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Trait Data and Analysis for CFA_10016

Details and Links

Group Mouse: CFW group
Phenotype
Morphology, musculoskeletal system: Body weight (g) at ~52 days of age [g]
Authors
Parker CC, Gopalakrishnan S, Carbonetto P, Gonzales NM, Leung E, Park YJ, Aryee E, Davis J, Blizard DA, Ackert-Bicknell CL, Lionikas A, Pritchard JK, Palmer AA
Title
Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice.
Journal Nat Genet. (2016)
Database CFW Phenotypes
Resource Links PubMed

Statistics


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

Transform and Filter Data

Edit or delete values in the Trait Data boxes, and use the Reset option as needed.



Outliers highlighted in orange can be hidden using the Hide Outliers button.

Samples with no value (x) can be hidden by clickingHide No Value button.

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).
More information on R/qtl mapping models and methods can be found here.

Review and Edit Data 



            
  # read into R
  trait <- read.csv("CFA_10016.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("CFA_10016.csv", header = 0, comment = "#")
Edit CaseAttributes

Samples


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