The field of Human Genetics has developed as the study of Mendelian-monogenic diseases. Their identification not only enables diagnosis-prognosis of a given disease but also uncovers the molecular mechanism of the underlying condition. On the other end of the spectrum there are common-complex diseases. These multi-factorial diseases, such as cancer, heart disease, diabetes and stroke also have some level of genetic component as shown by the family pedigrees of the patients. However, they are caused by complex interactions of several genetic variants as well as environmental factors.
Figure: Frequency of the variants and the sizes of their effects have inverse correlation (image from http://www.sciencemag.org)
The Mendelian diseases and the associated variants/mutations are rare because they are selected against in the population (unless the disease is not harmful but in that case it will be classified as a trait). Because the Mendelian variants are simple and have high effects, they predict the disease with high confidence. On the other hand, variants for diseases such as cancer or diabetes have low effects but they are predicted to be rare or common. Since these diseases are responsible for large fractions of mortality and morbidity rates and the associated health care costs, the possibilities for improving patient care are highly sought after. Genome-wide association studies, which look for the variants associated with a given disease, therefore has been a major hope against complex diseases. However, as the news article in this week's Science points out, the success rates are below expectations. For example, a diabetes study carried on 2700 patients, have not found any new variants above 1.5% in frequency with strong effects on diabetes risk. These low effect variants not only fail to predict the disease with high confidence but also fall short to explain the mechanism of the diseases as Mendelian variants do.
One strategy to overcome the challenges can be/has been to combine the expression values of multiple genes and use gene-panels or gene-signatures to predict diseases. The developments in Microarray and Next-Gen RNA sequencing technologies can move the field in this direction at least in the diagnosis/prognosis side of the problem.
Genetic Influences on Disease Remain Hidden
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