Abstract
BackgroundGenetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE) and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA) are preferable in this regard but their performance varies with methods of extracting principal components (PCs).ResultsPCA-based bootstrap confidence interval test (PCA-BCIT), which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PCs, i.e., cases only(CAES), controls only(COES) and cases and controls combined(CES). Extraction of PCs with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test.ConclusionsPCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.
Highlights
Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available
Simulation studies We examine the performance of principal component analysis (PCA)-bootstrap confidence interval test (BCIT) through simulations with data from the North American Rheumatoid Arthritis (RA) Consortium (NARAC) (868 cases and 1194 controls)[20], taking advantage of the fact that association between protein tyrosine phosphatase nonreceptor type 22 (PTPN22) and the development of RA
Simulation study The performance of PCA-BCIT is shown in Table 1 for the three strategies given a range of sample sizes
Summary
Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE) and potentially large number degrees of freedom can harm its statistical power and robustness. Genetic association studies customarily involve multiple SNPs in candidate genes or genomic regions and have a significant role in identifying and characterizing disease-predisposing variant(s). Population-based case-control studies have been very popular[1] and typically involve contingency table tests of SNP-disease association[2]. The genotype-wise Armitage trend test is appealing since it is equivalent to the score test from logistic regression[8] of case-control status on dosage of disease-predisposing alleles of SNP. Haplotype-based methods have many desirable properties[10] and could possibly alleviate the problem[11,12,13,14], but assumption of HWE is usually required and a potentially large number of degrees of freedom are involved[7,11,15,16,17,18]
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