This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
New insights into old methods for identifying causal rare variants
1 Department of Information Systems, Business Statistics, and Operations Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
2 Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, USA
BMC Proceedings 2011, 5(Suppl 9):S50 doi:10.1186/1753-6561-5-S9-S50Published: 29 November 2011
The advance of high-throughput next-generation sequencing technology makes possible the analysis of rare variants. However, the investigation of rare variants in unrelated-individuals data sets faces the challenge of low power, and most methods circumvent the difficulty by using various collapsing procedures based on genes, pathways, or gene clusters. We suggest a new way to identify causal rare variants using the F-statistic and sliced inverse regression. The procedure is tested on the data set provided by the Genetic Analysis Workshop 17 (GAW17). After preliminary data reduction, we ranked markers according to their F-statistic values. Top-ranked markers were then subjected to sliced inverse regression, and those with higher absolute coefficients in the most significant sliced inverse regression direction were selected. The procedure yields good false discovery rates for the GAW17 data and thus is a promising method for future study on rare variants.