genesetmap

Function to generate map for gene set

By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.

Kosuke Hamazaki

RAINBOWR

Genome-Wide Association Study with SNP-Set Methods

Hiroyoshi Iwata

genesetmap function

<dl><dt>map</dt>
<dd>Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.</dd>
<dt>gene.set</dt>
<dd>Gene information with the format of a "data.frame" (whose dimension is (the number of gene) x 2).
In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
which correspond to the marker names of "map" argument.</dd>
<dt>cumulative</dt>
<dd>If this argument is TRUE, cumulative position will be returned.</dd></dl>

Arguments

Function to generate map for gene set — genesetmap

<dl>

<dt>map</dt>
<dd>Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.</dd>


<dt>gene.set</dt>
<dd>Gene information with the format of a "data.frame" (whose dimension is (the number of gene) x 2).
In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
which correspond to the marker names of "map" argument.</dd>


<dt>cumulative</dt>
<dd>If this argument is TRUE, cumulative position will be returned.</dd>

</dl>

genesetmap: Function to generate map for gene set

Description

Usage

Value

Arguments