While we do marker trait correlation using widely used association mapping method, sometimes non significant p value gives significant q value from the FDR test – why? Can anybody explain about this matter?

Thanks for your response. I am providing details about p value and q value for your kind information.

I got p-value from MLM (Mixed linear method) test using TASSEL software. MLM p-values are nominal tests form individual markers and it is not similar with Bonferroni method. Then I followed Storey's Q-method to calculate FDR (http://www.genomine.org/qvalue/index.html).

To determine Q-value using q value soft and I specify the following optional arguments:

Lambda from 0.0 to 0.90 by: 0.05, Choose pi_0 method :Smoother and I got following output (some part) and I indentify as significant q value whereas original p-value shows non-significant eg. value p-value 0.5385(non) q-value-0.04839474(sig) and p-value 0.415(non) q-value 0.03871215 (sig.)

Some more information about p and q value:

p value q value 4.94E-05 2.02E-05 3.11E-08 1.15E-07 8.43E-05 2.82E-05 1.87E-06 2.30E-06 7.89E-06 4.47E-06 5.83E-06 3.58E-06 0.5385 0.04839474 3.80E-06 3.06E-06 0.000133 3.95E-05 1.99E-05 9.78E-06 0.001 0.000193929 0.415 0.03871215

Waiting for your kind response.

can you provide details on your outputs ....details on methods you used to calculate q values and p values

When you say non-significant p-values, do mean non-significant using a correction like Bonferroni but significant using FDR or do you mean non-significant at a nominal threshold like 0.05 but significant using FDR?

Yeah, we need some details. Is this a human study? Are the individuals related - family-based study? Which phenotype(s)?

It is not human study, it's a plant genome. Trait I considered like plant height, weight, etc. I am trying to some QTL related with these trait. It is not family based study.