Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Evaluating methods for the analysis of rare variants in sequence data

Alexander Luedtke1, Scott Powers2, Ashley Petersen3, Alexandra Sitarik4, Airat Bekmetjev5 and Nathan L Tintle5*

Author Affiliations

1 Division of Applied Mathematics, Brown University, 182 George Street, Providence, RI 02912, USA

2 Department of Statistics and Operations Research, 318 Hanes Hall, CB 3260, University of North Carolina, Chapel Hill, NC 27599-3260, USA

3 Departments of Mathematics, Computer Science, and Statistics, St. Olaf College, 1520 St. Olaf Avenue, Northfield, MN 55057, USA

4 Department of Mathematics, Wittenberg University, 200 West Ward Street, PO Box 720, Springfield, OH 45501, USA

5 Department of Mathematics, Computer Science and Statistics, Dordt College, 498 4th Ave NE, Sioux Center, IA 51250, USA

For all author emails, please log on.

BMC Proceedings 2011, 5(Suppl 9):S119  doi:10.1186/1753-6561-5-S9-S119

Published: 29 November 2011

Abstract

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.