Trait Data and Analysis for 5612792

Glutamate receptor, ionotropic, NMDA2B (epsilon 2); intron 6

Details and Links

Group Mouse: BXD group
Tissue Hippocampus mRNA
Gene Symbol Grin2b
Aliases Wikidata: NR3; NMDAR2B; NR2B; DEE27; EIEE27; GluN2B; hNR3; MRD6; AW490526; Nmdar2b; GluRepsilon2
GeneNetwork: hNR3; NMDAR2B; NR2B; NMDAR2B; Nmdar2b; GluN2B; GluRepsilon2; AW490526; MRD6; hNR3
Location Chr 6 @ 135.798431 Mb on the minus strand
Summary Enables NMDA glutamate receptor activity; calcium channel activity; and neurotransmitter receptor activity involved in regulation of postsynaptic membrane potential. Involved in negative regulation of dendritic spine maintenance and positive regulation of neuron death. Acts upstream of or within several processes, including behavioral fear response; detection of mechanical stimulus involved in sensory perception of pain; and learning or memory. Located in several cellular components, including cytoplasmic vesicle; lysosome; and synaptic membrane. Part of NMDA selective glutamate receptor complex. Is active in glutamatergic synapse. Is integral component of postsynaptic density membrane. Is expressed in several structures, including adipose tissue; central nervous system; eye; gut; and reproductive system. Human ortholog(s) of this gene implicated in several diseases, including alcohol use disorder; autosomal dominant non-syndromic intellectual disability 6; bipolar disorder; developmental and epileptic encephalopathy 27; and neurodegenerative disease (multiple). Orthologous to human GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B). [provided by Alliance of Genome Resources, Apr 2022]
Database UMUTAffy Hippocampus Exon (Feb09) RMA
Target Score BLAT Specificity : 1.018    Score: 72.000
Resource Links Gene    OMIM    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal   
UCSC    BioGPS    STRING    PANTHER    Gemma    ABA    EBI GWAS   


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Calculate Correlations

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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

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 2023—a version that handles complex populations with admixture and many haplotypes.
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
More information on R/qtl mapping models and methods can be found here.

Review and Edit Data

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  # read into R
  trait <- read.csv("5612792.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("5612792.csv", header = 0, comment = "#")
Edit CaseAttributes

BXD Only


  # read into R
  trait <- read.csv("5612792.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("5612792.csv", header = 0, comment = "#")
Edit CaseAttributes