Browsing by Subject "Codon bias"

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    Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker
    (Pacific Symposium on Biocomputing, 2018) Miller, Jason E.; Shivakumar, Manu K.; Risacher, Shannon L.; Saykin, Andrew J.; Lee, Seunggeun; Nho, Kwangsik; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    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.
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    Multi-animal model study reveals mutations in neural plasticity and nociception genes are linked to excessive alcohol drinking
    (Wiley, 2023) Muir, William M.; Lo, Chiao-Ling; Bell, Richard L.; Zhou, Feng C.; Medicine, School of Medicine
    Background: The basis for familial alcohol use disorder (AUD) remains an enigma due to various biological and societal confounds. The present study used three of the most adopted and documented rat models, combining the alcohol-preferring/non-alcohol-preferring (P/NP) lines and high alcohol-drinking/low alcohol-drinking (HAD/LAD) replicated lines, of AUD as examined through the lens of whole genomic analyses. Methods: We used complete genome sequencing of the P/NP lines and previously published sequences of the HAD/LAD replicates to enhance the discovery of variants associated with AUD and to remove confounding with genetic background and random genetic drift. Specifically, we used high-order statistical methods to search for genetic variants whose frequency changes in whole sets of gene ontologies corresponded with phenotypic changes in the direction of selection, that is, ethanol-drinking preference. Results: Our first finding was that in addition to variants causing translational changes, the principal genetic changes associated with drinking predisposition were silent mutations and mutations in the 3' untranslated regions (3'UTR) of genes. Neither of these types of mutations alters the amino acid sequence of the translated protein but they influence both the rate and conformation of gene transcription, including its stability and posttranslational events that alter gene efficacy. This finding argues for refocusing human genomic studies on changes in gene efficacy. Among the key ontologies identified were the central genes associated with the Na+ voltage-gated channels of neurons and glia (including the Scn1a, Scn2a, Scn2b, Scn3a, Scn7a, and Scn9a subtypes) and excitatory glutamatergic secretion (including Grm2 and Myo6), both of which are essential in neuroplasticity. In addition, we identified "Nociception or Sensory Perception of Pain," which contained variants in nociception (Arrb1, Ccl3, Ephb1) and enlist sodium (Scn1a, Scn2a, Scn2b, Scn3a, Scn7a), pain activation (Scn9a), and potassium channel (Kcna1) genes. Conclusion: The multi-model analyses used herein reduced the confounding effects of random drift and the "founders" genetic background. The most differentiated bidirectionally selected genes across all three animal models were Scn9a, Scn1a, and Kcna, all of which are annotated in the nociception ontology. The complexity of neuroplasticity and nociception adds strength to the hypothesis that neuroplasticity and pain (physical or psychological) are prominent phenotypes genetically linked to the development of AUD.