Asessing statistical significance in predictive GWA studies
Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers.
hierGWAS is a package designed to assess statistical significance in GWA studies, using a hierarchical approach.
There are 4 functions provided:
the hierarchical clustering of the SNPs, while
runs multiple penalized regressions on repeated random subsamples. These 2 functions
need to be executed before
test.hierarchy, which does the
hierarchical testing, though the order in which the 2 functions are executed does
test.hierarchy provides the final output of
the method: a list of SNP groups or individual SNPs, along with their corresponding
compute.r2 computes the explained variance
of an arbitrary group of SNPs, of any size. This group can encompass all SNPs, SNPs
belonging to a certain chromosome, or an individual SNP.
Laura Buzdugan email@example.com
Buzdugan, L. et al. (2015), Assessing statistical significance in predictive genome-wide association studies (unpublished)
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