Copyright © 2008 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 82, Issue 5, 1064-1074, 17 April 2008
doi:10.1016/j.ajhg.2008.03.002
Article
Estimating Odds Ratios in Genome Scans: An Approximate Conditional Likelihood Approach
Arpita Ghosh1, Fei Zou1, 2, 3 and Fred A. Wright1, 2, 3, 
, 
1 Department of Biostatistics, The University of North Carolina at Chapel Hill, NC 27599, USA
2 Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, NC 27599, USA
3 Center for Environmental Bioinformatics, The University of North Carolina at Chapel Hill, NC 27599, USA
Corresponding authorAbstract
In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.
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