Trait Data and Analysis for 10554150

RGM domain family, member A

Details and Links

Group Mouse: BXD group
Tissue Hypothalamus mRNA
Gene Symbol Rgma
Aliases Wikidata: RGM; BC059072; C230063O06
GeneNetwork: Rgma; BC059072; C230063O06; MGC38550; MGC69915
Location Chr 7 @ 73.375507 Mb on the plus strand
Database INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) PCA Test v080513
Resource Links Gene    OMIM    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal   
UCSC    BioGPS    STRING    PANTHER    Gemma    ABA    EBI GWAS   


More about Normal Probability Plots and more about interpreting these plots from the glossary

Transform and Filter Data

Edit or delete values in the Trait Data boxes, and use the Reset option as needed.

Outliers highlighted in orange can be hidden using the Hide Outliers button.

Samples with no value (x) can be hidden by clickingHide No Value button.

Calculate Correlations

Chr:     Mb:  to 
The Sample Correlation is computed between trait data and any other traits in the sample database selected above. Use Spearman Rank when the sample size is small (<20) or when there are influential outliers.

Mapping Tools

No collections available. Please add traits to a collection to use them as covariates.

No collections available. Please add traits to a collection to use them as covariates.
Maps traits with correction for kinship among samples using a linear mixed model method, and also allows users to fit multiple covariates such as sex, age, treatment, and genetic markers (PMID: 2453419, and GitHub code). GEMMA incorporates the Leave One Chromosome Out (LOCO) method to ensure that the correction for kinship does not remove useful genetic variance near each marker. Markers can be filtered to include only those with minor allele frequencies (MAF) above a threshold. The default MAF is 0.05.
Haley-Knott Regression
Fast linear mapping method (PMID 16718932) works well with F2 intercrosses and backcrosses, but that is not recommended for complex or admixed populations (e.g., GWAS or heterogeneous stock studies) or for advanced intercrosses, recombinant inbred families, or diallel crosses. Interactive plots in GeneNetwork have relied on the fast HK mapping for two decades and we still use this method for mapping omics data sets and computing genome-wide permutation threshold (QTL Reaper code).
R/qtl (version 1.44.9
The original R/qtl mapping package that supports classic experimental crosses including 4-parent F2 intercrosses (e.g., NIA ITP UM-HET3). R/qtl is ideal for populations that do not have complex kinship or admixture (PMID: 12724300). Both R/qtl as implemented here, and R/qtl2 (PMID: 30591514) are available as R suites.
More information on R/qtl mapping models and methods can be found here.

Review and Edit Data

Show/Hide Columns:

  # read into R
  trait <- read.csv("10554150.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("10554150.csv", header = 0, comment = "#")