Scientists have spent decades mapping human disease genes. Initially, most of the effort was focused on identifying genetic mutations responsible for single-gene disorders, like Duchenne muscular dystrophy, which is caused by a recessive X-linked mutation. More recently, with new and sophisticated technologies making it possible to survey entire genomes for links between loci and disease by way of genome-wide association studies (GWAS), scientists have made tremendous progress in understanding the genetic basis of more complicated polygenic diseases, such as heart disease and cancer.
In an effort to keep track of the burgeoning body of data regarding gene-disease associations, the National Cancer Institute (NCI) has posted an updated list of published, peer-reviewed GWAS. The list includes all studies that involved analysis of at least 100,000 single nucleotide polymorphisms (SNPs) and found SNP-disease or SNP-trait associations. For example, entries range from a 2005 study showing an association between the CFH gene on chromosome 1 and age-related macular degeneration (Klein et al., 2005) to a 2008 study showing an association between three SNPs on chromosome 6 and neuroblastoma, a childhood cancer (Maris et al., 2008). While all these new data points provide scientists with plenty of novel insights into the genetic and molecular mechanisms of human disease, researchers have a long way to go before much of this knowledge can actually be used to treat or cure human disease. The first step in this journey is to determine how to best organize the data.
Organizing Gene-Association Data
Figure 1: Human disease network.
In the HDN, each node corresponds to a distinct disorder, colored based on the disorder class to which it belongs, the name of the 22 disorder classes being shown on the right. A link between disorders in the same disorder class is colored with the corresponding dimmer color and links connecting different disorder classes are gray. The size of each node is proportional to the number of genes participating in the corresponding disorder (see key), and the link thickness is proportional to the number of genes shared by the disorders it connects.
As an initial effort toward organization of the vast and continually expanding set of gene-association data, a group of scientists from several different research institutions in the United States and Korea have developed what they call the "human disease network," a visual map of all human diseases with known underlying genetic associations; this map also details the genetic connections between these diseases (Figure 1; Goh et al., 2007). The map is made up of nodes and branches. Each node represents a disease, and the size of the node reflects the number of genes known to be associated with that disorder. The thickness of the branches that connect various nodes is a reflection of the number of genes shared by the connected diseases.
The idea behind the map is that most diseases with known genetic associations seem to share most of their genes with other diseases. Often, this is true of diseases that you would probably never imagine have anything to do with each other—consider type 2 diabetes and prostate cancer, for instance. Believe it or not, both of these conditions appear to be influenced by variation in the JAZF1 gene (Zeggini et al., 2008). As Dr. Francis S. Collins, the Director of the National Human Genome Research Institute, was quoted in a New York Times article, "I'm shaking my head with disbelief that two genes would pop up in these two diseases that have absolutely nothing in common" (Pollack, 2008).
By mapping the genetic connections among diseases, scientists are hopeful that they will be able to learn more about the molecular pathways that cause disease—not just to satisfy their curiosity, but also to aid in designing and developing new ways of treating or curing diseases. But how? Quite simply, biologists hope that by knowing when two diseases are connected through "six degrees of separation," they might find instances in which the treatment that works well for one of the diseases (e.g., a certain pharmaceutical drug) turns out to work for the other disease as well.
To complement the human disease network, the aforementioned U.S. and Korean scientists have also developed a disease gene network. Instead of portraying how various diseases are connected by genes, the disease gene network depicts how various genes are connected by diseases. Here, nodes represent genes, while branches represent diseases. The size of each node reflects the number of disorders known to be associated with that particular gene (Figure 2).
Benefits of Gene-Association Mapping
![Disease gene network.](javascript:dispOrigImg("6437/10[1].1073_pnas0701361104-fig2b_full.jpg", "Disease gene network.", "Y", "Figure 2", "1219953851038-2401305293_1", 'Y', "Copyright 2007 National Academy of Sciences, Kwang-Il, et. al., The human disease network, Proceedings of the National Academy of Sciences 104, 8685-8690", '740', '459', "http://www.pnas.org"); "Disease gene network.")
Many biologists argue that gene-disease networking is not only a first step toward making this knowledge useful for health care, but it is also a first step toward completely revamping how physicians think about disease—in particular, how they categorize various illnesses. It is hoped that instead of viewing human sickness in terms of the tissue involved (e.g., cancer of the breast), physicians will start thinking in terms of the molecular pathways involved. To some extent, doctors are already doing this, such as with invasive breast cancer, which is categorized as being either Her2/neu-positive or Her2/neu-negative. Her2/neu is a receptor on the surface of breast cancer cells that is coded for by the Her2/neu gene. Women diagnosed as Her2/neu-positive have many more of these receptors and are more likely to benefit from certain forms of treatment than are Her2/neu-negative patients (Burstein, 2005).
Eventually, some scientists say, all diseases will be thought of in terms of which genes a patient has, or at least which genes are active. As science writer Andrew Pollack described in the aforementioned New York Times article, shifts in the way scientists think about disease have occurred throughout history. For example, consider the work of Carolus Linnaeus, the eighteenth-century French naturalist credited with devising a system of classifying organisms (e.g., into species and genera) that is still used today. Linnaeus also categorized diseases into 11 classes: painful disease, motor disease, and so on. Approximately one hundred years later, when the stethoscope was developed, physicians starting thinking about anatomy instead of symptoms and were able to detect problems, such as high blood pressure, in patients with no obvious symptoms. Now, with scientists' tremendous and rapidly growing understanding of the genetic and molecular basis of human disease, medicine might be on the verge of yet another radical shift—this time, from consideration of anatomy and symptoms to consideration of genes.
As Pollack explained, not everybody is convinced that this shift will be beneficial. Some scientists think that many ambiguities will remain—for instance, there are already cases in which people diagnosed with one or another genetic variant end up never showing the symptoms typically associated with that variant. More serious is the fact that most polygenic diseases, like heart disease and cancer, are caused by more than genetic variations. The environment also plays a vitally important role in human disease—one that cannot be determined by mapping alone.References and Recommended Reading
Burstein, H. J. The distinctive nature of Her2-positive breast cancers. New England Journal of Medicine 353, 1652–1654 (2005)
Goh, K. I., et al. The human disease network. Proceedings of the National Academy of Sciences 104, 8685–8690 (2007)
Klein, R. J., et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005)
Maris, J. M., et al. Chromosome 6p22 locus associated with clinically aggressive neuroblastoma. New England Journal of Medicine 358, 2585–2593 (2008) doi:10.1056/NEJMoa0708698
Pollack, A. "Redefining disease, genes and all." New York Times, May 6, 2008.
McCarthy, M. I., et al. Genome-wide association studies for complex traits: Consensus, uncertainty, and challenges. Nature Reviews Genetics 9, 356–369 (2008) (link to article)
Zeggini, E., et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature Genetics 40, 638–645 (2008) doi:10.1038/ng.120 (link to article)
