Links Between the Sequence Kernel Association and the Kernel-Based Adaptive Cluster Tests

Weiming Zhang, Michael P. Epstein, Tasha E. Fingerlin and Debashis Ghosh ()
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Weiming Zhang: Colorado School of Public Health
Michael P. Epstein: Emory University School of Medicine
Tasha E. Fingerlin: National Jewish Health
Debashis Ghosh: Colorado School of Public Health

Statistics in Biosciences, 2017, vol. 9, issue 1, No 13, 246-258

Abstract: Abstract Two recently developed methods for the analysis of rare variants include the sequence kernel association test (SKAT) and the kernel-based adaptive cluster test (KBAC). While SKAT represents a type of variance component score test, and KBAC computes a weighted integral representing the difference in risk between variants, they appear to be developed using different initial principles. In this note, we show in fact that the KBAC can be modified to yield a test statistic with operating characteristics more similar to SKAT. Such a development relies on U- and V-statistic theory from mathematical statistics. Some simulation studies are used to evaluate the new proposed tests.

Keywords: Sequence kernel association test; Kernel-based adaptive cluster test; U-statistic; V-statistic; Rare variants (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9175-7

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