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Background  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. 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 ( PC s).    Results   PCA -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  PC s, i.e., cases only( CAES ), controls only( COES ) and cases and controls combined( CES ). Extraction of  PC s 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.    Conclusions   PCA-BCIT  is a valid and powerful method for assessing gene-disease association involving multiple SNPs.

PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs