Trait Data and Analysis for 57581_TIPAWATLSASQLAR_2

Heterochromatin protein 1, binding protein 3

Details and Links

Group Mouse: BXD-Longevity group
Tissue Hippocampus Proteome
Gene Symbol Hp1bp3
Aliases Wikidata: HP1-BP74; HP1BP74; Hp1bp74
GeneNetwork: Hp1bp74
Location Chr 4 @ 139.993841 Mb on the plus strand
Summary Predicted to enable DNA binding activity and nucleosome binding activity. Predicted to be involved in several processes, including cellular response to hypoxia; heterochromatin organization; and regulation of nucleus size. Predicted to be located in nuclear speck. Predicted to be part of nucleosome. Predicted to be active in chromosome and nucleus. Orthologous to human HP1BP3 (heterochromatin protein 1 binding protein 3). [provided by Alliance of Genome Resources, Apr 2022]
Database JAX BXD Hippocampal Proteome (Feb19)
Resource Links Gene    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal   
UCSC    BioGPS    STRING    PANTHER    Gemma    ABA    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 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

Show/Hide Columns: 


            
  # read into R
  trait <- read.csv("57581_TIPAWATLSASQLAR_2.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("57581_TIPAWATLSASQLAR_2.csv", header = 0, comment = "#")
Edit CaseAttributes

Samples


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