gtx (version 0.0.8)

grs.filter.Qrs: Filter SNPs for inclusion in genetic risk score using heterogeneity test.

Description

Performs a stepwise downward “model selection” in which SNPs are iteratively removed from the risk score until the heterogeneity test is no longer significant at the specified threshold.

Usage

grs.filter.Qrs(w, b, s, p.thresh = 0.05)

Arguments

w

coefficients for the risk score

b

aligned beta coefficients in the testing dataset

s

standard errors

p.thresh

P-value threshold

Value

A logical vector of the same length as w, set TRUE or FALSE respectively for SNPs included or excluded at the end of the model selection procedure.

Details

When there are m SNPs in the risk score, the heterogeneity test is an (m-1) d.f. LRT comparing the 1 d.f. risk score model against the unconstrained m d.f. model, as reported by the Qrs element of grs.summary. At each iteration, if the risk score model has a significant heterogeneity test (at p.thresh), the SNP is removed that gives the greatest decrease in the heterogeneity test statistic.

No guarantee is given about the performance of this procedure. Intuitively, it is expected to work when the majority of SNPs in the risk score only affect the outcome with effects proportional to their weights in the score (including the possibility of zero effects), and a minority of SNPs affect the outcome with non-proportional effects.

When using a risk score to make causal inference, the application of this procedure (and indeed of any use of the heterogeneity test) should not be viewed as a replacement for detailed biological knowledge about the mechanisms of action of the causal genetic variants tagged by the SNPs used in the risk score.

Compared with iteratively calculating the heterogeneity test using grs.summary and removing SNPs based on inspection of grs.plot, the grs.filter.Qrs procedure offers only convenience and (a modicum of) objectivity, and nothing more.

Examples

  data(magic.scores)
  score1 <- subset(magic.scores, score == "FG2010")
  score1 <- within(score1, okay <- grs.filter.Qrs(coef, beta_TG, se_TG))
  with(score1, {grs.plot(coef, beta_TG, se_TG, locus);
    title(xlab = "FG effect", ylab = "TG effect")})
  with(score1, locus[!okay]) # loci removed
  with(subset(score1, okay), {grs.plot(coef, beta_TG, se_TG, locus);
    title(xlab = "FG effect", ylab = "TG effect")})