| Title: | Mouse Map Converter |
|---|---|
| Description: | Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>. |
| Authors: | Karl W Broman [aut, cre]
|
| Maintainer: | Karl W Broman <[email protected]> |
| License: | GPL-3 |
| Version: | 0.11-2 |
| Built: | 2024-11-08 04:28:31 UTC |
| Source: | https://github.com/rqtl/mmconvert |
A data frame with rows being markers and six columns: marker name, chromosome, sex-averaged cM position, female cM position, male cM position, and build 39 basepair position.
Genetic maps were re-estimated after reordering markers according to their position in mouse genome build 39. See https://github.com/kbroman/CoxMapV3. Markers were shifted so that 0 cM corresponds to 3 Mbp, using the chromosome- and sex-specific recombination rate. The maps are smoothed slightly to avoid intervals with 0 recombination.
https://github.com/kbroman/CoxMapV3
data(coxmap)data(coxmap)
Convert a cross2 object (with genotypes from one of the MUGA arrays) to use mouse build GRCm39 and the revised Cox map positions, revising marker order and omitting markers that are not found.
cross2_to_grcm39(cross, array = c("guess", "gm", "mm", "mini", "muga"))cross2_to_grcm39(cross, array = c("guess", "gm", "mm", "mini", "muga"))
cross |
Object of class |
array |
Character string indicating which of the MUGA arrays was used ("gm" for GigaMUGA, "mm" for MegaMUGA, "mini" for MiniMUGA, or "muga" for the original MUGA), or "guess" (the default) to pick the array with the most matching marker names. |
The input cross object with markers subset to those in build GRCm39
and with pmap and gmap replaced with the GRCm39 physical map and
revised Cox genetic map, respectively.
library(qtl2) file <- paste0("https://raw.githubusercontent.com/rqtl/", "qtl2data/main/DOex/DOex.zip") DOex <- read_cross2(file) DOex_rev <- cross2_to_grcm39(DOex)library(qtl2) file <- paste0("https://raw.githubusercontent.com/rqtl/", "qtl2data/main/DOex/DOex.zip") DOex <- read_cross2(file) DOex_rev <- cross2_to_grcm39(DOex)
A vector of mouse chromosome lengths in basepairs for mouse genome build GRCm39.
https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001635.27/
data(grcm39_chrlen)data(grcm39_chrlen)
Convert mouse genome positions between the build 39 physical map and the Cox genetic map.
mmconvert( positions, input_type = c("bp", "Mbp", "ave_cM", "female_cM", "male_cM") )mmconvert( positions, input_type = c("bp", "Mbp", "ave_cM", "female_cM", "male_cM") )
positions |
A set of positions, in one of three possible formats
|
input_type |
Character string indicating the type of positions provided ( |
We use linear interpolation using the Cox map positions in the object coxmap. For positions outside the range of the markers on the Cox map, we extrapolate using the overall recombination rate.
A data frame with the interpolated positions, with seven columns: marker, chromosome, sex-averaged cM, female cM, male cM, basepairs, and mega-basepairs. The rows are sorted by genomic position.
# input as character strings like chr:position input_char <- c(rs13482072="14:6738536", rs13482231="14:67215850", gnf14.117.278="14:121955310") mmconvert(input_char) # input as list, as in the map object for R/qtl1 and R/qtl2 input_list <- list("14"=c(rs13482072=6738536, rs13482231=67215850, gnf14.117.278=121955310)) mmconvert(input_list) # input as data frame; *must* have chr as first column and position as second # (marker names can be third column, or can be row names) input_df <- data.frame(chr=c(14,14,14), pos=c(6738536, 67215850, 121955310), marker=c("rs13482072", "rs13482231", "gnf14.117.278")) mmconvert(input_df) # input can also be in Mbp input_df$pos <- input_df$pos / 1e6 mmconvert(input_df, input_type="Mbp")# input as character strings like chr:position input_char <- c(rs13482072="14:6738536", rs13482231="14:67215850", gnf14.117.278="14:121955310") mmconvert(input_char) # input as list, as in the map object for R/qtl1 and R/qtl2 input_list <- list("14"=c(rs13482072=6738536, rs13482231=67215850, gnf14.117.278=121955310)) mmconvert(input_list) # input as data frame; *must* have chr as first column and position as second # (marker names can be third column, or can be row names) input_df <- data.frame(chr=c(14,14,14), pos=c(6738536, 67215850, 121955310), marker=c("rs13482072", "rs13482231", "gnf14.117.278")) mmconvert(input_df) # input can also be in Mbp input_df$pos <- input_df$pos / 1e6 mmconvert(input_df, input_type="Mbp")
A list of four data frames with annotation information for the four MUGA arrays, GigaMUGA ("gm"), MegaMUGA ("mm"), MiniMUGA ("mini") and the original MUGA ("muga"). Each has columns marker, chromosome, build 39 basepair position, and sex-averaged cM position (in Cox Map v3).
SNP probes for the MUGA arrays were blasted against mouse genome build GRCm39 and locations interpolated using revised Cox maps. See https://github.com/kbroman/MUGAarrays for the array annotations and https://github.com/kbroman/CoxMapV3 for the genetic maps. Note that for the genetic map locations, markers were shifted so that 0 cM corresponds to 3 Mbp, using the chromosome- and sex-specific recombination rate. Moreover, the Cox map positions were smoothed slightly to avoid regions with 0 recombination.
https://github.com/kbroman/MUGAarrays
data(MUGAmaps)data(MUGAmaps)