STATISTICAL FRAMEWORKS FOR INTEGRATIVE QTL AND GWAS ANALYSIS IN CROP IMPROVEMENT RESEARCH

Abstract

Background: Quantitative Trait Loci (QTL) mapping and Genome-Wide Association Studies (GWAS) are genomic approaches commonly used for identification of genetic regions associated with complex agronomic traits in crops. However, the single application of these methods is often limited by low mapping resolution, population structure bias and reduced statistical power.

Objective: The goal of this study is to evaluate the statistical frameworks that integrate QTL and GWAS approaches to improve the accuracy of marker detection and genomic prediction in crop improvement research.
Methods: The performance of integrative statistical models such as Mixed Linear Models (MLM), Bayesian approaches, multi-locus GWAS and meta-QTL analysis was evaluated with high-density SNP datasets and multi-environment phenotypic data from major cereal crops. Statistical analyses were performed using the platforms of TASSEL, GAPIT and R/qtl.

Findings: The integrated framework improved the efficiency of QTL detection by 28% and the accuracy of genomic prediction from 0.61 to 0.82, compared with the conventional single-model approaches. Yield, drought tolerance and disease resistance were repeatedly detected at several stable loci across environments.

Conclusion: Integrative statistical frameworks for QTL-GWAS analysis of complex traits for improved accuracy, robustness and biological insight to accelerate marker-assisted breeding and climate-smart crop development.