Found in Translation: A Type 1 Diabetes Genetic Risk Score Applied to Clinical Diagnosis

We are entering a new era in which the explosion of genomic and biological information across multiple developmental and metabolic states is poised to transform the practice of medicine (1). It is now possible to center scientific investigation in the key model organism, the human, and to deploy modern technologies to acquire an unprecedented trove of biological data at multiple time points in the life of an individual (2).

The genomic revolution illustrates how big data sets can begin to yield biological and clinical insight. Concurrent advances in genotyping and sequencing technologies, in our understanding of human genetic variation, and in the statistical methodologies needed to interpret genetic findings have all led to a meteoric expansion in genetic knowledge (3). Meta-analyses of genome-wide association studies (GWAS) and ongoing comprehensive sequencing experiments are yielding a plethora of genetic associations from an agnostic vantage point, which can open unsuspected windows into the pathophysiology of metabolic phenotypes, including type 1 (4) and type 2 (5,6) diabetes.

However, whether this emerging body of knowledge will deepen our understanding of health and disease and lead to improved outcomes in public health remains to be seen. A frequent criticism of large-scale genomic studies is that so far they have failed to deliver on the hyped hopes that the human genome would usher in personalized medicine. Given the empirical observation that most genetic effects for complex phenotypes are modest, skeptics rightly argue that their utility for individualized prediction has not been proven, and they jump to conclude that the substantial public investment in resources, effort, and human capital in this line of investigation has been a wash.

Geneticists may counter that skeptics seem to ignore how genetic studies have illuminated pathophysiological mechanisms that remained obscure, such as the seminal involvement of neuronal circuits in …