GKnowMTest-package: Genomic Knowledge-guided Multiple Testing

In sbstatgen/GKnowMTest: Genomic Knowledge-guided Multiple Testing

Description

Tools for integrating prior knowledge into genomic studies.

Details

Currently the package provides two functions for integrating prior knowledge into SNP-level p-values (e.g. from a GWAS). One isanno.create and other is a statistical function prior.adjust.First function creates a Mapping Object of class annotatedSNPset.It supports two kinds of priors on SNPs, namely SNP-level functional annotations (e.g. from ENCODE) and gene-level annotations (e.g. pathways).SNP-level functional annotations can also be obtained using another function create.anno.mat. A mapping object needs to be first created using the anno.create or anno.mergefunctions before running the statistical routines. The statistical function prior.adjust integrates prior knowledge into SNP-level p-values (e.g. from a GWAS).The adjusted p-values of the SNPs are calculated based on the prior probability of being associated to the disease. The prior association probabilies are calculated either by Method of Moments or by penalized Regression, as specified by the user. It is incorporated as SNP groups such as functional annotations or pathway information as obtained from the mapping object. The adjusted p-values can be calculated by pair-wise or cubic-optimization or optimal weighting methods, as specified by the user.

References

Pickrell, Joseph K. "Joint analysis of functional genomic data and genome-wide association studies of 18 human traits." The American Journal of Human Genetics 94.4 (2014): 559-573.

sbstatgen/GKnowMTest documentation built on May 27, 2019, 7:40 a.m.