Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

See all formats and pricing
More options …
Volume 6, Issue 1


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates

Nathan L Tintle / Derek Gordon / Francis J McMahon / Stephen J Finch
Published Online: 2007-02-05 | DOI: https://doi.org/10.2202/1544-6115.1251

Although researchers use duplicate genotyped data to calculate an inconsistency rate, there is no power analysis to assess the value of the duplicate data. In this paper, we present a model in which the genotyping error rate is related to the inconsistency rate. We extend the g genotype by h phenotype chi-squared test to incorporate the duplicate genotyped data. When a subject is inconsistently genotyped (that is, has two observed genotypes), our procedure is to allocate 0.5 units to each of the two genotypes. We specify the multivariate analysis of variance (MANOVA) test comparing these extended counts. We provide freely available software for this test and also for a permutation test used on small samples. A simulation study shows that the asymptotic null distribution of the MANOVA test holds when the total number of subjects, N, is at least 300. We also document with a simulation study that the asymptotic distribution of this test under various alternative hypotheses is a satisfactory approximation to the simulated power. In all cases, the power of the MANOVA test using the duplicate genotyped data is greater than the power of the chi-squared test ignoring the duplicate data. Power increases ranged from 0.776% to 4.652% for 80% powered tests and 0.292% to 2.028% for 95% powered tests. Researchers now can compute the value of the duplicate genotyped data as part of the design of the study.

Keywords: genotype error; misclassification; test of association; case-control; re-genotype; inconsistency rate; duplicate genotype; whole genome association; genome wide association

About the article

Published Online: 2007-02-05

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 6, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1251.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Derek Gordon, Chad Haynes, Yaning Yang, Patricia L. Kramer, and Stephen J. Finch
Genetic Epidemiology, 2007, Volume 31, Number 8, Page 853
Nathan Tintle, Derek Gordon, Dirk Van Bruggen, and Stephen Finch
Annals of Human Genetics, 2009, Volume 73, Number 3, Page 370
Y. Zuo, G. Zou, J. Wang, H. Zhao, and H. Liang
Annals of Human Genetics, 2008, Volume 72, Number 3, Page 375
Anna Pluzhnikov, Jennifer E. Below, Anuar Konkashbaev, Anna Tikhomirov, Emily Kistner-Griffin, Cheryl A. Roe, Dan L. Nicolae, and Nancy J. Cox
The American Journal of Human Genetics, 2010, Volume 87, Number 1, Page 123
Morgan Mayer-Jochimsen, Shannon Fast, Nathan L. Tintle, and Zhaoxia Yu
PLoS ONE, 2013, Volume 8, Number 3, Page e56626
Qingyu Chen, Justin Zobel, and Karin Verspoor
Database, 2017, Volume 2017, Page baw163
Airat Bekmetjev, Dirk VanBruggen, Brian McLellan, Benjamin DeWinkle, Eric Lunderberg, Nathan Tintle, and Zheng Su
PLoS ONE, 2012, Volume 7, Number 2, Page e32058
Øystein A. Haaland and Hans J. Skaug
Statistics & Probability Letters, 2013, Volume 83, Number 3, Page 812
Brooke L. Fridley, Stephen T. Turner, Arlene B. Chapman, Andrei S. Rodin, Eric Boerwinkle, and Kent R. Bailey
Computational Statistics & Data Analysis, 2008, Volume 52, Number 12, Page 5367

Comments (0)

Please log in or register to comment.
Log in