Changes

that includes all three files is also available.

that includes all three files is also available.

=== Generating a Q-Q Plot ===

== Generating a Q-Q Plot ==

 

=== Background ===

One of the most common analysis in a genomewide association is to generate a Q-Q plot which compares observed test statistics with those expected to occur by chance when a similar number of markers is examined. The approach works because in a [[GWAS]] we expect that the vast majority of variants will show no evidence of association with the trait of interest. For this exercise, you should pick one of the three datasets above and load into an appropriate statistical package (like R). If no suitable statistical package is available, Microsoft Excel will do.

One of the most common analysis in a genomewide association is to generate a Q-Q plot which compares observed test statistics with those expected to occur by chance when a similar number of markers is examined. The approach works because in a [[GWAS]] we expect that the vast majority of variants will show no evidence of association with the trait of interest. For this exercise, you should pick one of the three datasets above and load into an appropriate statistical package (like R). If no suitable statistical package is available, Microsoft Excel will do.

Here are the basic steps you will need to carry out to generate a Q-Q plot:

Here are the basic steps you will need to carry out to generate a Q-Q plot:

* Match each expected p-value with an expected p-value, which is simply its rank divided by M.

* Match each expected p-value with an expected p-value, which is simply its rank divided by M.

* Plot expected p-values along the X axis and actual p-values along the X-axis.

* Plot expected p-values along the X axis and actual p-values along the X-axis.

Compare your generated Q-Q plot with the three examplar plots below:

Compare your generated Q-Q plot with the three examplar plots below:

This final plot represents an ideal situation, where the majority of markers reassuringly fit null expectations and a small number of markers (perhaps tagging elusive loci impacting the trait of interest) show evidence for association.

This final plot represents an ideal situation, where the majority of markers reassuringly fit null expectations and a small number of markers (perhaps tagging elusive loci impacting the trait of interest) show evidence for association.