Miller Jason E, Shivakumar Manu K, Risacher Shannon L, Saykin Andrew J, Lee Seunggeun, Nho Kwangsik, Kim Dokyoon
Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, USA.
Pac Symp Biocomput. 2018;23:365-376.
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.
阿尔茨海默病（AD）是一种神经退行性疾病，尽管它影响了相当一部分人群，并且预计未来会影响更多个体，但目前可用的生物标志物却很少。神经影像学已与遗传信息结合使用，以增进我们对AD的发病机制及潜在诊断方法的理解。此外，有证据表明同义变异可能对基因调控机制产生功能影响，包括与AD相关的调控机制。一些同义密码子比其他密码子更受青睐，从而导致密码子偏好性。这种偏好性可能源于基因组中使用频率或多或少的密码子。它也可能由最优密码子和非最优密码子导致，这两种密码子分别具有更强和更弱的密码子-反密码子相互作用。尽管之前已经利用关联测试来识别与AD相关的基因，但尚不清楚密码子偏好性如何发挥作用，以及它是否能改善罕见变异分析。在这项研究中，使用BioBin将来自阿尔茨海默病神经影像学倡议（ADNI）队列全基因组测序的罕见变异分类到各个基因中。使用SKAT-O对这些基因与AD相关神经影像学生物标志物进行了关联分析。当使用所有同义变异时，我们未发现任何全基因组显著关联，但仅使用影响密码子频率的同义变异时，我们识别出几个与成像表型显著相关的基因。此外，仅使用次要等位基因中包含最优密码子且主要等位基因中包含非最优密码子的罕见变异时，也发现了显著关联。这些结果表明，密码子偏好性可能在AD中发挥作用，并且可用于提高罕见变异关联分析的检测能力。
