1. ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data.
- Author
-
Asimit JL, Day-Williams AG, Morris AP, and Zeggini E
- Subjects
- Alleles, Computer Simulation, Genotype, Humans, Logistic Models, Genetic Association Studies, Genetic Variation, Software
- Abstract
Objectives: There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method., Methods: Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software., Results: The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario., Conclusions: The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores., (Copyright © 2012 S. Karger AG, Basel.)
- Published
- 2012
- Full Text
- View/download PDF