Human Heredity

Original Paper

Identification of Influential Variants in Significant Aggregate Rare Variant Tests

Blumhagen R.Z.a,b · Schwartz D.A.c · Langefeld C.D.d,e,f · Fingerlin T.E.a,b,c

Author affiliations

aCenter for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
bDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
cSchool of Medicine, University of Colorado, Aurora, CO, USA
dDepartment of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
eComprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA
fCenter for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA

Keywords: Rare variantsLocalizationGenetic association testsIdiopathic pulmonary fibrosis

Related Articles for ""
Hum Hered 2020;85:11–23
https://doi.org/10.1159/000513290

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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: May 04, 2020
Accepted: November 19, 2020
Published online: February 10, 2021
Issue release date: March 2021

Number of Print Pages: 13
Number of Figures: 7
Number of Tables: 2

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

Abstract

Introduction: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. Methods: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). Results: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. Discussion: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

© 2021 S. Karger AG, Basel



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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: May 04, 2020
Accepted: November 19, 2020
Published online: February 10, 2021
Issue release date: March 2021

Number of Print Pages: 13
Number of Figures: 7
Number of Tables: 2

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

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