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We propose a computational workflow (I3) for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle. We illustrate the use in interpreting genetics of gene expression and understanding genetic regulators of protein phenotypes, particularly in conjunction with information from human population genetics and/or evolutionary history of human genes. We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of gene expression in brains, and if highly expressed, have broad effects on the protein phenotypes studied. We suggest that this workflow presents a general solution to the challenge of complex genetic data interpretation. I3 is available at http://suprahex.r-forge.r-project.org/I3.html. Keywords: Self-organising, Human genetics, Interpretation, Evolution, Machine learning