Whole genome sequencing and genome-wide association studies (GWAS) clearly demonstrate the complexity of common diseases and in the majority of cases risk cannot be defined by a single gene mutation. As a result of the wealth of genetic data, modeling of complex traits has become ever more challenging.
I would be very interested in your opinion on where we should be going in designing better cell models and at the end better animal models. Needless to say that monocausal approaches are dated, even though most of our attempts to understand the molecular underpinnings of diseases are based on monocausal models, but the important question is how complex diseases could be modeled in a more appropriate way? Your opinion is most valued.
If you are looking for "correlation" (in the wide sense) between whole-genome data and a disease, and if you are using naive approach (i.e. without any prior information on the genetic mechanisms of the disease) then you should try various datamining methods that are tailored to find "system in the madness of data".
But there are some pitfalls of the way: 1) datamining methods may find some correlation between genes and disease, but "correlation does not mean causation"; 2) given the vast size of the genetic data you will inevitably face the problem of spurious correlations (that appear statistically significant by pure chance); 3) genome is not the only cause of disease, so there are lots of unobserved intervening factors that make the correlation analysis much harder and less reliable.
Speaking of cell and animal models, there is no direct bridge between them and mathematical/statistical analysis of gene data yet (my personal opinion as a biomathematician). Only when the internal mechanisms of a cell are understood to the point enabling us to build a fully functional "electronic cell" (i.e. a mathematical model of a cell in its entirety) there will be a direct way connect the genetic data and the behavior/diseases of the cell.
On the positive side, mathematical/statistical methods applied to the gene data may provide some candidate hypotheses for the experimenter.
I certainly agree that mathematical and statistical modelling will be indispensable to better define how genetic variance may affect disease pathways and complex traits. However, systems approaches often fall short of delivering viable hypotheses, particularly, when genes or gene pathways are poorly described. This may result in a hypotheses that are skewed towards well-described genes.
My question regarding better models to characterize common diseases and complex traits is related to experimental models, rather than data mining. For instance, induced pluripotent stem cells (iPS) cells, isolated from tissues of patients and healthy controls may be a superior model to the single gene approach we are generally pursuing. I would be interested to understand whether there are other options to 'custom-design' model systems.
- Badges
- Science topic
- Similar topics
- Medicine
- Disease
- Disease Models