Description Usage Arguments Value Author(s) References See Also Examples
This function performs multivariate GWA analysis using meta-GWAS summary statistics
1 2 | MultiSummary(x, index = NULL, type = "direct", vars = NULL,
high.dim = FALSE)
|
x |
A data object of class |
index |
A numeric vector that gives the indices of the traits to be analyzed jointly. |
type |
A string gives the type of analysis. Default is |
vars |
A numeric vector gives the variance of the genotypes at each SNP, e.g. coded as 0, 1 and 2.
Only used when |
high.dim |
Are the phenotypes high-dimensional or not? This is particularly important when the ratio
of the number of individuals (n) to the number of phenotypes being analyzed (p) is not big enough, e.g when
analyzing a big number of omics phenotypes in a small cohort. Default = |
The function returns a data frame containing the multi-trait GWAS results, where the row names are
the variants names. The column names are: variant name (Marker
), allele frequency (Freq
),
the effective sample size of the multiple traits (N
), effect on the phenotype score (Beta.S
, see reference),
standard error (SE
), p-value (P
), and the rest the coefficients to construct the phenotype score
(see reference).
Xia Shen
Zheng Ning, Yakov Tsepilov, Peter K. Joshi, James F. Wilson, Yudi Pawitan, Chris S. Haley, Yurii S. Aulchenko, Xia Shen (2018). Pleiotropic meta-analysis for genomic studies: discovery, replication, and interpretation. Submitted.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
## download the six example files from:
## https://www.dropbox.com/sh/hhta45cewvvea2s/AADfj4OXlbroToZAwIii2Buha?dl=0
## the summary statistics from Randall et al. (2013) PLoS Genet
## for males only
## bmi: body mass index
## hip: hip circumference
## wc: waist circumference
## whr: waist-hip ratio
## load the prepared set of independent SNPs
indep.snps <- as.character(read.table('indep.snps')$V1)
## load summary statistics of the six traits
stats.male <- load.summary(files = c('bmi.txt', 'height.txt',
'weight.txt', 'hip.txt', 'wc.txt',
'whr.txt'), indep.snps = indep.snps)
## perform multi-trait meta-GWAS
result <- MultiSummary(stats.male)
head(result)
## End(Not run)
|
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