Application of family-based tests of association for rare variants to pathways

BMC Proceedings

Proceedings

Carolina Alvarez¹  Brian Greco²  Andrew Beck³  Nathan L Tintle⁴  Allison Hainline⁵  Alexander Luedtke⁶ 

[1] Department of Biostatistics, Florida International University, 11200 SW 8th St., 33199, Miami, FL, USA;Department of Mathematics and Statistics, Grinnell College, 1115 8th Ave, 50112, Grinnell, IA, USA;Department of Mathematics, Loyola University Chicago, 1052 W Loyola Ave, 60660, Chicago, IL, USA;Department of Mathematics, Statistics and Computer Science, Dordt College, 498 4th Ave. NE, 51250, Sioux Center, IA, USA;Department of Statistics, Baylor University, 1511 S. 5th St, 76798, Waco, TX, USA;Division of Biostatistics, UC Berkeley, 367 Evans Hall, 94720, Berkeley, CA, USA;

关键词: Rare Variant;    Null Distribution;    Causal Variant;    Shrinkage Estimator;    Functional Principal Component Analysis;   

DOI  :  10.1186/1753-6561-8-S1-S105

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Greco et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.