Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty

Title
Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty
Author(s)
곽민정Jungnamn Joo[Jungnamn Joo]Zehua Chen[Zehua Chen]Gang Zheng[Gang Zheng]
Keywords
GENOME-WIDE ASSOCIATION; FAMILY-BASED ASSOCIATION; TREND TESTS; NUISANCE PARAMETER; SAMPLE-SIZE; ASYMPTOTIC PROPERTIES; UNIFIED APPROACH; AFFECTED SIBS; P-VALUE; POWER
Issue Date
201001
Publisher
JOHN WILEY & SONS LTD
Citation
STATISTICS IN MEDICINE, v.29, no.1, pp.158 - 180
Abstract
When testing genetic linkage and association, test statistics that follow a normal or Chi-square distributions are often used. These statistics are usually derived under a specific mode of inheritance (genetic model). Common genetic models include, but not limited to, the recessive, additive, multiplicative, and dominant models. For many diseases, their underlying genetic models are often unknown. Instead, a family of scientifically plausible genetic models may be available, which includes the four commonly used models. Hence, the optimal test is not available. Employing a single test statistic which is optimal for one model may suffer from substantial loss of power when the model is misspecified. In this situation efficient robust tests are useful. In this tutorial, we first review several commonly used robust statistics, including maximin efficency robust tests, maximal tests, and constrained likelihood ratio tests for three common designs in genetic studies: (i) linkage analysis using affected sib-pairs, (ii) association studies using parents-offspring trios, and (iii) case-control association studies (unmatched and matched). Codes in the R statistical language for applying these robust statistics to test for linkage and association are presented with examples. We also provide some comparisons of the performance of the various robust tests via simulation studies. Guidelines for applications are also given for each study design. Finally, applications of robust tests to genome-wide association studies and meta-analysis are discussed. Copyright (C) 2009 John Wiley & Sons, Ltd.
URI
http://hdl.handle.net/YU.REPOSITORY/22990http://dx.doi.org/10.1002/sim.3759
ISSN
0277-6715
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이과대학 > 통계학과 > Articles
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