**0**wrote:

Hi,

reading this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143681/pdf/1753-6561-8-S1-S28.pdf

Says the following: **a transformation matrix was calculated as the inverse of the decomposition of the kinship matrix. Then, this transformation matrix was used to decorreate the family data by multiplying it with both the phenotype and genotypes matrices.**

I did that on a non-normal phenotype, but I don't quite believe the results, because my dataset has some clear outliers and after the decorrelation, I lost some.

Is there someone who can validate this approach? searching I didn't find other papers that use the same methodology.

According to the paper you linked to, this method is not published yet. However, I believe that decomposition of the kinship matrix refers to eigendecomposition of the matrix. If so and only some principal components are retained, the original matrix is only approximated. This is usually done to remove some noise but it could lead to the loss of outliers that you observe.

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