Description Usage Arguments Details Value References
We efficiently compute minimum test p-values (minP) across multiple quantitative traits; and report two test p-values: 1) Bonferroni corrected minimum p-values (Pbonf) across traits. 2) Analytical significance p-value (Pmin) of minP using asymptotic multivariate normal integration. See refs in the PGmvn() function.
1 | MTA.minp(obj, G)
|
obj |
fitted null model from MLM.null |
G |
genotype vector |
Remarks: (1) Be cautious to interpret extreme Pmin: it is very hard to accurately compute extreme p-values. (2) Generally Pmin is close to Pbonf at extreme values under moderate trait correlations. (3) Under extreme trait correlations, Pmin can offer advantages compared to Pbonf. (4) Note that theoretically we have minP ≤ Pmin ≤ Pbonf.
individual trait association test p-values
Bonferroni corrected minimum p-value
Significance p-value of minimum p-value computed analytically based on multivariate normal dist
Conneely,K.N. and Boehnke,M. (2007) So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. Am. J. Hum. Genet. 81, 1158–1168.
Conneely,K.N. and Boehnke,M. (2010) Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits. Genetic Epidemiology. 34:739-746.
Wu,B. (2017) MTAR: an R package for versatile genome-wide association test of multiple traits. tech report.
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