Document Type

Conference Proceeding

Publication Date

6-17-2014

Department

Mathematics, Statistics, and Computer Science

Keywords

single-nucleotide variants, analysis, single-marker approaches, multiple-marker approaches

Abstract

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard “gene-based” test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (>200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches.

Comments

From Genetic Analysis Workshop 18, Stevenson, WA, USA. 13-17 October 2012.

Source Publication Title

BMC Proceedings

Volume

8

Issue

Supplement 1

First Page

S105

DOI

10.1186/1753-6561-8-S1-S105

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