Latent class model with familial dependence to address heterogeneity in complex diseases: adapting the approach to family-based association studies.
- Authors
- Bureau, Alexandre; Croteau, Jordie; Tayeb, Arafat; Mérette, Chantal; Labbe, Aurélie
- Year
- 2011
- Journal
- Genetic epidemiology
- PMID
- 21308764
- DOI
- 10.1002/gepi.20566
- PMCID
- PMC4000257
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 an 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).
Power to detect association to a disease-susceptibility (DS) variant with a marker correlated at r2 = 0.8 in simulations of an heterogeneity model with four DS variants (4G5C model). Datasets contain 400 families with two affected siblings and parents with no phenotypic information. Genotypes of all family members are observed. For latent class (LC)-derived phenotypes, p-values were multiplied by the number of classes. The significance level was set to 5 × 10−8. Results are based on 400 replicates. Panel A shows the results of an analysis under the dominant model, panel B, D and E results under the additive model and panel C results under the recessive model. Error bars represent exact 95% confidence intervals. The first four bars from the left on each panel represent power using LC-derived phenotypes: P: posterior probability of class membership used as a quantitative trait in affected subjects, C: most probable class used as phenotype, σ: within-class standard deviation. The rightmost bar (orig) represents power using the original phenotype where all symptomatic subjects are affected.
Distribution of symptoms in latent classes formed using four ADI-R items. SOC3: Item 3 of the subdomain “qualities of reciprocal social interaction”; COM1: Item 1 of the subdomain “communication and language”; BEH1 and BEH2: Items 1 and 2 of the subdomain “restricted and repetitive, stereotyped interests and behaviors”. The distribution of BEH1 is shown for 6 year old males, and the distribution of BEH2 for 6 year old children (no adjustment for sex). SOC3 and COM1 were not adjusted for any covariate. The distributions are shown for the 4 classes containing at least 100 genotyped ASD subjects when assigning these subjects to their most likely class.
Association of SNPs to latent class 7 and to the ASD phenotype at 6p24-p23 in the AGRE sample. FBAT results under the additive model. For latent class 7 phenotypes, p-values were multiplied by 4, the number of tested classes.
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