Identification of influential rare variants in aggregate testing using random forest importance measures
Abstract
Aggregate tests of rare variants are often employed to identify associated regions compared to sequentially testing each individual variant. When an aggregate test is significant, it is of interest to identify which rare variants are “driving” the association. We recently developed the rare variant influential filtering tool (RIFT) to identify influential rare variants and showed RIFT had higher true positive rates compared to other published methods. Here we use importance measures from the standard random forest (RF) and variable importance weighted RF (vi‐RF) to identify influential variants. For very rare variants (minor allele frequency [MAF] TERT and FAM13A, respectively. In summary, the vi‐RF provides an improved, objective approach to identifying influential variants following a significant aggregate test. We have expanded our previously developed R package RIFT to include the random forest methods.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 23, 2023
- Source ID
- 10.1111/ahg.12509
Entities
People
- Carl D. Langefeld
- David A Schwartz
- Rachel Z. Blumhagen
- Tasha E. Fingerlin
Organizations
- Colorado School of Public Health
- National Heart, Lung, and Blood Institute
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Neurological Disorders and Stroke
- National Jewish Health
- United States Department of Defense
- University of Colorado
- University of Michigan Rogel Cancer Center
- Wake Forest School of Medicine