Improving the identification of relevant variants in genome information systems: A methodological approach with a case study on early onset Alzheimer's disease

Abstract

Alzheimer's disease is the most common type of dementia in the elderly. Nevertheless, there is an early onset form that is difficult to diagnose precisely. As the genetic component is the most critical factor in developing this disease, identifying relevant genetic variants is key to obtaining a more reliable and straightforward diagnosis. The information about these variants is stored in an extensive number of data sources, which must be carefully analyzed to select only the information with sufficient quality to be used in a clinical setting. This selection has become complex due to the increasing available genomic information. The SILE method was designed to systematize identifying relevant variants for a disease in this challenging context. However, several problems on how SILE identifies relevant variants were discovered when applying the method to the early onset form of Alzheimer's disease. More specifically, the method failed to address specific features of this disease such as its low incidence and familiar component. This paper proposes an improvement of the identification process defined by the SILE method to make it applicable to a further spectrum of diseases. Details of how the proposed solution has been applied are also reported. As a result of this improvement, a set of 29 variants has been identified (25 variants Accepted with a Limited Evidence and 4 Accepted with Moderate Evidence). This constitutes a valuable result that facilitates and reinforces the genetic diagnosis of the disease.