1. Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
- Author
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Richard S. Cooper, Liping Tong, Jie Yang, and Bamidele O. Tayo
- Subjects
False discovery rate ,lcsh:Medicine ,Single-nucleotide polymorphism ,Computational biology ,Quantitative trait locus ,Bioinformatics ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,010104 statistics & probability ,03 medical and health sciences ,Polymorphism (computer science) ,SNP ,Medicine ,0101 mathematics ,lcsh:Science ,030304 developmental biology ,Genetic association ,0303 health sciences ,business.industry ,lcsh:R ,Linear model ,General Medicine ,Proceedings ,Multiple comparisons problem ,lcsh:Q ,business - Abstract
We compare the SNP-based and gene-based association studies using 697 unrelated individuals. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all the multiple comparisons. We use a linear model for the single-nucleotide polymorphism (SNP) based association study. For the gene-based study, we consider three methods. The first one is based on a linear model, the second is similarity based, and the third is a new two-step procedure. The results of power using a subset of SNPs show that the SNP-based association test is more powerful than the gene-based ones. However, in some situations, a gene-based study is able to detect the associated variants that were neglected in a SNP-based study. Finally, we apply these methods to a replicate of the quantitative trait Q1 and the binary trait D (D = 1, affected; D = 0, unaffected) for a genome-wide gene search.
- Published
- 2012