Alzheimer’s disease (AD) is the most common neurodegenerative disorder that affects the nervous system and leads to loss of memory. A vast amount of genetic variants are associated with this disease condition and characterizing their effects (disease-causing/neutral) are more important.
We have constructed a comprehensive dataset of 314 Alzheimer’s disease-causing and 370 neutral mutations. Utilizing the data, we explored the importance of several sequence-based features such as conservation scores, position-specific scoring matrix (PSSM) profile, change in hydrophobicity, amino acid residue substitution matrices and neighboring residue information for identifying the Alzheimer’s disease-causing mutations. Our method showed a sensitivity, specificity and accuracy of 89.3%, 90% and 89.7%, respectively for discrimination of test set. We have developed a tool, Alz-disc, for discriminating the Alzheimer’s disease-causing and neutral mutations using sequence information alone. This study is useful to annotate the new variants and design or develop mutation specific drugs for Alzheimer's disease.
Please cite us if the use of this tool has helped you in your work and /or resulted in any publication:
Kulandaisamy A., Akila S. and Michael Gromiha, M. (2020). Alz-Disc: A tool to discriminate Alzheimer’s disease-causing and neutral mutations
(Submitted).