Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

BMC Proceedings

Proceedings

Yauheniya Cherkas¹  Marsha A Wilcox¹  Frank DeFalco²  Nandini Raghavan³  Stephan Francke⁴ 

[1] Epidemiology, Johnson & Johnson, 1125 Trenton-Harbourton Road, 08560, Titusville, NJ, USA;Informatics, Johnson & Johnson, 920 Route 202, 08869, Raritan, NJ, USA;Non-Clinical Biostatistics, Johnson & Johnson, OMP Building, 1000 Route 202-S, 08869, Raritan, NJ, USA;Pharmacogenomics, Johnson & Johnson PRD, PO Box 300, 1000 Route 202, 08869, Raritan, NJ, USA;

关键词: Vascular Endothelial Growth Factor;    Rare Variant;    Ingenuity Pathway Analysis;    Genetic Analysis Workshop;    Vascular Endothelial Growth Factor Pathway;   

DOI  :  10.1186/1753-6561-5-S9-S94

来源: Springer

【 摘 要 】

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.

【 授权许可】

Unknown   
© Cherkas et al; licensee BioMed Central Ltd. 2011. 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.