text: user2020: Is there a way to do GWAS on phenotype data that is not normally distributed?
  For example, if you were measuring a trait as a proportion, and the majority of the data consisted of 0.00 with a long tail of variation. Or, perhaps the data was skewed the other way where the majority of the observations were 100% with a long tail towards zero.
  Is there a GWAS method that is appropriate for these types of distributions?
  
  user4766: as mentioned in previous answers, transformations are frequently used. One commonly used method is quantile normal transformation. Basically you calculate the quantile from the original data and match it to a standard normal distribution. This transforms your data to a standard normal distribution. Even for traits that are naturally normal distributed like height, it is still a good idea to do this transformation as it has been shown to improve power as well.
  
  user73: Yes, that can be done.
  A common approach for these types of analysis is to carry out a transformation on the data first in order for it to have a normal distribution (the most general approach called an inverse normal transformation, among other names), then run the test on the transformed data.
  As an example, for data that are positive and zero-skewed, a log transformation may be appropriate.
  The transformation approach does require variation in the data values, though. For a trait that is exactly zero for most values, it might be better to treat it as a binary/categorical trait for the purpose of association testing, rather than a quantitative trait. Most association testing packages should allow associations for binary traits to be tested.
  
original_text: 2020: <p>Is there a way to do GWAS on phenotype data that is not normally distributed?</p>
  <p>For example, if you were measuring a trait as a proportion, and the majority of the data consisted of 0.00 with a long tail of variation. Or, perhaps the data was skewed the other way where the majority of the observations were 100% with a long tail towards zero.</p>
  <p>Is there a GWAS method that is appropriate for these types of distributions?</p>
   4766: <p>as mentioned in previous answers, transformations are frequently used. One commonly used method is quantile normal transformation. Basically you calculate the quantile from the original data and match it to a standard normal distribution. This transforms your data to a standard normal distribution. Even for traits that are naturally normal distributed like height, it is still a good idea to do this transformation as it has been shown to improve power as well.</p>
   73: <p>Yes, that can be done.</p>
  <p>A common approach for these types of analysis is to carry out a transformation on the data first in order for it to have a normal distribution (the most general approach called an <a href="https://en.wikipedia.org/wiki/Inverse_transform_sampling" rel="nofollow noreferrer">inverse normal transformation</a>, among other names), then run the test on the transformed data.</p>
  <p>As an example, for data that are positive and zero-skewed, a log transformation may be appropriate.</p>
  <p>The transformation approach does require variation in the data values, though. For a trait that is exactly zero for most values, it might be better to treat it as a binary/categorical trait for the purpose of association testing, rather than a quantitative trait. Most association testing packages should allow associations for binary traits to be tested.</p>
  