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Trait Data and Analysis for 1419076_a_at

Breast cancer 2

Details and Links

Group Mouse: BXD group
Tissue Hippocampus mRNA
Gene Symbol Brca2
Aliases Wikidata: Not available
GeneNetwork: Fancd1; RAB163
Location Chr 5 @ 150.569266 Mb on the plus strand
Summary Predicted to enable several functions, including gamma-tubulin binding activity; histone acetyltransferase activity; and identical protein binding activity. Involved in several processes, including establishment of protein localization to telomere; male meiosis I; and telomere maintenance via recombination. Acts upstream of or within several processes, including DNA metabolic process; hematopoietic stem cell proliferation; and signal transduction by p53 class mediator. Located in chromosome; cytoplasm; and nucleus. Is expressed in several structures, including alimentary system; central nervous system; early embryo; genitourinary system; and hemolymphoid system gland. Used to study Fanconi anemia complementation group D1 and breast cancer. Human ortholog(s) of this gene implicated in several diseases, including Fanconi anemia (multiple); esophagus squamous cell carcinoma; hereditary breast ovarian cancer syndrome; melanoma (multiple); and reproductive organ cancer (multiple). Orthologous to human BRCA2 (BRCA2 DNA repair associated). [provided by Alliance of Genome Resources, Feb 2025]
Database Hippocampus Consortium M430v2 (Jun06) PDNN
Target Score BLAT Specificity : 11.800    Score: 236.000
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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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("1419076_a_at.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("1419076_a_at.csv", header = 0, comment = "#")
Edit CaseAttributes

BXD Only


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  # read into R
  trait <- read.csv("1419076_a_at.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("1419076_a_at.csv", header = 0, comment = "#")
Edit CaseAttributes

Other


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