From Darwin's Finches to Canaries in the Coal Mine — Mining the Genome for New Biology

From Darwin's Finches to Canaries in the Coal Mine — Mining the Genome for New Biology

David J. Hunter, M.B., B.S., Sc.D., David Altshuler, M.D., Ph.D., and Daniel J. Rader, M.D.

The observations of finches that Charles Darwin made while in the Galapagos contributed to his theory of the origins of interspecies differences, ultimately leading to our understanding of mutation and natural selection as drivers of phenotypic variation. Now, more than 150 years later, genomewide
association studies have identified more than 100 new chromosomal regions at which DNA variation influences risk of common human diseases and clinical phenotypes.1 Since previous approaches to identifying genetic causes of common diseases have met with very limited success, this moment constitutes a watershed in the history of genetics in medicine.

Although associations with common single-nucleotide polymorphisms (SNPs) identified in genomewide association studies have proven robust and reproducible (see diagram), nearly all these SNPs are associated with relative risks of 1.5 per copy or less. In aggregate, the SNPs discovered to date account for a small fraction of the overall inherited risk of each disease. The mechanisms whereby DNA variation in most of these regions influences disease are not obvious from our previous understanding of pathophysiology, the genes in the regions, or the nature of the DNA changes observed.

For example, in this issue of the Journal, Pharoah et al. (pages 2796–2803) discuss six common markers of risk for breast cancer that have been discovered through genomewide association studies. Each marker has a modest influence on a woman's risk of disease; none act through well-understood mechanisms. Pharoah et al. consider the potential usefulness of these markers in targeting patients who would benefit from screening for early detection of disease and argue that as more associated loci are identified, risk-prediction algorithms will need to be based on the best available risk estimates. The authors conclude that stable algorithms may eventually be useful in identifying groups of women with clinically meaningful differences in risk.

Do the small effects of multiple genes, the modest fraction of heritability explained, and the lack of overlap with our previous biologic understanding suggest an underlying weakness in the genomewide approach? We believe not. Rather, these features illuminate the limits of current knowledge at the interface of three historically distinct approaches to understanding disease causality — genetic mapping, epidemiology, and studies of pathophysiological mechanisms.

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