Abstract
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
Highlights
In 1904 Dr James Herrick reported [1] the findings of ‘‘peculiar elongated and sickle shaped’’ red blood cells discovered by Dr Ernest Irons in a hospital patient afflicted with shortness of breath, heart palpitations, and various other aches and pains
The development of high throughput technologies has augmented our abilities to identify genetic deficiencies and inconsistencies that lead to the development of diseases
Inferences that could potentially be made from combining different studies and existing research results are beyond reach for anyone of human descent
Summary
In 1904 Dr James Herrick reported [1] the findings of ‘‘peculiar elongated and sickle shaped’’ red blood cells discovered by Dr Ernest Irons in a hospital patient afflicted with shortness of breath, heart palpitations, and various other aches and pains. The first disease-associated gene, coding for beta-globin chain of hemoglobin A, was discovered It took another thirty years before in 1983 a study of the DNA of families afflicted with Huntington’s disease has revealed its association with a gene on chromosome 4 called huntigtin (HTT) [4]. Huntington’s became the first genetic disease mapped using polymorphism information (G8 DNA probe/genetic marker), closely followed by the same year discovery of phenylketonuria association with polymorphisms in a hepatic enzyme phenylalanine hydroxylase [5]. These advances provided a route for predicting the likelihood of disease development and even stirred some worries regarding the possibility of the rise of ‘‘medical eugenics’’ [6]. Computational methods - gene prioritization techniques, are necessary to effectively translate the experimental data into legible disease-gene associations [11]
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