Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

International journal of molecular sciences - 25 Feb 2026

Gou Xiangjian, Fu Shaohong, Li Jiakun, Gong Wanzhuo, Wu Qiaobo, Li Yun, Yang Jin, Zou Qiong, Yu Qin, Tao Lanrong, Shi Haoran

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Genome-Wide Association Study
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Share to Quantitative Trait Loci

Share to Zea mays

Source

Open in PubMed Open full text

Identifiers

PMID: 41828352
DOI: 10.3390/ijms27052124
PMCID: PMC12984276

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

CQD: A Rapid and Accurate Tool for Identifying High-Confidence QTLs and Candidate Genes from GWAS Summary Statistics.PMID 41828352Read ↗

Genetic diversity, GWAS, and candidate genes identification for plant architecture traits in maize (Zea mays L.).Semantic proximity Read ↗

Genic and nongenic contributions to natural variation of quantitative traits in maize.Semantic proximity Read ↗

PICARA, an analytical pipeline providing probabilistic inference about a priori candidates genes underlying genome-wide association QTL in plants.Semantic proximity Read ↗

Joint-GWAS, Linkage Mapping, and Transcriptome Analysis to Reveal the Genetic Basis of Plant Architecture-Related Traits in Maize.Semantic proximity Read ↗

QTL mapping, validation, and candidate gene predicting for maize plant architecture traits via bulked segregant analysis plus linkage analysis with F₂ population.Semantic proximity Read ↗

An ultra-high density bin-map for rapid QTL mapping for tassel and ear architecture in a large F₂ maize population.Semantic proximity Read ↗

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.Semantic proximity Read ↗

Integrating GWAS, QTL Mapping, and RNA-seq to Identify Candidate Genes for Kernel Weight and Oil Traits in Maize (Zea mays ssp. mays).Semantic proximity Read ↗

COMPILE: a GWAS computational pipeline for gene discovery in complex genomes.Semantic proximity Read ↗

Gene expression and expression quantitative trait loci analyses uncover natural variations underlying the improvement of important agronomic traits during modern maize breeding.Semantic proximity Read ↗

Dissection of the Genetic Basis of Maize Plant Architecture and Candidate Gene Mining Based on the MAGIC Population.Semantic proximity Read ↗

Unveiling core genomic regions shaping plant architecture, productivity, and seed quality traits in sesame (Sesamum indicum L.): insights from Meta-QTL study into breeding targets.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.