Iuliana Ionita-Laza, Seunggeun Lee, Vlad Makarov, Joseph D. Buxbaum, Xihong Lin
Published:May 16, 2013
AJHG VOLUME 92, ISSUE 6, P841-853, JUNE 06, 2013
https://doi.org/10.1016/j.ajhg.2013.04.015
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They focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genentic variants in a region influence trait susceptibility.
.. the overall goal is to identify genes that contatin disease rist variants, be they rare or common, ..
.. whole-xome sequencing (WES) focuses on a gene's protein-coding componentsm ..
.. over 90% of the variants on the array are in intronic or intergenic regions and only 3% and 8% are located in coding and exonic regions, ..
.. when common variants are important for disease risk, such an approach is likely to lose power.
An alternative approach is to compute p values for varying values of ϕ and use the minimum p value as a test statistic. This approach can be potentially more powerful if the overall effect sizes of rare and common variants are very different, for example, when only rare variants in the region are associated or when only common variants are associated with a trait. However, for this type of adaptive test, asymprotic p values cannot be obtained easily because of the potential correlation that exists between rare and common variants.
.. both Q_rare and Q_common follow a mixture of chi-square distributions and they are independent, the null distribution of T can be easily obtained.
An alternative approach is to combine the p values from the rare- and common-variant tests instead of combining test statistics.
... because the rare- and common-variant statistics might be correlated, the distribution of Q_F,ρ1,ρ2 is more complicated.
SKAT tests tend to be more powerful than the corresponding burden tests when the proportion of risk variants is small(e.g., 10%); however, burden tests become more powerful than SKAT tests when the proportion of risk variants is large (e.g., 30% or more). Note that for models 1-3, which include both rare and common risk variants, SKAT-F (Fisher) test tends to have better power than the burden-F test regardless of whether the proportion of associated variants is 10% or 30%. For model 5, which includes only common risk variants, the SKAT tests tend to perform better than the burden tests regardless of the proportion of risk variants.
As with the existing burden and SKAT tests, it is easy to incorporate covariate, including principal components, to adjust for population stratification.
Even though rare variants are more likely to be functional and are expected to have higher effects than common variants, common variants can account for a substantial proportion of the genetic variance.
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