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BMC Proc. 2011 Nov 29;5 Suppl 9:S117. doi: 10.1186/1753-6561-5-S9-S117. eCollection 2011.

Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data.

Author information

  • 1Division of Biostatistics, Yale School of Public Health, Yale University, 60 College St., PO Box 208034, New Haven, CT 06520-8034, USA ; Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
  • 2Keck Biotechnology Resource Laboratory, Yale University, 300 George St., New Haven, CT 06511, USA.
  • 3Division of Biostatistics, Yale School of Public Health, Yale University, 60 College St., PO Box 208034, New Haven, CT 06520-8034, USA.
  • 4Division of Biostatistics, Yale School of Public Health, Yale University, 60 College St., PO Box 208034, New Haven, CT 06520-8034, USA ; Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China.

Abstract

Recently there has been great interest in identifying rare variants associated with common diseases. We apply several collapsing-based and kernel-based single-gene association tests to Genetic Analysis Workshop 17 (GAW17) rare variant association data with unrelated individuals without knowledge of the simulation model. We also implement modified versions of these methods using additional information, such as minor allele frequency (MAF) and functional annotation. For each of four given traits provided in GAW17, we use the Bayesian mixed-effects model to estimate the phenotypic variance explained by the given environmental and genotypic data and to infer an individual-specific genetic effect to use directly in single-gene association tests. After obtaining information on the GAW17 simulation model, we compare the performance of all methods and examine the top genes identified by those methods. We find that collapsing-based methods with weights based on MAFs are sensitive to the "lower MAF, larger effect size" assumption, whereas kernel-based methods are more robust when this assumption is violated. In addition, many false-positive genes identified by multiple methods often contain variants with exactly the same genotype distribution as the causal variants used in the simulation model. When the sample size is much smaller than the number of rare variants, it is more likely that causal and noncausal variants will share the same or similar genotype distribution. This likely contributes to the low power and large number of false-positive results of all methods in detecting causal variants associated with disease in the GAW17 data set.

PMID:
22373309
[PubMed]
PMCID:
PMC3287841
Free PMC Article
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