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Ultra-high-dimensional threshold selection for quantile feature screening with false discovery rate error rate
Saidat Abidemi Sanni1, Yan Yu2, Zhigen Zhao3
1Department of Operations and Analytics, The University of Texas at San Antonio, San Antonio, TX 78249, United States.
Biometrics
|February 26, 2026
Summary
This study introduces a new quantile mirror (QM) method for identifying genetic risk factors for high blood pressure. The approach adaptively selects thresholds, controlling false discovery rates (FDR) for better hypertension management.
Area of Science:
- Genetics
- Biostatistics
- Cardiovascular Disease Research
Background:
- High blood pressure (hypertension) is a prevalent condition requiring effective management.
- Identifying genetic risk factors is crucial for targeted interventions.
- Ultra-high-dimensional genetic data necessitates robust feature screening methods.
Purpose of the Study:
- To develop a data-adaptive threshold selection method for quantile feature screening.
- To control the false discovery rate (FDR) in identifying genetic risk factors for high blood pressure.
- To discover novel genetic contributors to abnormally high blood pressure levels.
Main Methods:
- Proposed a novel quantile mirror (QM) approach for data-adaptive threshold selection.
- Implemented false discovery rate (FDR) control within quantile feature screening.
- Introduced Quantile REflection via Data Splitting (QREDS) and Hard Threshold with QREDS procedures.
- Utilized multiple data splitting for enhanced result stability.
Main Results:
- The QM approach enables data-adaptive threshold selection and FDR estimation.
- Applied methods to Framingham Heart Study data, identifying known and novel genetic risk factors.
- Demonstrated asymptotic control of FDR under specified conditions.
- Extensive simulations confirmed the performance of the proposed methods.
Conclusions:
- The novel QM approach provides a stable and effective method for genetic feature screening in hypertension research.
- The proposed methods successfully identify significant genetic risk factors while controlling for false discoveries.
- This work contributes to a better understanding of the genetic architecture of high blood pressure.
Keywords:
FDR controlfeature screeninghigh blood pressurequantile REDSquantile mirror statisticultra-high-dimensional dataMore Related Videos
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