PCA outperforms popular hidden variable inference methods for molecular QTL mapping.

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genomics

Background:

• Hidden variable estimation is crucial for improving power in molecular quantitative trait locus (QTL) analysis.
• Existing methods for hidden variable inference lack comprehensive benchmarking.
• Evaluating the efficacy of various hidden variable inference techniques is essential for advancing QTL studies.

Purpose of the Study:

• To benchmark popular hidden variable inference methods against Principal Component Analysis (PCA) in QTL analysis.
• To assess the performance, speed, and interpretability of different hidden variable inference approaches.
• To provide practical tools and guidance for researchers to implement PCA in QTL mapping.

Main Methods:

• Benchmarking of Surrogate Variable Analysis (SVA), Probabilistic Estimation of Expression Residuals (PEER), and Hidden Covariates with Prior (HCP) against PCA.
• Utilized 362 synthetic and 110 real datasets for comprehensive evaluation.
• Developed an R package, PCAForQTL, for practical application.

Main Results:

• PCA demonstrates superior performance, speed, and ease of use compared to SVA, PEER, and HCP.
• PCA is the underlying statistical methodology for many popular hidden variable inference methods.
• The study provides empirical evidence supporting PCA's advantages in QTL analysis.

Conclusions:

• PCA offers a more effective, transparent, and reproducible approach to hidden variable inference in QTL mapping.
• The PCAForQTL R package and guide facilitate the adoption of PCA in genetic research.
• Implementing PCA can significantly enhance the accuracy and efficiency of QTL identification.