Trait Data and Analysis for 1460303_at

Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor); distal 3' UTR

Details and Links

Group Mouse: BXD group
Tissue Hippocampus mRNA
Gene Symbol Nr3c1
Aliases Wikidata: GCRST; GR; GRL; GCR; GCCR; Grl-1; Grl1; Gcr; Grl
GeneNetwork: GR; Grl1; Grl-1; GCR; GRL; GCCR; GCRST; GRA; GRB
Location Chr 18 @ 39.410764 Mb on the minus strand
Summary Enables several functions, including glucocorticoid receptor activity; identical protein binding activity; and protein kinase binding activity. Involved in positive regulation of pri-miRNA transcription by RNA polymerase II. Acts upstream of or within several processes, including gland development; glucocorticoid receptor signaling pathway; and regulation of cellular biosynthetic process. Located in cytosol; membrane; and nucleus. Is expressed in several structures, including alimentary system; central nervous system; genitourinary system; lung; and sensory organ. Used to study primary hyperaldosteronism. Human ortholog(s) of this gene implicated in several diseases, including anorexia nervosa; lung disease (multiple); major depressive disorder; obesity; and renal cell carcinoma. Orthologous to human NR3C1 (nuclear receptor subfamily 3 group C member 1). [provided by Alliance of Genome Resources, Apr 2022]
Database Hippocampus Consortium M430v2 (Jun06) PDNN
Target Score BLAT Specificity : 10.150    Score: 203.000
Resource Links Gene    OMIM    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal   
UCSC    BioGPS    STRING    PANTHER    Gemma    ABA    EBI GWAS   


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Transform and Filter Data

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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

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

Show/Hide Columns:

  # read into R
  trait <- read.csv("1460303_at.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("1460303_at.csv", header = 0, comment = "#")
Edit CaseAttributes

BXD Only


  # read into R
  trait <- read.csv("1460303_at.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("1460303_at.csv", header = 0, comment = "#")
Edit CaseAttributes