Personalized medicine aims to determine the best therapy for an individual based on personal characteristics. Given that the family history is a risk factor for many diseases there is a strong motivation for the search of inheritable genetic variation that might provide molecular explanations for diseases. In the last couple years, improvements in sequencing technology have helped to scale up these efforts. The HapMap project is an example of these attempts at genome wide characterization of human genetic variation. The project aims to create a haplotype map of the human genome. This map is important because correlating a disease with a haplotype can be used to pin-point the cause of a disease to a genome region. This map based approach is done by first sequencing known sites of polymorphisms, spaced across the genome, in a large population and then associating disease with haplotypes (see a recent example).
Eventually sequencing costs will go down to a point when these map based approaches are replaced by full genome re-sequencing. It looks like there is a consensus that this is just a matter of time. Also, the main sequencing centers seem to be directing more of their efforts to studying variation. If sequencing full genomes is currently too expensive, sequencing coding regions is much more affordable. In two recent papers (Greenman et al. and Sjoblom et al.) researchers have tried to identify somatic mutations in human cancer genomes by sequencing. Greenman and colleagues focused on 518 kinases and searched for mutations in these genes in 210 different human cancers (see post by Keith Robison). Sjoblom and colleagues on the other hand sequenced fewer cancer types (11 breast and 11 colorectal cancers) but did so for 13023 genes. The challenge going forward is to understand what is the impact of these mutations on cellular function.
Instead of sequencing to find new polymorphism is also possible to test the association of previously identified variation with disease by high-throughput profiling. Two recent papers focused on profiling known polymorphisms in cancer tissues using either microarrays or PCR plus mass spec.
Underlying all of these efforts is the idea of genetic determinism. That if I sequence my genome I should know how each variation impacts on my health and what treatment I should use to correct it. It begs the question however of much does it really depend on inherited genetic variation ? The often re-visited Nature vs. Nurture debate. The latests MSB paper highlights the impact of the environment on mammalian metabolic functions. Fracois-Pierre J Martin and colleagues have studied how the microbial gut population affects the mouse metabolism. They have used NMR metabolic profiling in conventional mice, and germ free mice colonized by human baby flora to study this question.
Metabolic analysis of liver, plasma, urine and ileal of both types of mice showed a significant change in metabolites in the different compartments associated with the two microbial populations. This is a very clear example of how the environment must be taken into consideration for future efforts of personalized medical care.
This example also underscores the importance of studying the human microbial associations. As Jonathan Eisen discussed in his blog, maybe we should aim at a human microbiome program.
Nature or Nurture ? In either case, abundant streams of data are forthcoming as the sequencing centers crunch away and new omics tools get directed at studying disease. There will be a lot of work to do in order to understand causal relationships and suggest therapeutic strategies. That might be why Google is taking a look at this. They keep saying they want to organize the worlds information, why not health related data.
The picture was taking from News and View by Ian Wilson:
Top-down versus bottom-up—rediscovering physiology via systems biology? Molecular Systems Biology 3:113