Introduction

Quantitative trait loci (QTL) analysis is a powerful approach to investigating the regulatory effect of genetic variants on the quantitative phenotype. But traditional regression based QTL analyses can only obtain a global overall effect in a specific group. For samples from case and control groups, the interpretation of QTL results is still limited to simple comparisons, which is not accurate and objective enough. Instead, a standard hypothesis testing method can definitely make it. However, it is a big challenge how to individualize the overall regression based QTL effect as individual QTL regulatory effect data of each single sample is necessary for hypothesis testing.

Therefore, we first proposed an individualized QTL index (IQI) that represents the degree of regulation between genetic variants and the trait for each individual. Based on IQI, Differential QTL (DQ) analysis framework was presented to accurately detect the case-control changes in more detail. According to DQ framework, regulatory effect differences were divided into five typical significant types: ROE (Reversal of QTL Effect), GOE (Gain of QTL Effect), LOE (Loss of QTL Effect), SOE (Strengthening of QTL Effect), and WOE (Weakening of QTL Effect). In addition, IQI can be a new feature/biomarker applied to various accurate bioinformatics analysis, such as differential analysis, disease prediction, and survival analysis. Overall, IQI affords a new perspective on pathogenic mechanisms and will facilitate the study of genetic regulation more detailedly and precisely.