SNP-set association testing for censored phenotypes in the presence of intrafamilial correlation
|License:||GPL (>= 2)|
This variance-components test between a set of SNPs and a survival trait is valid for both common and rare variants. A proportional hazards Cox model (written as a transformation model with censored data; Cheng et al., 1995) is specified for the marginal distribution of the survival trait. The familial dependence is modelled via a Gaussian copula with a correlation matrix expressed in terms of the kinship matrix. The statistical procedure has been described in full detail by Leclerc et al. (2015).
Censored values are treated as partially missing data and a multiple imputation procedure is employed to estimate vectors of residuals. These residuals and the SNPs in the genomic region under study are used to compute measures of phenotypic and genotypic similarity between pairs of subjects. The contribution to the score statistic is maximal when these measures are both high which corresponds to departure from the null hypothesis of no association between the set of SNPs and the survival outcome. The selection of the SNPs forming the SNP set can be based on biological information such as linkage disequilibrium (LD) blocks or rely on a sliding window method.
The procedure is convenient for GWAS as the multiple imputation procedure for the estimation of a completed vector of residuals has to be performed only once using the function genComplResid. A sliding window approach can then be used to examine the evidence of association across the SNP set. In each run, the p-value is computed with the function testGyriq.
Cheng SC, Wei LJ, Ying Z. 1995. Analysis of transformation models with censored data. Biometrika 82:835-845.
Leclerc M, The Consortium of Investigators of Modifiers of BRCA1/2, Simard J, Lakhal-Chaieb L. 2015. SNP set association testing for survival outcomes in the presence of intrafamilial correlation. Genetic Epidemiology 39:406-414.
Lin X, Zhou Q. 2015. coxKM: Cox kernel machine SNP-set association test. R package version 0.3, URL http://www.hsph.harvard.edu/xlin/software.html#coxkm.
Lin X, Cai T, Wu M, Zhou Q, Liu G, Christiani D, Lin X. 2011. Survival kernel machine SNP-set analysis for genome-wide association studies. Genetic Epidemiology 35:620-631.
Cai T, Tonini G, Lin X. 2011. Kernel machine approach to testing the significance of multiple genetic markers for risk prediction. Biometrics 67:975-986.
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