It is possible to obtain a good test statistic value by chance alone. Multiple testing corrections help ensure this is not the case.

HelixTree supports the following multiple testing corrections.

Bonferroni Adjustment
The Bonferroni adjustment seeks to estimate the probability that an association test’s results would be significant by chance alone, given the number of times the test was performed.

False Discovery Rate
The False Discovery Rate (FDR) option calculates the FDR for each statistical test selected. This test is based on the p-values from your original test.

The interpretation of the FDR is “What would the rate of false discoveries (false positives) be if I accepted all of the tests whose p-value is at or below the p-value of this test?”
See the Formulas and Theories chapter of the HelixTree Manual for a more comprehensive explanation of this statistic.

Simes Method
When analyzing data with many covariates, it is desirable to extract those of greater significance. When plotting p-values (raw or adjusted), HelixTree offers consolidation of p-values using the Simes method over a moving window. The Simes method, a process similar to finding the FDR, is used over those p-values corresponding to covariates close to each other within the moving window of covariates.
See the Formulas and Theories chapter of the HelixTree Manual for a more comprehensive explanation of this statistic.

Single Value and Full Scan Permutations
Permutation testing is another, perhaps more robust way, of determining if you may have obtained a good test statistic value by chance alone.

With single value permutations, the dependent variable is permuted and the given statistical test is performed (using the given model on the given marker). This process is repeated the number of times you select. The permuted p-value is the fraction of times the test came out equal to or better than it did with the non-permuted dependent variable.

Full-scan permutation testing is similar to single-value permutation testing. The difference is the p-value is the fraction of times the statistical test on the given marker came out better than the best value of the same statistical test done within that same permutation of the dependent variable over all the markers tested using this test statistic.
See the Formulas and Theories chapter of the HelixTree Manual for a more comprehensive explanation of this statistic.

Carlson SNP Tagging
HelixTree offers a SNP tagging feature based on the Carlson method. This method is based on the R² LD statistic and determines groupings of markers which are in tight correlation with an individual marker or markers (tagging markers) within another grouping. By using only tagging markers for analysis, rather than the entire set, you can significantly reduce the number of multiple-testing corrections without substantial loss of statistical power.