Description Usage Arguments Value Note Author(s) References See Also Examples
This function tests the difference between genetic correlation estimates reported by LD Score regression (LDSC), to infer causality.
1 2 3 |
rg1.log |
LDSC log file for the 1st genetic correlation estimation between the exposure and outcome phenotypes. |
rg2.log |
LDSC log file for the 2nd genetic correlation estimation between the exposure and outcome phenotypes. |
exposure.sumstats |
The gzipped file of exposure phenotype GWAS summary statistics, munged by LDSC. |
outcome.sumstats1 |
The gzipped file of outcome phenotype GWAS (1st population) summary statistics, munged by LDSC. |
outcome.sumstats2 |
The gzipped file of outcome phenotype GWAS (2nd population) summary statistics, munged by LDSC. |
exposure |
Name of the exposure phenotype. |
outcome |
Name of the outcome phenotype. |
heading |
logical value that specified whether the software intro heading is printed. |
A list is returned with:
rg.diff The estimated difference between two genetic correlation estimates.
se The standard error of rg.diff
.
p.value The p-value testing the null hypothesis of rg.diff
= 0.
r.rg The estimated correlation between two genetic correlation estimates.
GWAS of the exposure phenotype is required to be done in only one population, to keep its heritability fixed. Two different populations/sources are required for GWAS of the outcome phenotype.
Xia Shen
Shen X, Ning Z, Joshi PK, Lee Y, Wilson JF, Pawitan Y (2017). Genetic correlation heterogeneity detects causal factors for complex traits. Submitted.
GCHC homepage: http://gchc.shen.se
1 2 3 4 5 6 7 8 9 10 | ## Not run:
gchc(rg1.log = 'EDU_BMI1.log',
rg2.log = 'EDU_BMI2.log',
exposure.sumstats = 'EDU.sumstats.gz',
outcome.sumstats1 = 'BMI1.sumstats.gz',
outcome.sumstats2 = 'BMI2.sumstats.gz',
exposure = 'EA',
outcome = 'BMI')
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.