Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Morris, N. Kaplan (2002)
On the advantage of haplotype analysis in the presence of multiple disease susceptibility allelesGenetic Epidemiology, 23
J. Hoh, Anja Wille, J. Ott (2001)
Trimming, weighting, and grouping SNPs in human case-control association studies.Genome research, 11 12
L. Lazzeroni, K. Lange (1998)
A Conditional Inference Framework for Extending the Transmission/Disequilibrium TestHuman Heredity, 48
R. Penrose (1955)
A generalized inverse for matrices, 51
R. Straub, Yuxin Jiang, C. Maclean, Yunlong Ma, B. Webb, M. Myakishev, C. Harris-Kerr, B. Wormley, H. Sadek, B. Kadambi, Anthony Cesare, Avi Gibberman, Xu Wang, F. O’Neill, D. Walsh, K. Kendler (2002)
Genetic variation in the 6p22.3 gene DTNBP1, the human ortholog of the mouse dysbindin gene, is associated with schizophrenia.American journal of human genetics, 71 2
D. Schaid (2002)
Relative efficiency of ambiguous vs. directly measured haplotype frequenciesGenetic Epidemiology, 23
Electronic Database Information URLs for data presented herein are as follows: FAMHAP: Haplotype Frequency Estimation
A. Bogaert, J. Schumacher, T. Schulze, Andreas Otte, S. Ohlraun, S. Kovalenko, T. Becker, J. Freudenberg, E. Jönsson, M. Mattila‐Evenden, G. Sedvall, P. Czerski, P. Kapelski, J. Hauser, W. Maier, M. Rietschel, P. Propping, M. Nöthen, S. Cichon (2003)
The DTNBP1 (dysbindin) gene contributes to schizophrenia, depending on family history of the disease.American journal of human genetics, 73 6
F. Dudbridge (2003)
Pedigree disequilibrium tests for multilocus haplotypesGenetic Epidemiology, 25
N. Kaplan, R. Morris (2001)
Issues concerning association studies for fine mapping a susceptibility gene for a complex diseaseGenetic Epidemiology, 20
L. M. McIntyre, E. R. Martin, K. L. Simonsen, N. L. Kaplan (2000)
Circumventing Multiple Testing: A Multilocus Monte Carlo Approach for Association, 19
Yongchao Ge (2003)
Resampling-based Multiple Testing for Microarray Data Analysis
M. Martín, P. Westfall, S. Young (1993)
Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment
B. Manly (1997)
Randomization, Bootstrap and Monte Carlo Methods in Biology
S. Schwab, Michael Knapp, Stephanie Mondabon, Joachim Hallmayer, M. Borrmann-Hassenbach, Margot Albus, Bernard Lerer, M. Rietschel, M. Trixler, Wolfgang Maier, D. Wildenauer (2003)
Support for association of schizophrenia with genetic variation in the 6p22.3 gene, dysbindin, in sib-pair families with linkage and in an additional sample of triad families.American journal of human genetics, 72 1
T. Becker, M. Knapp (2004)
A powerful strategy to account for multiple testing in the context of haplotype analysis.American journal of human genetics, 75 4
T. Becker, M. Knapp (2002)
Efficiency of Haplotype Frequency Estimation when Nuclear Familiy Information Is IncludedHuman Heredity, 54
L. McIntyre, E. Martin, K. Simonsen, N. Kaplan (2000)
Circumventing multiple testing: A multilocus Monte Carlo approach to testing for associationGenetic Epidemiology, 19
T. Becker, M. Knapp (2004)
Maximum‐likelihood estimation of haplotype frequencies in nuclear familiesGenetic Epidemiology, 27
D. Zaykin, Peter Westfall, S. Young, Maha Karnoub, Michael Wagner, M. Ehm (2002)
Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated IndividualsHuman Heredity, 53
We have lately presented a testing procedure for family data which accounts for the multiple testing problem that is induced by the enormous number of different marker combinations that can be analyzed in a set of tightly linked markers. Most methods of haplotype based association analysis already require simulations to obtain an uncorrected P value for a specific marker combination. As shown before, it is nevertheless not necessary to carry out nested simulations to obtain a global P value that properly corrects for the multiple testing of different marker combinations without neglecting the dependency of the tests. We have now implemented this approach for case‐control data in our program FAMHAP, as this data structure currently plays a dominant role in the field. We consider different ways to deal with phase ambiguities and two different statistical tests for the underlying single marker combinations to obtain uncorrected P values. One test statistic is chi‐square based, the other is a haplotype trend regression. The performance of these different tests in the multiple testing situation is investigated in a large simulation study. We obtain a considerable gain in power with our global P values as opposed to Bonferroni corrected P values for all suggested test statistics. Good power was obtained both with the haplotype trend regression approach as well as with the simpler chi‐square based test. Furthermore, we conclude that the better strategy to deal with phase ambiguities is to assign to each individual its list of weighted haplotype explanations, rather than to assign to each individual its most likely haplotype explanation. Finally, we demonstrate the usefulness of our approach by a real data example.
Annals of Human Genetics – Wiley
Published: Nov 1, 2005
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.