emats: Principal component based adaptive multi-trait association...
In baolinwu/MTAR: Various Multi-Trait Association tests in R

Description Usage Arguments Value References

We show that using the summary statistics, we can compute the PC to summarize the multiple traits, and then construct the 1-DF test statistic (ET). The omnibus K-DF chi-square test (OT) is generally robust and powerful. We then define the adaptive test (AT) as the minium p-values of weighted sums of ET (ρ) and OT (1-ρ) tests. Efficient algorithms are developed to compute the analytical p-values for all tests. We use the LD score regression (see GCvr() function) to accurately estimate the marginal trait correlation using GWAS summary data.

1	emats(Z, Sig, rho = 0:5/5)

`Z`	summary Z-statistics across multiple traits
`Sig`	the estimated marginal trait correlation matrix
`rho`	sequence of weights assigned to the 1-DF test

p.value: p-value for the AT
pvals: the list of all p-values
rho.est: estimated optimal ρ value

Bulik-Sullivan B et al. (2015) An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11):1236–41.

Guo,B. and Wu,B. (2018) Principal component based adaptive association test of multiple traits using GWAS summary statistics. bioRxiv 269597; doi: 10.1101/269597

Guo,B. and Wu,B. (2018) Integrate multiple traits to detect novel disease-gene association using GWAS summary data with an adaptive test approach. Bioinformatics, under revision.

baolinwu/MTAR documentation built on May 14, 2019, 6:03 a.m.