In genetic association studies, important and common goals are the
identification of single nucleotide polymorphisms (SNPs) showing a
distribution that differs between several groups and the detection of
SNPs with a coherent pattern. In the former situation, tens of thousands
of SNPs should be tested, whereas in the latter case typically
several ten SNPs are considered leading to thousands of statistics that
need to be computed.
A test statistic appropriate for both goals is Pearson’s Chi^2-statistic.
However, computing this (or another) statistic for each SNP or pair
of SNPs separately is very time-consuming.
In this article, we show how simple matrix computation can be
employed to calculate the Chi^2-statistic for all SNPs simultaneously