Abstract
With recent advances in sequencing, genotyping arrays, and imputation, GWAS now aim to identify associations with rare and uncommon genetic variants. Here, we describe and evaluate a class of statistics, generalized score statistics (GSS), that can test for an association between a group of genetic variants and a phenotype. GSS are a simple weighted sum of single-variant statistics and their cross-products. We show that the majority of statistics currently used to detect associations with rare variants are equivalent to choosing a specific set of weights within this framework. We then evaluate the power of various weighting schemes as a function of variant characteristics, such as MAF, the proportion associated with the phenotype, and the direction of effect. Ultimately, we find that two classical tests are robust and powerful, but details are provided as to when other GSS may perform favorably. The software package CRaVe is available at our website (http://dceg.cancer.gov/bb/tools/crave).
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Acknowledgements
The research of Dr Joshua Sampson was supported by the intramural program of the National Institute of Cancer. The research of this study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. (http://biowulf.nih.gov).
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Ferguson, J., Wheeler, W., Fu, Y. et al. Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation. Eur J Hum Genet 21, 680–686 (2013). https://doi.org/10.1038/ejhg.2012.220
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DOI: https://doi.org/10.1038/ejhg.2012.220
