Journal Mobile Options
Table of Contents
Vol. 76, No. 2, 2013
Issue release date: April 2014
Hum Hered 2013;76:64-75
(DOI:10.1159/000357567)

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

Freytag S. · Manitz J. · Schlather M. · Kneib T. · Amos C.I. · Risch A. · Chang-Claude J. · Heinrich J. · Bickeböller H.
aInstitute of Genetic Epidemiology, Medical School, bDepartment of Statistics and Econometrics, and cCenter for Statistics, Georg-August University Göttingen, Göttingen, and dInstitute for Mathematics, University of Mannheim, Mannheim, Germany; eDepartment of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, N.H., USA; fDivision of Epigenomics and Cancer Risk Factors, Translational Lung Research Center Heidelberg, German Cancer Research Center, gTranslational Lung Research Center Heidelberg, Member of the German Center for Lung Research, and hDivision of Cancer Epidemiology, German Cancer Research Center, Heidelberg, and iInstitute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany

Individual Users: Register with Karger Login Information

Please create your User ID & Password





Contact Information











I have read the Karger Terms and Conditions and agree.

To view the fulltext, please log in

To view the pdf, please log in

Abstract

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. © 2014 S. Karger AG, Basel



Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.


Pay-per-View Options
Direct payment This item at the regular price: USD 38.00
Payment from account With a Karger Pay-per-View account (down payment USD 150) you profit from a special rate for this and other single items.
This item at the discounted price: USD 26.50