Finding an association can be confuted by population stratification or variations in test equipment collectively referred to as batch effects. This is because a condition may be more prevalent in one group than another, resulting in a spurious associations between the condition or trait being tested and genetic characteristics that vary between the groups.

While it is good practice to base studies on as homogeneous a group of test subjects as possible, it has been noted that even the mild variation in genetic characteristics among those who classify themselves as “Caucasian” can be problematic enough to confound a study performed over thousands of genetic markers.

For most genetic models with binary or continuous traits, HelixTree now offers stratification correction using one or both of the following methods:

Principal Components Analysis
Originally pioneered by the Broad Institute, HelixTree uses an enhanced version of Eigenstrat-based principal component analysis to subtract patterns in your data caused by stratification and batch effects. By using this method, the influence on associations resulting from stratification can be minimized or eliminated altogether.
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Genomic Control
Genomic control indicates that the chi-squared distribution of statistics from association tests is confounded by stratification and will be more “spread out” than it should be. Using HelixTree, you can employ genomic control to assess how close to ideal the chi-squared distribution of an association test is.
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