Q: Public participation by contributing specimens to assess personal genomic information is rapidly increasing. How might this expansion of testing become actually useful for the health of the public?
A: The fields of public health and medicine share a common objective of maintaining the well- being of people, but with very different modes of operation. Medicine has historically focused on interventions to treat illness and restore health, while public health seeks to prevent disease. Hence, medicine generally focuses on the needs of the individual, while public health focuses on the population. Because of this division, policies that serve the common good are limited by the individuals who do not realize the intended benefit. Could prevention become personalized with genomic information so that public health is synonymous with personal health?
The two perspectives can be illustrated by the approaches for dealing with nutrition. Various food pyramids purporting to guide the amount of milk products, fruits and vegetables, grains and legumes, meat, and water have been proposed amid controversy. A school system that adopts a public health approach to the problem might implement a food pyramid based menu, thus increasing its compliance but at the risk of variable responses among its students. Some individuals may have a gene or a lifestyle that makes them benefit from one kind of nutrient while others may have undesired weight gain. If it were possible to identify those individuals who would benefit most from a particular nutrient, then it may be possible to optimize the program as a whole, by avoiding side effects and increasing efficiency. In effect, this effort would bring together aspects of the medical and the public health perspectives by personalizing the public health strategies, thus providing a method by which individuals can optimize their own health, and in the process, benefit the population at large.
I believe the current consumer genomic products offer a path to personal health. Many citizens are purchasing these services and depositing their genomes with companies that will offer various interpretations of the genomic data, as the consumer requires. So far, the leading interest has been ancestry, but business models are evolving to offer consumers a variety of windows on their genome to guide diet and exercise and even expose disease susceptibility (information that can be “opened” by purchasing different products and applications). The genome is sequenced or genotyped once, the information is stored and queried repeatedly depending on the need or interest.
In this scenario, most of the algorithms leading to an interpretation will not be results of causative inference. Knowledge that a particular genetic polymorphism will produce a given effect on the outcome is really limited to genetic diseases, some pharmacokinetic polymorphisms and various cancers. A practical standard would be to determine whether ensembles of particular polymorphisms are associated with the outcome, a relationship that most likely is not causative.
We must be alert to this developing model. This is not science, which has the ultimate goal of understanding mechanisms of pathophysiology. This is modeling for predicting outcomes based on associations that exist between individual genomic characteristics and the outcome of interest. This would allow us to improve our prediction of the response by incorporating information that is related to the outcome, but when we cannot be certain that the associations are causal, we must maintain a level of caution in using the predictions.
There are various technical and medical problems with this model. The first is the quality of the genomic data. In the laboratory profession we strive to maintain a given level of quality and reproducibility so the test results are clinical grade and support medical interventions. But in the genome storage model, there is likely none because the algorithms are sampling multiple polymorphisms, and some redundant ones because of linkage disequilibrium may become the actual quality control. The second limitation is the relative contribution of phenotypic characteristics versus genetic markers to a prediction. It may be that for some predictions, the bulk of the prediction is carried by the phenotype, and the genotype is a small percentage of the predictive power. But will this matter to the consumer?
I believe the genie is out of the bottle concerning consumer genomics, and that an antagonistic view of the field by the medical profession is not in the best interest of the consumer or our profession. In the evolving genome storage model, a number of vendors could provide the initial sampling of the personal genome, and yet other vendors could support a marketplace of algorithms to interpret the genome and provide guidance. Competing algorithms will probably exist so that the consumer can select or compare for the same prediction. The models become fluid and there will be various versions released to the market, each claiming to be more precise. If a vendor persuades the consumer to give feedback information on individual response, the models could become self-improving. I predict that this world of genome data obtained once coupled to a diversity of algorithms for querying will allow public health to become personalized. I further suspect that the interest in ancestry will propel much demand for algorithms related to longevity, wellness, nutrition and fitness. These are indeed the historically desired outcomes of sound public health policy, but enabled by the interpretations of the personal genome of each individual.
Gualberto Ruaño’s contact info is included in the author affiliations at the top of this page.
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