Say a database were to store human omics datasets. The human subjects are known and the sample size is rather small in size initially (n=500). The database contains genomics, transcriptomics, proteomics, gut metabolomics, and epigenomics. Since the sample size is rather small initially, it would be important that certain identifying features are mitigated so that individual subjects cannot be identified. This leads me to two questions:
What identifiable features could be present in these different types of raw data? (Such as race, sex, age, hair color, eye color, height, where someone has lived, and anything else I may not even be considering)? Which of these omics types would be the most and least dangerous for identifying individuals? How can the identifiable nature of these data be mitigated?
If environmental metagenomics is also collected by these human subjects, is it possible to identify the human subjects by contamination? (i.e. some of the reads from the metagenomics data inadvertently contain human reads?) How can the identifiable nature of these data be mitigated?
I think this subject may be a bit futuristic, but I am very uninformed. If there are any references that provide additional thinking about these topics, please kindly share. Thank you for sharing your thoughts.