grs.summary: Genetic risk score calculation from summary statistics.

Description

Implements the summary statistic method of Johnson et al. for approximating the regression of a response variable onto an additive multi-SNP genetic risk score in a given testing dataset, using only single SNP association summary statistics.

Usage

grs.summary(w, b, s, n)

Arguments

coefficients for the risk score.

aligned beta coefficients in the testing dataset, of same length as w.

standard errors for b, of same length as w and b.

sample size of testing dataset.

Value

A named list with the following elements: m is the number of SNPs used in the risk score. n is the input sample size. X2m is the chi squared test statistic for an m d.f. test in the testing dataset (all SNPs have independent effects). R2m is the (pseudo) variance explained by the m d.f. model in the testing dataset. ahat is the estimated coefficent for regressing the response onto the m SNP risk score. aSE is the standard error. X2rs is the chi squared test statistic for a 1 d.f. test for the risk score in the testing dataset. R2rs is the (pseudo) variance explained by the risk score model in the testing dataset. pval is the P-value for the 1 d.f. test. Qrs is the (m-1) d.f. heterogeneity test statistic. phet is the heterogeneity test P-value.

Details

The risk score coefficients w are the weights used to construct the risk score, for a set of SNPs, in chosen units per dose of the coded allele. Typically these are single SNP regression coefficients estimated in a discovery dataset.

The aligned beta coefficients b are regression coefficients for the response variable of interest, for the same set of SNPs and per dose of the same coded allele as used to define w. Typically these are single SNP regression coefficients estimated in the testing dataset.

The sample size argument n is required only to compute the (pseudo) variance explained in the testing dataset from the likelihood ratio test statistic.

References

Cite the paper by T. Johnson.