Exploring and genotyping APOE allele variants in RNA sequencing data for the study of amyotrophic lateral sclerosis

Abstract

Amyotrophic lateral sclerosis (ALS) is the third most occurring neurodegenerative disease in humans and is defined by its heterogeneous clinical presentation. Over the years, many genes related to ALS were discovered by the rapid advancement of sequencing technology. Most of these studies were mainly focused on neuronal involvement. Only recently, non-neuronal cells joined the spotlight of the ALS field. In Alzheimer’s disease (AD), the involvement of these non-neuronal cells had been well defined, with the genotype of the APOE gene being the biggest risk factor for AD onset. We propose that APOE genotype could perhaps also have an effect on ALS. However, APOE involvement in ALS is not well defined and thus most public datasets do not readily offer genotype information on this gene. Here we present our method of utilizing a well-established variant calling pipeline on publicly available RNA sequencing data to genotype APOE allele status. We observed that if proper quality control steps on publicly available data were taken, we could robustly genotype APOE providing that read depth around the region of interest was high enough. After validation with a pre-genotyped dataset, we found that our method managed to reach an accuracy of 0.9667 in determining the correct genotype. Our results show that even if there are no microarray or whole genome sequencing data available for genotyping, we can use RNA sequencing data alone to accurately genotype the polymorphic variants of our gene of interest. We could possibly open up many doors for researchers in the field who are limited to RNA sequencing and need genotype information. Adding to it, it could also contribute to researchers in the exploration of already available datasets and reuse data that otherwise would be less informative. With this, we ultimately hope to contribute to the acceleration of scientific discovery and indirectly help patients towards a better clinical outcome.