pleio.glm.sequential {pleio}

Perform sequential tests of pleiotropy

Description

Perform sequential tests of pleiotropy to determine the number of traits, and which traits, are associatd with a vector of genotypes.

Usage

pleio.glm.sequential(obj.pleio.glm.fit, pval.threshold)

Arguments

obj.pleio.glm.fit

result of pleio.glm.fit

pval.threshold

p-value for rejecting the null hypothesis of the specified number of coefficients constrained to be zero.

Details

Perform sequential tests of pleiotropy, starting at the usual multivarite null hypothesis that all coefficients = 0. If this test rejects because the p-value < pval.threshold, then allow one coefficient to be non-zero in order to test whether the remaining coefficients = 0. If the test of one non-zero coefficient rejects, then allow two non-zero coefficients, considering all possible combinations of two non-zero coefficients and test whether the remaining coefficients = 0. Continue this sequential testing until the p-value for a test is greater than the specific pval.threshold. The step at which the p-value > pval.threshold determines which traits are associated with the genotype. If there are m traits, the sequential testing stops either when p-value > pval.threshold, or when (m-1) traits are tested. If the p-value remains less than pval.threshold when testing (m-1) traits, this implies that all m traits are associated with the genotype.

Value

A list containing:

pval

p-value of the final test from the sequential testing

count

the number of nonzero coefficients

index.nonzero.coef

index of column(s) of y that have non-zero coefficients. These indices indicate which traits are associated with the genotype, accounting for the correlations among the traits.

Author(s)

Dan Schaid and Jason Sinnwell

References

Schaid DJ, Tong X, Larrabee B, Kennedy RB, Poland GA, Sinnwell JP. Statistical Methods for Testing Genetic Pleiotropy. Genetics. 2016 Oct;204(2):483-497.

Schaid DJ, Tong X, Batzler A, Sinnwell JP, Qing J, Biernacka JM. Multivariate Generalized Linear Model for Genetic Pleiotropy. Under review.

Examples

data(pleio.demo)

## test without covars
fams <- c("gaussian","binomial","ordinal")
obj <- pleio.glm.fit(y, geno, glm.family=fams)
stat <- pleio.glm.test(obj, count.nonzero.coef = 0)
stat$stat
stat$pval
pseq <- pleio.glm.sequential(obj, pval.threshold=.5)
pseq