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Rare variant association  

2012-02-27 19:59:12|  分类: 生物信息分析 |  标签: |举报 |字号 订阅

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With the increasing availability of large-scale sequencing data sets, there has been a growing need for new statistical methods for rare variant association analyses. Several new methods have been described that employ collapsing, weighting and distribution-based approaches. In a recent report, Brent Richards and colleagues evaluate a selection of rare variant association methods, comparing the performance of the methods on a data set of Sanger sequencing results for seven genes in 1,998 individuals (PLoS Genet. 8, e1002496, 2012). The authors also develop a new rare variant association method for continuous traits called weighted outlier detection (WOD), which is based on the KBAC method. They test each method using simulations under a range of scenarios that reflect assumptions on disease architecture and association, finding that these parameters greatly influence the performance of each method, and they note under which scenarios each method performs best. In additional simulations, they examine performance across ranges of power, effect size and proportion of causal variants for both continuous and dichotomous traits. They recommend that, as the power for all the tested methods remains low, studies should include sensitivity analyses and comparisons using several different methods, in addition to replication of results in an independent sample.

Orli Bahcall
Nature Genetics  Volume: 44, Page: 241 Year published: (2012) DOI: doi:10.1038/ng.2211

VIA:http://www.nature.com/ng/journal/v44/n3/full/ng.2211.html

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