Statistical guidance for experimental design and data analysis of mutation detection in rare monogenic mendelian diseases by exome sequencing.

TitleStatistical guidance for experimental design and data analysis of mutation detection in rare monogenic mendelian diseases by exome sequencing.
Publication TypeJournal Article
Year of Publication2012
AuthorsZhi, D, Chen, R
JournalPLoS One
Volume7
Issue2
Paginatione31358
Date Published2012
ISSN1932-6203
KeywordsExome, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Humans, Models, Statistical, Mutation, Research Design, Sample Size, Sequence Analysis, DNA
Abstract

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 current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work.

DOI10.1371/journal.pone.0031358
Alternate JournalPLoS ONE
PubMed ID22348076
PubMed Central IDPMC3277495
Grant ListR00 RR024163 / RR / NCRR NIH HHS / United States
R01 EY018571 / EY / NEI NIH HHS / United States
R01EY018571 / EY / NEI NIH HHS / United States