Comparative assay of LRRK2 gene mutations via bioinformatics approach

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease known to cause irreversible brain damage to afflicted individuals. It has been characterized as a chronic brain deterioration illness with an increased age-related risk factor. Being the second most common neural degenerative affliction after Alzheimer’s its most prominent symptoms are known to be slow movement, tremors, loss of balance and muscle atrophy. This progressive disorder affects the neural cells embedded deep in the brain called basal ganglions and the substantia nigra. The substantia nigra has a key role in the production of the neurotransmitter dopamine of the human body responsible for the reward center and muscle movement. Since there is no known definitive diagnosis for Parkinson’s, different approaches have identified a high number of mutations, especially in the LRRK2 gene (over 40%) in PD cases. This has made this gene a target for analysis. The mutations that affect this gene can create a series of damaging effects in the human body, ranging from abnormal signalling up to onset of Parkinson’s disease and/or cancer. To assess these deleterious effects, we analysed 30-point mutations by predictive algorithms. Since our previous study and this one correlate on most effects and predictions, we can safely assume that the predictors we additionally chose were up to standard, up to date, with high sensitivity, specificity and accuracy.