It may be possible that a good test statistic value that you have obtained would have occurred by chance alone. Multiple testing corrections are designed to help you ensure, if possible, that this is not the case.

You may optionally select one or more of the following multiple test corrections.

18.5.1 Bonferonni Adjustment

The Bonferonni adjustment multiplies each individual p-value by the number of times that same test was performed. This value, which is quite conservative, seeks to estimate the probability that this test would have come out this well by chance at least once from all of the times this test was performed. (The number of times this test was performed will be equal to the number of bi-allelic markers processed. Other types of tests on the same markers are not counted.)

18.5.2 False Discovery Rate

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

The interpretation of the False Discovery Rate 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 26.20.

18.5.3 Permutation Testing

Permutation testing is another way of finding if you may have obtained a significant test statistic value by chance alone.

NOTE: Permutation testing is available only for non-exact tests. (Exact tests already use permutation techniques.)

NOTE: Genomic control is not available concurrently with permutation testing. Genomic control works directly on the chi-square results of those tests which incorporate a chi-square statistic. (If you did do permutation testing after applying genomic control, you would get all of the same answers, because genomic control is applied using a constant multiplier on all the chi-square values.) (See 18.6.8.)

18.5.3.1 Single Value Permutation Testing

With single value permutations, the dependent variable is permuted and the given statistical test using the given model on the given marker is performed. This process is repeated the number of times that you select (counting the original test as one “permutation”). The permuted p-value is the fraction of times that the test came out as significant as or more significant than it did with the non-permuted dependent variable.

18.5.3.2 Full Scan Permutation Testing

Full-scan permutation testing is the same as single-value permutation testing with the exception that the p-value is the fraction of times that the best value of the same statistical test that was done within that same permutation of the dependent variable over all the markers tested using this test statistic was more significant than the statistical test on the given marker.

See 26.21 for a more detailed explanation and examples of permutation testing.