Latent class model with familial dependence to address heterogeneity in complex diseases: adapting the approach to family-based association studies.
Latent class model with familial dependence to address heterogeneity in complex diseases: adapting the approach to family-based association studies.
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DOI：
10.1002/gepi.20566
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发表时间：
2011-04
影响因子：
2.1
通讯作者：
Labbe, Aurelie
中科院分区：
文献类型：
--
作者：
Bureau, Alexandre;Croteau, Jordie;Tayeb, Arafat;Merette, Chantal;Labbe, Aurelie
Clinical diagnoses of complex diseases may often encompass multiple genetically heterogeneous disorders. One way of dissecting this heterogeneity is to apply latent class (LC) analysis to measurements related to the diagnosis, such as detailed symptoms, to define more homogeneous disease sub-types, influenced by a smaller number of genes that will thus be more easily detectable. We have previously developed a LC model allowing dependence between the latent disease class status of relatives within families. We have also proposed a strategy to incorporate the posterior probability of class membership of each subject in parametric linkage analysis, which is not directly transferable to genetic association methods. Under the framework of family-based association tests (FBAT), we now propose to make the contribution of an affected subject to the FBAT statistic proportional to his or her posterior class membership probability. Simulations showed a modest but robust power advantage compared to simply assigning each subject to his or her most probable class, and important power gains over the analysis of the disease diagnosis without LC modeling under certain scenarios. The use of LC analysis with FBAT is illustrated using autism spectrum disorder (ASD) symptoms on families from the Autism Genetics Research Exchange, where we examined eight regions previously associated to autism in this sample. The analysis using the posterior probability of membership to a LC detected an association in the JARID2 gene as significant as that for ASD (p = 3×10−5) but with a larger effect size (odds ratio = 2.17 vs. 1.55).
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