The article discusses the use of expression quantitative trait loci (eQTL) mapping to understand the genetic basis of complex diseases. eQTLs are genetic variants that influence gene expression levels. By combining genome-wide association studies (GWAS) with global gene expression data, researchers can identify eQTLs that may explain the genetic factors underlying disease susceptibility. This approach helps to interpret GWAS results by linking genetic markers to gene expression levels, potentially identifying genes involved in disease pathogenesis. eQTL mapping has been applied to various organisms, including yeast, humans, animals, and plants, and has provided insights into the biological mechanisms of disease. The study highlights the importance of eQTL mapping in understanding the genetic architecture of complex diseases, as well as the challenges and limitations of current methods. The article also discusses the potential of eQTL data in identifying candidate genes for disease susceptibility, and the role of epigenetic factors in gene expression. The use of eQTL data in conjunction with GWAS has shown promise in identifying genetic variants associated with disease, and the integration of eQTL and GWAS data is expected to enhance the understanding of complex diseases. The article also addresses the limitations of eQTL mapping, including the challenges of interpreting results from different microarray platforms and the need for further research to improve the accuracy and reliability of eQTL data. Overall, the study emphasizes the importance of eQTL mapping in advancing our understanding of the genetic basis of complex diseases and the potential of this approach in future research.The article discusses the use of expression quantitative trait loci (eQTL) mapping to understand the genetic basis of complex diseases. eQTLs are genetic variants that influence gene expression levels. By combining genome-wide association studies (GWAS) with global gene expression data, researchers can identify eQTLs that may explain the genetic factors underlying disease susceptibility. This approach helps to interpret GWAS results by linking genetic markers to gene expression levels, potentially identifying genes involved in disease pathogenesis. eQTL mapping has been applied to various organisms, including yeast, humans, animals, and plants, and has provided insights into the biological mechanisms of disease. The study highlights the importance of eQTL mapping in understanding the genetic architecture of complex diseases, as well as the challenges and limitations of current methods. The article also discusses the potential of eQTL data in identifying candidate genes for disease susceptibility, and the role of epigenetic factors in gene expression. The use of eQTL data in conjunction with GWAS has shown promise in identifying genetic variants associated with disease, and the integration of eQTL and GWAS data is expected to enhance the understanding of complex diseases. The article also addresses the limitations of eQTL mapping, including the challenges of interpreting results from different microarray platforms and the need for further research to improve the accuracy and reliability of eQTL data. Overall, the study emphasizes the importance of eQTL mapping in advancing our understanding of the genetic basis of complex diseases and the potential of this approach in future research.