BACKGROUND: Prioritizing individual rare variants within associated genes or regions often consists of an ad hoc combination of statistical and biological considerations. From the statistical perspective, rare variants are often ranked using Fisher's exact p values, which can lead to different rankings of the same set of variants depending on whether 1- or 2-sided p values are used. RESULTS: We propose a likelihood ratio-based measure, maxLRc, for the statistical component of ranking rare variants under a case-control study design that avoids the hypothesis-testing paradigm. We prove analytically that the maxLRc is always well-defined, even when the data has zero cell counts in the 2×2 disease-variant table. Via simulation, we show that the maxLRc outperforms Fisher's exact p values in most practical scenarios considered. Using next-generation sequence data from 27 rolandic epilepsy cases and 200 controls in a region previously shown to be linked to and associated with rolandic epilepsy, we demonstrate that rankings assigned by the maxLRc and exact p values can differ substantially. CONCLUSION: The maxLRc provides reliable statistical prioritization of rare variants using only the observed data, avoiding the need to specify parameters associated with hypothesis testing that can result in ranking discrepancies across p value procedures; and it is applicable to common variant prioritization.
BACKGROUND: Prioritizing individual rare variants within associated genes or regions often consists of an ad hoc combination of statistical and biological considerations. From the statistical perspective, rare variants are often ranked using Fisher's exact p values, which can lead to different rankings of the same set of variants depending on whether 1- or 2-sided p values are used. RESULTS: We propose a likelihood ratio-based measure, maxLRc, for the statistical component of ranking rare variants under a case-control study design that avoids the hypothesis-testing paradigm. We prove analytically that the maxLRc is always well-defined, even when the data has zero cell counts in the 2×2 disease-variant table. Via simulation, we show that the maxLRc outperforms Fisher's exact p values in most practical scenarios considered. Using next-generation sequence data from 27 rolandic epilepsy cases and 200 controls in a region previously shown to be linked to and associated with rolandic epilepsy, we demonstrate that rankings assigned by the maxLRc and exact p values can differ substantially. CONCLUSION: The maxLRc provides reliable statistical prioritization of rare variants using only the observed data, avoiding the need to specify parameters associated with hypothesis testing that can result in ranking discrepancies across p value procedures; and it is applicable to common variant prioritization.
Authors: Alkes L Price; Gregory V Kryukov; Paul I W de Bakker; Shaun M Purcell; Jeff Staples; Lee-Jen Wei; Shamil R Sunyaev Journal: Am J Hum Genet Date: 2010-05-13 Impact factor: 11.025
Authors: Lisa J Strug; Susan E Hodge; Theodore Chiang; Deb K Pal; Paul N Corey; Charles Rohde Journal: Eur J Hum Genet Date: 2010-04-28 Impact factor: 4.246
Authors: Lisa J Strug; Tara Clarke; Theodore Chiang; Minchen Chien; Zeynep Baskurt; Weili Li; Ruslan Dorfman; Bhavna Bali; Elaine Wirrell; Steven L Kugler; David E Mandelbaum; Steven M Wolf; Patricia McGoldrick; Huntley Hardison; Edward J Novotny; Jingyue Ju; David A Greenberg; James J Russo; Deb K Pal Journal: Eur J Hum Genet Date: 2009-01-28 Impact factor: 4.246
Authors: Abra Brisbin; Gregory D Jenkins; Katarzyna A Ellsworth; Liewei Wang; Brooke L Fridley Journal: Front Genet Date: 2012-09-06 Impact factor: 4.599