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Introduction to GWAS and MutMap for identification of genes/QTL using next-generation sequencing

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Abstract

Most of the morphological and physiological traits including macronutrient use efficiency are quantitative and are controlled by many quantitative trait loci (QTL) working together. So far, QTL analysis is one of the most powerful tools to identify QTL. However, classical QTL analysis is costly, laborious, and time consuming. Here we introduce two adaptive progressive approaches, the genome-wide association study (GWAS) and MutMap, based on next-generation sequencing (NGS), and discuss the experimental designs for NGS-based analysis. GWAS and MutMap are the most powerful approaches for identification of the causal genes/QTL underlying complex traits. These approaches will accelerate the understanding of the molecular mechanisms and breeding selection for increasing macronutrient use efficiency.

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