Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future

Abstract

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Keywords: diabetes mellitus; genetics; medicine; obesity; precision.

Figure.. Drawing the wrong conclusions from clustering of cross-sectional data.

Your alien spacecraft crashes near a ski resort. In an effort to understand the diversity of human life, you apply your favorite clustering algorithm to the life forms visible through the window. You conclude that there are 14 subtypes of human life with distinctive and nonoverlapping appearances and behaviors. However, as you await rescue, you realize that this clustering was only transient and an artifact arising from the restriction to a single cross-sectional view. Over time, you observe that almost all life forms progress through all 4 stages at various points and that membership of a given cluster at a single point of time provides little insight into long-term behavior.