uses careful filtering of spurious SNPs. Conventional QTL mapping verified the candidate QTLs detected by the QTL-seq, and the method was validated by simulation analysis. QTL-seq has also been used in cucumber to map a QTL involved in flowering trait [101]. Likewise, the deployment of QTL-seq for rapid identification and fine mapping of QTLs was reported in chickpea [102] and sorghum [103].

In IITA, there are ongoing projects aiming to apply this technique to mapping of QTLs controlling disease resistance (e.g., anthracnose and yam mosaic virus), as well as root quality traits such as starch content. In cassava, the approach of genome-wide association study (GWAS) and conventional QTL mapping in F1 populations is being pursued to identify markers associated with key traits, including yield, dry matter, quality, and resistance to disease.
