MangroveORs: The MangroveORs class

Description Usage Arguments Details Value See Also Examples

Description

Functions to read in and use MangroveORs objects

Usage

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readORs(ORfile,header=TRUE)

## S3 method for class 'MangroveORs'
print(x, ...)
## S3 method for class 'MangroveORs'
summary(object, K = NULL, ...)
## S3 method for class 'MangroveORs'
plot(x, K = NULL, ...)

Arguments

ORfile

The location of an odds ratio file to read in

header

Logical. Indicates whether the ORfile above has a header.

x

A MangroveORs object

object

A MangroveORs object

K

The prevalence of the disease that the odds ratios predict. If NULL, data is returned for a few example prevalences.

...

Additional arguments to be passed to downstream methods.

Details

A MangroveORs object is used to hold the risk alleles, odds ratios and frequencies for a set of genetic variants used to predict disease. You can read an odds ratio file from disk using readORs.

The format of the odds ratio file is a text file with 4 or 5 columns. If the file has 4 columns, the first should be the variant ID, the second the risk allele, the third the additive odds ratio and the fourth the frequency. If 5 columns exist, column three is the heterozygous odds ratio, column four is the homozygous risk odds ratio, and five is the allele frequency.

The print method shows the data that the object holds. The summary method gives some measures of how predictive the variants are, including the variance explained by the variants in the object on the liability scale. The plot method show the cumulative variance explained on the liability scale as the variants are added in one-at-a-time (in order of most-to-least variance explained).

See vignette("Mangrove") for an example of an odds ratio file, and usage of an odds ratio object.

Value

An object of class MangroveORs for readORs. Nothing is returned for the methods.

See Also

MangroveRiskPreds for performing risk prediction using this object, and getVarExp for more on assessing variance explained.

Examples

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Mangrove documentation built on May 29, 2017, 11:54 a.m.