Evaluating Methods for the Analysis of Rare Variants in Sequence Data
BioMed Central Ltd.
A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.
rare variant statistical methods, sequencing data, causal genes
Published in: BMC Proceedings, Volume 5, Issue 9, November 29, 2011, pages S119-. Copyright © 2011 BioMed Central Ltd.. The final published version is available at: http://www.biomedcentral.com/1753-6561/5/S9/S119
This work is funded by National Human Genome Research Institute grant R15HG004543. We wish to thank Scott DeClaire and Ben Boerema for their participation in early stages of this project. The Genetic Analysis Workshops are supported by National Institutes of Health grant R01 GM031575.
This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data