gwas.cross: Read genomic data to perform GWAS analyses.

In kbroman/lmem.gwaser: Linear Mixed Effects Models for Genome-Wide Association Studies

Description

This function reads genomic data and is similar to the read.cross function from r/qtl package (Broman and Sen, 2009) but allows importing data from a flapjack format (Milne et al., 2010). Additionally, it loads diverse populations for GWAS analysis into r/qtl format. The files required include a file containing phenotypic information (P.data), a file containing genotypic information (G.data), and a file containing map information (map.data) for all markers.

Usage

gwas.cross(P.data = NULL, G.data, map.data, cross = "gwas",
  heterozygotes = TRUE, sep = "\t")

Arguments

P.data	Name of the file containing phenotypic information. Each row represents the individuals while each column represents the phenotypic traits. The first column should be labeled as 'genotype' and should contain identification name for each individual. The name of each trait should also be included.
G.data	Name of the file containing genotypic (marker scores) information. Each row represents the individuals while each column represents the markers. Headers for markers should be included, but not for genotypes. The first column contains the names of the genotypes. The first row contains the names of the markers. The marker genotypes are coded by two characters corresponding to the alleles using a separator between alleles (by default a slash /). If a single character is given, the genotype is assumed to be homozygous. Missing values are indicated by default with '-'. In the example below, the two alleles have been called 1 and 2 because it is useful to link alleles to their origin, i.e. parent 1 or parent 2. Therefore, 1 corresponds to homozygous for allele 1 (synonymous to 1/1), 1/2 corresponds to heterozygous, and 2 corresponds to homozygous for allele 2 (synonymous to 2/2). In the case of partially informative markers (e.g. dominant markers) genotypes are coded as 1/- or 2/-, depending on whether the dominant allele originated from parent 1 or parent 2.
map.data	Name of the file containing marker map information ( i.e. linkage group and position within linkage group). The file is a text tab delimited file. Each row represents markers. The file consists of three columns. Column 1 gives the marker names, column 2 the chromosome on which the marker has been mapped, and column 3 indicates the position of the marker within the chromosome.
cross	The type of population studied. gwas is set as default Diverse population panel for GWAS.
heterozygotes	It indicates whether there are heterozygotes or not in the association mapping population. TRUE is set as default.
sep	To define the espace between the data.

Details

The function creates an intermediate file called 'temp.csv' and then uses the read.cross from r/qtl to read it. The output object is an object of class=cross, the same as the one produced by the function read.cross in r/qtl (Broman and Sen, 2009)

Value

Creates an object of class cross to be used in GWAS analysis. The component are the same as r/qtl (Broman and Sen, 2009): geno

Note

All functions in this package uses cross data style.

Author(s)

Lucia Gutierrez, Gaston Quero.

References

Broman KW, Sen S (2009) A Guide to QTL Mapping with R/qtl. Springer, NewYork Comadran J, Thomas W, van Eeuwijk F, Ceccarelli S, Grando S, Stanca A, Pecchioni N, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Bort J, Romagosa I, Hackett C, Russell J (2009) Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association-mapping population for the Mediterranean basin. Theor Appl Genet 119:175-187 Milne et al., (2010) Flapjack - graphical genotype visualization. Bioinformatics 26(24), 3133-3134.

See Also

gwas.analysis

Examples

data (QA_geno)
data (QA_map)
data (QA_pheno)

P.data <- QA_pheno
G.data <- QA_geno
map.data <- QA_map

cross.data <- gwas.cross (P.data, G.data, map.data,
cross='gwas', heterozygotes=FALSE)

summary (cross.data)

kbroman/lmem.gwaser documentation built on May 30, 2019, 3:10 p.m.