hortis Horticultural Studies 2717-882X ANTALYA BATI AKDENİZ AGRICULTURAL RESEARCH INSTITUTE 10.16882/hortis.1538865 Agricultural Engineering (Other) Ziraat Mühendisliği (Diğer) QTL-Seq: Rapid, Cost-Effective, and Reliable Method for QTL Identification https://orcid.org/0000-0001-5296-2939 Topcu Yasin Batı Akdeniz Agricultural Research Institute https://orcid.org/0000-0002-9987-1193 Sapkota Manoj Cornell University Cornell Institute of Biotechnology https://orcid.org/0000-0001-6513-3005 Aydın Serkan Batı Akdeniz Agricultural Research Institute 11 25 2024 41 3 106 115 05 28 2024 08 26 2024 Copyright © 2020, Horticultural Studies 2020 Horticultural Studies

QTL-seq is a powerful method that integrates whole-genome sequencing (WGS) with bulk-segregant analysis to rapidly and reliably identify quantitative trait loci (QTLs) associated with specific traits. This approach significantly advances traditional QTL mapping by eliminating the need for genome wide DNA markers such as SSR, RFLP, and INDELs, which are typically used in linkage-based QTL mapping. Instead, QTL-seq leverages WGS to detect all genetic variations such as SNPs, Indels, and Structural Variants across the entire genome, providing a comprehensive resource for marker development in marker-assisted selection. The QTL-seq process begins with the creation of genetically diverse mapping populations, such as F2 or RILs, followed by detailed phenotypic characterization. DNA from plants exhibiting similar phenotypes is pooled into bulk groups and sequenced, allowing for cost-effective and efficient QTL identification. Identified QTLs can be further validated through fine mapping using recombinant screenings and progeny testing, leading to the identification of candidate genes associated with traits of interest. In this study, we outline a user-friendly QTL-seq pipeline, from sequencing to data visualization to demonstrate its practical application. While the manuscript primarily focuses on describing the pipeline, we also conducted a case study analysis with real data to showcase its effectiveness. Our work contributes to the broader understanding of QTL-seq applications and offers practical recommendations for optimizing this method in future breeding programs.

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