Trait Data and Analysis for 1369001_at

Cholinergic receptor, nicotinic, alpha 3

Details and Links

Group Rat: HXBBXH group
Tissue Peritoneal Fat mRNA
Gene Symbol Chrna3
Aliases Wikidata: BAIPRCK; LNCR2; NACHRA3; PAOD2; (a)3; A730007P14Rik; Acra-3; Acra3
GeneNetwork: Not available
Location Chr 8 @ 55.401778 Mb on the minus strand
Summary Enables acetylcholine binding activity; heterocyclic compound binding activity; and postsynaptic neurotransmitter receptor activity. Involved in several processes, including acetylcholine receptor signaling pathway; regulation of synaptic vesicle exocytosis; and response to nicotine. Located in several cellular components, including neuronal cell body; plasma membrane raft; and postsynaptic density. Part of acetylcholine-gated channel complex. Is active in cholinergic synapse and dopaminergic synapse. Is integral component of postsynaptic specialization membrane. Used to study visual epilepsy. Human ortholog(s) of this gene implicated in lung cancer. Orthologous to human CHRNA3 (cholinergic receptor nicotinic alpha 3 subunit). [provided by Alliance of Genome Resources, Apr 2022]
Database MDC/CAS/ICL Peritoneal Fat 230A (Jun05) RMA 2z+8
Target Score BLAT Specificity : 20.000    Score: 232.000
Resource Links Gene    OMIM    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal    PhenoGen   
UCSC    BioGPS    STRING    PANTHER    Gemma    EBI GWAS   

Statistics


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 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 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("1369001_at.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("1369001_at.csv", header = 0, comment = "#")
            
          
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Samples


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