Multi-bin multi-variant tests for gene-based linear regression analysis of genetic association
نویسندگان
چکیده
By jointly analyzing multiple variants within a gene together, instead of one at a time, gene-based regression analysis can improve power and robustness of genetic association analysis. Extending prior work that examined multi-bin linear combination (MLC) statistics for combined analysis of rare and common variants, here we investigate analysis of common variants more extensively under realistic trait models and conditions. This method exploits the linkage disequilibrium structure in a gene to construct bins of closely correlated variants, and for variants within each bin, corrects the coding scheme to make the majority of pairwise correlations positive. After bin construction and recoding, variant effects within the same bin are combined linearly, and the bin-specific effects are aggregated in a quadratic sum. This produces a test statistic with reduced degrees of freedom (df) as
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