The chapter discusses the mapping of complex disease traits using global gene expression data, focusing on the identification of expression Quantitative Trait Loci (eQTLs). eQTLs are genetic factors that influence individual differences in gene expression levels, which can provide insights into the biological basis of disease associations identified through genome-wide association studies (GWAS). The authors highlight the importance of eQTL mapping in understanding the functional effects of DNA polymorphisms on multifactorial diseases, such as inflammatory bowel disease and eczema. They explain how eQTL mapping combines genetic association studies with global gene expression measurements to identify causal variants and their functional consequences. The chapter also addresses the limitations of current eQTL maps, such as the underrepresentation of trans effects and the need for larger sample sizes to detect weak trans effects. Additionally, it discusses the potential of new technologies, international efforts, and the use of tissue-specific samples to enhance eQTL studies. The authors emphasize the importance of integrating eQTL data with other biological information, such as epigenetic modifications and regulatory networks, to gain deeper insights into disease mechanisms. Finally, they outline future directions, including the use of exon arrays, RNA sequencing, and network analyses to improve eQTL mapping and disease genetics.The chapter discusses the mapping of complex disease traits using global gene expression data, focusing on the identification of expression Quantitative Trait Loci (eQTLs). eQTLs are genetic factors that influence individual differences in gene expression levels, which can provide insights into the biological basis of disease associations identified through genome-wide association studies (GWAS). The authors highlight the importance of eQTL mapping in understanding the functional effects of DNA polymorphisms on multifactorial diseases, such as inflammatory bowel disease and eczema. They explain how eQTL mapping combines genetic association studies with global gene expression measurements to identify causal variants and their functional consequences. The chapter also addresses the limitations of current eQTL maps, such as the underrepresentation of trans effects and the need for larger sample sizes to detect weak trans effects. Additionally, it discusses the potential of new technologies, international efforts, and the use of tissue-specific samples to enhance eQTL studies. The authors emphasize the importance of integrating eQTL data with other biological information, such as epigenetic modifications and regulatory networks, to gain deeper insights into disease mechanisms. Finally, they outline future directions, including the use of exon arrays, RNA sequencing, and network analyses to improve eQTL mapping and disease genetics.