Login to MyKarger

New to MyKarger? Click here to sign up.

Login with Facebook

Forgot Password? Reset your password

Authors, Editors, Reviewers

For Manuscript Submission, Check or Review Login please go to Submission Websites List.

Submission Websites List

Institutional Login (Shibboleth)

For the academic login, please select your country in the dropdown list. You will be redirected to verify your credentials.

Table of Contents
Vol. 69, No. 4, 2010
Issue release date: April 2010
Section title: Original Paper
Free Access
Hum Hered 2010;69:219–228

Approaches for Evaluating Rare Polymorphisms in Genetic Association Studies

Li Q.a · Zhang H.b · Yu K.b
aKey Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; bDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Md., USA
email Corresponding Author

Kai Yu

Division of Cancer Epidemiology and Genetics

National Cancer Institute, National Institutes of Health

Bethesda, MD 20892 (USA)

Tel. +1 301 594 7206, Fax +1 301 402 0081, E-Mail yuka@mail.nih.gov

Do you have an account?

Login Information

Contact Information

I have read the Karger Terms and Conditions and agree.


Most current genetic association studies, including genome-wide association studies, look for the single nucleotide polymorphisms (SNPs) with a relatively large minor allele frequency (MAF) (e.g. >5%) in the search for genetic loci underlying the susceptibility for complex diseases. The strategy of focusing on common SNPs in genetic association studies is very effective under the common-disease-common-variant (CDCV) hypothesis, which claims that common diseases are caused by common variants that have relatively small to moderate effects. Although the CDCV hypothesis has become the dogma guiding the conduct of association studies over the past decade, growing evidence from recent empirical data and simulations suggests that the causal genetic polymorphisms, including SNPs and copy number variants (CNVs), for common diseases have a wide spectrum of MAFs, ranging from rare to common. Unlike the analysis for common genetic variants, statistical approaches for the analysis of rare variants receive very little attention. Methods developed for common variants usually rely on their asymptotic properties, which can be inaccurate for the study of the rare variants with limited sample size. Although Fisher’s exact test can be used for such a scenario, it is usually conservative and thus its usefulness is diminished to some extent. Here we propose two novel approaches for the analysis of rare genetic variants. Simulation studies and two real examples demonstrate the advantages of the proposed methods over the existing methods.

© 2010 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: June 25, 2009
Accepted: November 03, 2009
Published online: March 24, 2010
Issue release date: April 2010

Number of Print Pages: 10
Number of Figures: 4
Number of Tables: 0

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

Copyright / Drug Dosage / Disclaimer

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 government 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.