CQD: A Rapid and Accurate Tool for Identifying High-Confidence QTLs and Candidate Genes from GWAS Summary Statistics

TL;DR

CQD is a new tool that quickly and accurately identifies high-confidence QTLs and candidate genes from GWAS data in crops.

Contribution

CQD introduces a novel framework for extracting robust QTLs from GWAS summary statistics in crop species.

Findings

CQD identified 108 high-confidence QTLs from 4179 significant GWAS signals in maize.

The detected QTLs included well-characterized genes like NS2, DRL1, and ZmACS6.

CQD offers an efficient and reliable method for genetic improvement and functional genomics in crops.

Abstract

Genome-wide association studies (GWAS) are a powerful approach for elucidating the genetic architecture of complex traits. The widespread application of GWAS across diverse crop species has generated vast amounts of GWAS summary statistics, creating a pressing need for effective tools to identify high-confidence quantitative trait loci (QTLs) from these data. Here, we present Candidate QTL Detector (CQD), a tool that enables the rapid and accurate identification of confident QTL regions from GWAS summary statistics. To evaluate the performance of CQD, we reanalyzed 64 phenotypic traits from a previously published maize diversity panel. CQD identified 108 high-confidence QTLs from 4179 significant GWAS signals, and these QTLs harbored several well-characterized genes, including NS2 (a regulator of tassel branch number), DRL1 (a key regulator of plant architecture), and ZmACS6 (an ACC synthase involved in ethylene biosynthesis), demonstrating the reliability of the QTLs detected by CQD. Overall, CQD provides an efficient and flexible framework for extracting robust QTLs from GWAS summary statistics, thereby facilitating the genetic improvement of complex traits and advancing crop functional genomics.

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