?

Trait Data and Analysis for 10551483

EP300 interacting inhibitor of differentiation 2

Details and Links

Group Mouse: BXD group
Tissue Hippocampus mRNA
Gene Symbol Eid2
Aliases Wikidata: Not available
GeneNetwork: Eid2; Cri2; EID-2
Location Chr 7 @ 28.267881 Mb on the plus strand
Summary Predicted to enable SMAD binding activity and transcription corepressor activity. Predicted to be involved in several processes, including negative regulation of transcription by RNA polymerase II; negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway; and transforming growth factor beta receptor complex assembly. Predicted to be located in nucleus. Predicted to be active in nucleoplasm. Is expressed in several structures, including heart; liver; lung; metanephros; and pancreas. Orthologous to human EID2 (EP300 interacting inhibitor of differentiation 2). [provided by Alliance of Genome Resources, May 2025]
Database UTHSC BXD Aged Hippocampus rev3 Affy Mouse Gene 1.0 ST (Sep12) RMA
Resource Links Gene    OMIM    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal   
UCSC    BioGPS    STRING    PANTHER    Gemma    ABA    EBI GWAS   

Statistics


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 
Sample Correlation
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.
Literature Correlation
The Literature Correlation (Lit r) between this gene and all other genes is computed
using the Semantic Gene Organizer and human, rat, and mouse data from PubMed. Values are ranked by Lit r, but Sample r and Tissue r are also displayed.
More on using Lit r
Tissue Correlation
The Tissue Correlation (Tissue r) estimates the similarity of expression of two genes or transcripts across different cells, tissues, or organs (glossary). Tissue correlations are generated by analyzing expression in multiple samples usually taken from single cases.
Pearson and Spearman Rank correlations have been computed for all pairs of genes using data from mouse samples.

Mapping Tools

You currently have no collections.

You currently have no collections.
You currently have no collections.
You currently have no collections.
GEMMA
GEMMA maps with correction for kinship using a linear mixed model and can include covariates such as sex and age. Defaults include a minor allele frequency of 0.05 and the leave-one-chromosome-out method (PMID: 2453419, and GitHub code).
Haley-Knott Regression
HK regression (QTL Reaper) is a fast mapping method with permutation that works well with F2 intercrosses and backcrosses (PMID: 16718932), but is not recommended for admixed populations, advanced intercrosses, or strain families such as the BXDs (QTL Reaper code).
R/qtl (version 1.44.9)
R/qtl maps using several models and uniquely support 4-way intercrosses such as the "Aging Mouse Lifespan Studies" (NIA UM-HET3). We will add support for R/qtl2 (PMID: 30591514) in the near future—a version that handles complex populations with admixture and many haplotypes.
R/qtl2 (version 0.36)
R/qtl2 (aka qtl2) is a reimplementation of the QTL analysis software R/qtl, to better handle high-dimensional data and complex cross designs.. R/QTL2 documentation
Pair Scan (R/qtl v 1.44.9)
The Pair Scan mapping tool performs a search for joint effects of two separate loci that may influence a trait. This search typically requires large sample sizes. Pair Scans can included covariates such as age and sex. For more on this function by K. Broman and colleagues see www.rdocumentation.org/packages/qtl/versions/1.60/topics/scantwo
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("10551483.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("10551483.csv", header = 0, comment = "#")
            
          
Edit CaseAttributes

Samples


Loading...