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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 identical protein binding activity; nuclear glucocorticoid receptor activity; and protein kinase binding activity. Involved in several processes, including glial cell differentiation; nuclear receptor-mediated glucocorticoid signaling pathway; and positive regulation of miRNA transcription. Acts upstream of or within several processes, including maternal behavior; regulation of glucocorticoid biosynthetic process; and regulation of gluconeogenesis. Located in cytosol; membrane; and nucleus. Is expressed in several structures, including alimentary system; central nervous system; eye; genitourinary system; and leg muscle. 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, Nov 2024]
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   

Statistics

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

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
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.
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



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  # 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


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  # 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

Other


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