Jason E. Miller1, Manu K. Shivakumar1, Shannon L. Risacher2, Andrew J. Saykin2, Seunggeun Lee3, Kwangsik Nho2,*, Dokyoon Kim1,4,*, for the Alzheimer's Disease Neuroimaging Initiative (ADNI)
1Biomedical and Translational Informatics Institute, Geisinger Health System
2Department of Radiology and Imaging Sciences, Indiana University School of Medicine
3Department of Biostatistics, University of Michigan
4Huck Institute of the Life Sciences, Pennsylvania State University
*Corresponding author
Email: jason.eli.miller@gmail.com, mkshivakumar@geisinger.edu, srisache@iupui.edu, asaykin@iupui.edu, leeshawn@umich.edu, knho@iupui.edu, dkim@geisinger.edu
Pacific Symposium on Biocomputing 23:365-376(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
Alzheimer's disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genomewide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.