Description Usage Arguments Value Note Author(s) References See Also Examples
The function performs replication analysis of multivariate GWA signals.
1 2 3 |
training.pheno |
An (optional) matrix or data frame contains the phenotype data for the discovery sample, preferrably adjusted for fixed effects and population structure before multivariate GWA analysis. |
training.phenofile |
An (optional) plain text file contains phenotypes for the discovery sample.
If this is provided, it will serve as |
test.pheno |
An (optional) matrix or data frame contains the phenotype data for the replication sample, preferrably adjusted for fixed effects and population structure. |
test.phenofile |
An (optional) plain text file contains phenotypes of the replication sample.
If this is provided, it will serve as |
pheno.names |
A vector (length > 1) giving the column names of the phenotypes to be analyzed. |
training.geno |
A matrix or data.frame that contains the discovery sample genotype dosages of the variants to replicate. |
test.geno |
A matrix or data.frame that contains the replication sample genotype dosages
of the variants to replicate. This object should have the same column names and order
as |
The function returns a list of 3 matrices. $replication
contains the estimate of
variant effect on the corresponding compound phenotype (beta_c
), standard error (s.e.
),
replication P-value (P
), and proportion of phenotypic variance explained (R-squared
).
$training.coef
contains the estimated coefficients in the discovery sample of each phenotype
for each variant to construct the compound phenotype. $test.coef
contains similar coefficients
as in $training.coef
but estimated in the replication sample, but these are just for the record,
NOT used in the replication procedure.
Either .pheno
or .phenofile
has to be provided.
If both are provided, only phenofile
will be used. Individual IDs
in .pheno
or .phenofile
and .geno
have to match!
Xia Shen
Shen X, Klaric L, Sharapov S, Mangino M, Ning Z, Wu D, Trbojevic-Akmacic I, Pucic-Bakovic M, Rudan I, Polasek O, Hayward C, Spector TD, Wilson JF, Lauc G, Aulchenko YS (2017): Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation. Nature Communications, 8, 447; doi: 10.1038/s41467-017-00453-3.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ## Not run:
## loading example discovery sample gwaa.data in GenABEL
data(ge03d2)
## running multivariate GWAS for 3 traits: height, weight, bmi
res <- Multivariate(gwaa.data = ge03d2, trait.cols = c(5, 6, 8),
covariate.cols = c(2, 3))
## extracting 5 significant variants
(top <- res[order(res[,'P.F']),][2:6,])
snps <- rownames(top)
training.geno <- as.double(gtdata(ge03d2)[,snps])
## loading example test sample gwaa.data in GenABEL
data(ge03d2c)
## extracting genotypes of the 5 variants
test.geno <- as.double(gtdata(ge03d2c)[,snps])
## try replication
rep <- MultiRep(training.pheno = phdata(ge03d2), test.pheno = phdata(ge03d2c),
pheno.names = c('height', 'weight', 'bmi'),
training.geno = training.geno, test.geno = test.geno)
## End(Not run)
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