Detecting case-control expression quantitative trait loci using locally most powerful or maximin robust rank tests

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genome-wide gene expression quantitative trait loci (eQTL) analysis is crucial for understanding genetic influences on gene expression.
• Efficient and robust statistical methods are needed for analyzing large-scale eQTL data, particularly in case-control studies.
• Existing methods may lack robustness or computational efficiency for complex genetic data.

Purpose of the Study:

• To propose and evaluate robust statistical methods for genome-wide eQTL analysis in case-control settings.
• To enhance the efficiency and reliability of identifying genetic variants associated with gene expression.
• To provide computationally convenient tools for genetic researchers.

Main Methods:

• A modified locally most powerful rank test is proposed for case-control expression data.
• The proposed test is computationally simple, robust to non-normal expression data, and asymptotically locally most powerful.
• Gastwirth's maximin efficiency robust rank test is applied when location distribution forms cannot be specified, optimizing worst-case efficiency.

Main Results:

• Simulation studies demonstrated the performance of the proposed rank tests.
• The modified rank test shows robustness and high power for eQTL detection.
• Application to real data illustrates the practical utility of the methods.

Conclusions:

• The modified locally most powerful rank test and Gastwirth's maximin efficiency robust rank test offer efficient and robust solutions for eQTL analysis.
• These methods are valuable for identifying genetic associations with gene expression in case-control studies.
• The proposed statistical approaches enhance the analysis of genetic variations impacting gene expression patterns.