Purchase this article with an account.
Rui Chen, Hui Wang, Richard A. Lewis, James R. Lupski, Graeme Mardon, Emad B. Abboud, Ali A. Al-Rajhi, Richard A. Gibbs, Robert K. Koenekoop, Degui Zhi; Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing. Invest. Ophthalmol. Vis. Sci. 2011;52(14):3320.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
We aimed at establish a statistical framework to assess the statistic power of disease gene identification by exome sequencing approach, which can in turn be used to guide both the experiment design and subsequence data analysis.
Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the impact of limitations in the current exome sequencing approach, such as false positive and false negative rate due to sequencing errors and other artifacts, on experimental design has not been systematically analyzed. To address these questions we present a theoretical framework to calculate the power of indentifying disease genes under various inheritance models and experimental conditions, which could provide guidance for both proper experimental design and data analysis. In addition, computational simulations are performed to confirm the predictions.
Based on our model, we find that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. Disease genes that are responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Guided by this result, we plan to perform exome sequencing of a collection of LCA patients. So far, we have completed sequencing for 30 patient samples and have identified several candidate disease genes. Further expansion of our sequencing effort and validation of these candidate genes is currently underway.
Based on these results we propose that sequencing a pool of patients of sufficient number represents a very efficient and robust approach for identifying rare recessive Mendelian disease genes.
This PDF is available to Subscribers Only