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)
``` |

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