The 20^th century obsession with differentiating disease causality determined by whether we have XX or XY genes may be outdated. Now that we are accruing large datasets of genetic material in biobanks, the impact of the other 22 genes and their billions of variations is emerging. 

Block quote: The curation of Biobanks should not be left to the researchers alone.

Biobanking is the process of collecting bodily fluids or tissue for research purposes to improve our understanding of health and disease. The UK Biobank, for example, has done a lot of work to debunk the X/Y chromosomal axis as a major disease differentiator.  Their recent studies have found no sex differences in the risk of getting Type 2 diabetes and hypertension. However, they did little to clear the subsequent confusion that may result in dispelling the sex chromosome hypothesis, when another biobank study identified 1000 sites on the genome that signal the propensity for hypertension. 

The US biobanks, which are less Eurocentric, focus on a different range of diversity and have the potential determine whether the differential treatment of hypertension by race has a scientific basis or is the product of racism. The Penn Medicine Biobank, which has approximately one third participants of non-European ancestry, could particularly contribute to this inquiry, but has not done so. 

A great deal of new information is gathered in biobanks. Some of these samples are stored and used indefinitely. Modern medical ethics require researchers to obtain the informed consent of participants, but privacy issues nonetheless abound.  

Biobanks acknowledge the issue of privacy and yet give participants few assurances. As evidenced by this disclaimer on the University of Pennsylvania biobank site:

“It is impossible for us to completely guarantee your privacy. Genomic privacy is a developing concept that will likely require new laws and regulations over time. You should consider the potential impact before deciding whether to participate.”

The large volume of date contained in biobanks is too complex, currently, for our human minds to decipher. When scientific and medical complexity increases, three things occur in sequence. First, there is a search for a single explanation. Then, once one explanation gains acceptance, a polar opposite emerges and gains traction. Finally, the picture becomes blurred with intermediate explanations that leave most of us bewildered and sceptical.

It used to be that a trusted advisor, for example your regular clinician, would provide an interpretation that was palatable, but that is no longer the case. Science is amassing data at such a rate that interpretation at the level of the individual patient requires a combination of skills beyond one physician – real-time data extraction, synthesis of information, generalised interpretation, and finally application to an individual. All this is unlikely to be accomplished in the 5-10 minutes allocated to an individual patient consultation.

The curation of large biobanks with rich genetic data is currently unregulated and at the mercy of those coders who frame the AI used to describe and interpret the data. There is little  scrutiny about the sociocultural directions of the research and what questions need to be answered as a priority . This needs to change.

The overarching aim of Biobanks is to uncover the genetic underpinning of health and disease. The curation of these datasets should not be left to the researchers alone.