Computing a gene-based test for basic GWAS designs using permutations is conceptually simple and is currently implemented as the ‘set-based test’ in the PLINK software package ; however heavy computational requirements have restricted this method from being adopted on a genome-wide scale. Other gene-based tests such as those based on genetic distances  or entropy  are often also restricted to situations where individual genotype information is a...
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...lated using the Cholesky decomposition method implemented in the R mvtnorm package . The number of simulations per gene is determined adaptively. In the first stage, 103 simulations will be performed. If the resulting empirical p-value is less than 0.1, an additional 104 will be performed. If the empirical p-value from 104 simulations is 0, the program will perform 106 simulations. For computational reasons, if the empirical p-value is still 0, then no more simulations will be performed. An empirical p-value of 0 from 106 simulations can be interpreted as p < 10-6, which exceeds a Bonferroni corrected threshold of p < 2.85×10-6 (this threshold is likely to be conservative given the overlap between genes). The user may select whether to perform the gene-based test on either the full set of SNPs within a gene or a specified percentage of the most significant SNPs.
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