Editors' Introduction to the Special Issue

Any human enterprise that consumes billions of dollars, especially when those dollars are those of citizen tax payers, should be subject to at least occasional scrutiny and stock-taking (Joyner, Paneth, and Ioannidis 2016). This Special Issue of Perspectives in Biology and Medicine is an attempt to do just that: to ask whether the massive investment of money, equipment and human scientific talent that has been poured into studying the human genome under the assumption that this enormous scientific endeavor will advance human health has been worth it thus far, and whether it is likely to bear fruit in the future

The expression that best encapsulates the goal of using genomic information to alter trajectories of disease has varied from time to time in the 15 years since the completion of the Human Genome Project (HGP) in 2003. "Personalized medicine" held sway for the first few years, but that term has now been eclipsed by "precision medicine." Whichever term is used, the underlying principle is much the same, namely, that what modern medicine most lacks is the incorporation of genetic information into health care (Letai 2017). If only health-care providers would use host genomic information to decide which drugs to give their patients—whether with curative or preventive intent, whether based on the drugs suitability for the disease or for the patient—the results, we are told, would be transformative.

The building blocks for the widespread adoption of precision medicine are well known (Aronson and Rehm 2015). First, whole genome sequencing is to be performed on the entire population. Next, this genomic data must be linked with the human disease characteristics that geneticists like to refer to as the phenotype, using the large-scale study design referred to as the gene-wide association study (GWAS). The potential of GWAS studies has been enlarged in recent years by another massive biomedical investment, the creation of the electronic medical record, which is viewed as a reliable source of information about the clinical states under investigation. For all this to help patients, however, health-care providers must be educated in the nuances of genomics so that they know how to use this information to make the decisions that are promised to dramatically alter the health of the patient. The extraordinary array of assumptions that lie behind this rosy scenario have rarely been subject to serious discussion, although some occasional suggestions about problems have been made (Joyner and Paneth 2015).

In this issue of Perspectives in Biology and Medicine, a multidisciplinary group of scientists take on these assumptions and hold them up to scrutiny from a variety of perspectives. The disciplines of the 15 authors include anthropology, epidemiology, genetics, immunology, microbiology, law, medicine, physiology, pharmacology, and public health. Our nine physician-authors have training in anesthesiology, cardiology, gastroenterology, pediatrics, infectious diseases, internal medicine, and global health.

Precision medicine is built on the same foundational assumption used to justify the HGP: that there is a tight linkage between an individual's genotype and clinical disease (Collins 1999). This assumption leads to the expectation that information about the genome can predict disease risk with precision. But fundamental scientific and philosophical questions need to be asked about genomic research itself, and about how that research intersects with the biological matrix from which human genomic information is extracted, not to mention the societal matrix in which all human biology is embedded.

Questions about the epistemology of hypothesis-free big data in genomic medicine and its limitations are raised by Sui Huang, while Ana M. Soto and Carlos Sonnenschein address the limitations of reductionist approaches applied to the study of complex biomedical phenomena. The latter two authors propose switching to an organicist perspective—a theory of organisms—which they see as sorely needed to provide an appropriate foundation for the understanding of biological organization, and the framing of observations and experiments. All three authors consider the broader problem of how we infer causality in complex systems, raising serious questions about the capacity of big data to accurately predict disease.

Kenneth M. Weiss considers the conceptual biases implicit in the...