QUAIL (quantile integral linear model) is a quantile regression-based framework to estimate genetic effects on the variance of quantitative traits. QUAIL can be used in
- Genome-wide vQTL analysis - QUAIL constructs a quantile integral phenotype which aggregates information from all quantile levels, and only requires fitting two linear regressions per SNP in genome-wide analysis.
- Evaluating the vPGS performance - QUAIL can be extended to continuous predictors such as vPGS and quantify the performance of vPGS in predicting the phenotypic variability.
QUAIL can be downloaded via git clone https://github.com/qlu-lab/QUAIL
Please see the wiki for the short tutorials describing the two basic functions (Genome-wide vQTL analysis and Evaluating the vPGS performance), as well as the detailed manual of QUAIL.
- Mar 14, 2023: Speed up the step2 and move the tutorials into wiki.
- Aug 22, 2022: Add the simulation codes.
- Feb 25, 2022: Add the dispersion effects.
- Jan 13, 2022: Add the test data part.
- Apr 13, 2021: Initial release. Release the codes for Genome-wide vQTL analysis and evaluating the vPGS performance.
If you use QUAIL, please cite
Miao, J., Lin, Y., Wu, Y., Zheng, B., Schmitz, L. L., Fletcher, J. M., & Lu, Q. (2022). A quantile integral linear model to quantify genetic effects on phenotypic variability. Proceedings of the National Academy of Sciences, 119(39), e2212959119. https://doi.org/doi:10.1073/pnas.2212959119
For questions and comments, please open a GitHub issue or contact Jiacheng Miao at jmiao24@wisc.edu.
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