Identification of influential rare variants in aggregate testing using random forest importance measures Article Swipe

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Rachel Z. Blumhagen , David A. Schwartz , Carl D. Langefeld , Tasha E. Fingerlin ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1111/ahg.12509

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] < 0.001), the vi‐RF:Accuracy method had the highest median true positive rate (TPR = 0.24; interquartile range [IQR]: 0.13, 0.42) followed by the RF:Accuracy method (TPR = 0.16; IQR: 0.07, 0.33) and both were superior to RIFT (TPR = 0.05; IQR: 0.02, 0.15). Among uncommon variants (0.001 < MAF < 0.03), the RF methods had higher true positive rates than RIFT while observing comparable false positive rates. Finally, we applied the RF methods to a targeted resequencing study in idiopathic pulmonary fibrosis (IPF), in which the vi‐RF approach identified eight and seven variants in 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.

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Computational Biology

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Random forest Interquartile range Medicine Aggregate (composite) Minor allele frequency Mixed model Internal medicine Statistics Computational biology Biology Allele Genetics Mathematics Allele frequency Computer science Materials science Nanotechnology Gene Machine learning

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Type: article
Language: en
Landing Page: https://doi.org/10.1111/ahg.12509
PDF: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ahg.12509
OA Status: hybrid
Cited By: 1
References: 36
Related Works: 10
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DOI: https://doi.org/10.1111/ahg.12509

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Title: Identification of influential rare variants in aggregate testing using random forest importance measures

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Language: en

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Publication year: 2023

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Publication date: 2023-05-23

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Authors: Rachel Z. Blumhagen, David A. Schwartz, Carl D. Langefeld, Tasha E. Fingerlin

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Landing page: https://doi.org/10.1111/ahg.12509

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Concepts: Random forest, Interquartile range, Medicine, Aggregate (composite), Minor allele frequency, Mixed model, Internal medicine, Statistics, Computational biology, Biology, Allele, Genetics, Mathematics, Allele frequency, Computer science, Materials science, Nanotechnology, Gene, Machine learning

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Cited by: 1

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