iBMQ

The "iBMQ" software tool introduces an approach to the analysis of expression quantitative trait loci (eQTL) mapping studies, which consider expression levels of thousands of genes as quantitative traits to delve deeper into gene regulation biology. Traditional methods for eQTL detection, which typically examine one gene at a time, often overlook the complex interactions between genes. While various methods have been proposed to address these limitations, each has its drawbacks.

iBMQ stands out by employing an integrated hierarchical Bayesian model that simultaneously models all genes and SNPs, facilitating the detection of eQTLs. This method is particularly tailored to manage scenarios characterized by a large number of gene expressions (G), a substantial number of genetic markers (S), and a relatively small number of individuals (n), a paradigm referred to as "large G, large S, small n."

The key advantages of iBMQ include its ability to handle the high dimensionality inherent in eQTL data, leverage information across all gene expression data to improve mapping accuracy, and maintain control over the false positive rate.