simGyriq: Simulated SNP-set

In gyriq: Kinship-Adjusted Survival SNP-Set Analysis

Description

Simulated dataset of phenotypic, genotypic and kinship data.

Format

A list containing the following elements:

U

600x1 vector containing the survival times. U = min(C, T) where C is the censoring time, and T the failure time

Delta

600x1 vector containing the censoring indicator

Phi

600x600 kinship matrix

blkID

600x1 vector with entries identifying correlated groups of observations

X

600x2 matrix of 2 covariates

G

600x50 matrix containing the set of 50 SNPs

w

50x1 vector of weights for the 50 SNPs

bsw

4x1 vector containing the lower bounds of the 4 sliding windows considered for the SNP-set

tsw

4x1 vector containing the upper bounds of the 4 sliding windows considered for the SNP-set

pos

50x1 vector of SNP positions (used for the output only)

indResid

10,000*600x1 vector of permuted row indices

Details

This dataset was generated under conditions described in Leclerc et al. (2015).

Samples of n = 600 individuals from 120 families were generated: 40 families of two parents and one child, 40 families of two parents and two children, and 40 families of three generations (two grand-parents, four parents, and two grandchildren). The coefficients of the block diagonal kinship matrix were fixed at their expected theoretical values. The number of biallelic SNPs was set to s = 50. The minor allele frequencies were randomly sampled from Unif(0.001, 0.1). The genotypes of the 50 SNPs were simulated assuming a linkage disequilibrium corresponding to a squared correlation coefficient of r^2 = 0.5 between consecutive SNPs.

The two covariates follow Bernoulli(0.5) and Uniform(-0.2, 0.2) distributions respectively. The polygenic heritability parameter was fixed at 0.5. Each covariate parameter was set equal to 1 and the monotone increasing function of the transformation model with censored data (Cheng et al., 1995) was fixed at H(t) = log(t) in order to generate the survival traits. The censoring rate was equal to 50%. The weight of each SNP was defined as the density function of the Beta (1, 25) evaluated at the corresponding minor allele frequency.

The dataset includes simulated positions for the 50 SNPs, and the lower and upper bounds of 4 sliding windows. Each window includes 10 SNPs, overlapping with the previous and subsequent windows. A vector of size B*n of permuted row indices is also included, where B=10,000. This is to be used to compute the p-value of the test following the standard or matching moments permutation approach.

References

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.

Examples

data(simGyriq)
for (i in seq_along(simGyriq)) assign(names(simGyriq)[i], simGyriq[[i]])

cr <- genComplResid(U, Delta, Phi, blkID, m=50, X)
testGyriq(cr$compResid, G, w, ker="LIN", asv=NULL, method="davies", 
starResid=NULL, bsw, tsw, pos)

gyriq documentation built on May 2, 2019, 2:39 a.m.