Molecular Genetics

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    Towards a Comprehensive Pharmacogenetic Profile for Predicting Opiate Metabolizer Phenotype
    (2017-03-14) Sajantila, Antti; Chakraborty, Ranajit; Pathak, Gita; Moura-Neto, Rodrigo; Budowle, Bruce; Wendt, Frank
    Purpose: The gene encoding cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is a key pharmacogenetic marker for an enzyme which confers poor, intermediate, extensive, and ultrarapid phase I metabolism of many endogenous toxins and foreign compounds, including marketed opiate-based drugs. The pharmacogenetics of opiate metabolism is particularly important due to the relatively high incidence of addiction and overdose of opiates. Recently, trans-acting opiate metabolism and analgesic response enzymes (UGT2B7, ABCB1 [also called p-glycoprotein and/or multi-drug resistant protein], OPRM1, and COMT) have been incorporated into pharmacogenetic studies to generate more comprehensive metabolic profiles of patients. While meaningful, these studies are limited in that demography is not documented during sample selection, and use of targeted genotyping approaches inherently cannot detect novel variants. With use of massively parallel sequencing, it is possible to identify additional polymorphisms that fine tune, or refine, previous pharmacogenetic findings. Methods: The 1000 Genomes Project data were analyzed in two phases: (1) To describe population genetic variation and summary statistics for these five genes in self-reported healthy individuals in five super- and 26 sub-populations; and (2) To utilize individual polymorphism data to form full-gene haplotypes of the five genes of interest in the same sample set to use full-gene information to refine metabolizer phenotype estimates. Both phases of this work were performed using R Studio®, Excel-based workbooks, Genetic Data Analysis, and TreeView. Results: A summary is provided of population statistics, variant effect predictions, and clustering of super- and sub-populations based on pharmacogenetically relevant polymorphisms in five genes whose protein products are associated with opiate metabolism. Comparisons of current standards versus full-gene metabolizer phenotype predictions indicate that a full-gene approach provides better resolution of metabolizer phenotype. These data also indicate that a substantial portion of extensive metabolizers may be incorrectly classified as such due to novel damaging polymorphisms elsewhere in the gene. Conclusions: The results of these studies serve as substantial baseline population genetic data of individual pharmacogenetically relevant polymorphisms and highlight the advantage of using full-gene sequence information to infer metabolizer phenotypes.
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    Case-Case Genome-Wide Association Study of Age-Related Cancer and Alzheimer’s Disease
    (2017-03-14) Barber, Robert; Phillips, Nicole R.; Olmstead, Keegan
    Background: Research over the past five years has strengthened in support of an inverse epidemiological correlation between Alzheimer’s disease (AD) and cancer--individuals with cancer are less likely to develop AD and those with AD have reduced cancer risk. Since cancer is characterized by uncontrolled cell division, and AD by neuronal death (and limited neuronal regeneration), this inverse relationship may point to dysregulation in some common underlying pathways. Here, we aim to investigate the genetic underpinnings of this unique relationship which have not been fully explored, using a unique case-case genome-wide association study (GWAS) design between an AD and cancer cohort. Hypothesis: We hypothesize that suggestive association signals will be observed when comparing the AD to cancer group, with the most interesting signals being those that are stronger when comparing cases-to-cases than when comparing cases-to-controls. Methods: Genome-wide SNP data for AD, Cancer, and Control groups were created using two publically available datasets: Breast Cancer (BrCa) and Prostate Cancer (PCa) Cohort Consortium and Alzheimer’s Disease Neuroimaging Initiative. Breast and prostate cancer were combined to form the Cancer group, which according to Cancer Research UK, are the most prevalent forms of adult and elderly cancers. All samples were typed with the Illumina Human610-Quad BeadChip. Rigorous data management and quality control measures were taken: group matching, updating map location, permutations test, sex check and filtering of low genotyping individuals and loci as well as loci with HWE issues. Three association analyses were performed: AD–Control, Cancer–Control, and AD–Cancer. Results: After matching for age, gender, and Caucasian ethnicity 492 individuals were included in the AD group (Avg age: 75 years, 37% female), 691 individuals in Cancer group (Avg age: 67.7 years, 37% female), and 1150 individuals in the combined Control group (avg age: 71 years, 37% female). Association analysis of the AD–Cancer study indicated one marker, rs2075650, as significant at p -8. Initial analysis also indicated possible clustering of significant SNPs on chromosomes 8 and 11. Conclusions: Case-case GWAS provides a novel means for identifying novel loci involved in the dichotomous relationship of risk of AD and risk of BrCa/PCa. These signals may point to critical genomic regions involved in age-related pathologies of cancer and AD.
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    Pharmacogenomic Determinants of Concomitant Opioid Use in Chronic Low Back Pain Patients: A Preliminary Review
    (2017-03-14) White, Annesha; Licciardone, John C.; Pfluger, Kassie
    Purpose: The aim of this study is to provide pharmacogenetic information on opioid user profiles to better understand the inter-individual variability in drug response and provide guidance to healthcare providers. One of the major mechanisms of opioid metabolism is through hepatic cytochrome P-450 CYP2D6 enzymatic activity which predominately converts codeine to morphine and then morphine-6-glucuronide leading to therapeutic analgesic effects. The association of the CYP2D6 metabolizer phenotypes with formation of morphine via this pathway is well known. Codeine serves as a Prototype for Opioid Metabolism and Analgesia. The “extensive metabolizer” phenotype represents patients who experience normal analgesia at recommended opioid doses. However, the three other CYP2D6 metabolizer phenotypes present clinical challenges in opioid prescribing. At one end of the spectrum, “ultra-rapid metabolizers” are at high risk of opioid toxicity due to increased conversion of codeine to morphine. Alternatively, “poor metabolizers” lack opioid response because of decreased conversion to morphine. However, such patients may paradoxically experience opioid side effects if the dose is increased in efforts to achieve analgesia. Finally, “intermediate metabolizers” may not achieve adequate analgesia at recommended opioid doses and must be closely monitored to balance potential benefits and risks of therapy. Pharmacokinetic and pharmacodynamic studies of opioids such as tramadol and oxycodone similarly show that these drugs depend on CYP2D6 for conversion to active metabolites responsible for analgesia. Methods: DNA Genotyping using Scanner (Illumina) and precision medicine array. CYP2D6, CYP2C9 and CYP2C19 SNP panels, and the genotypes for all SNPs within these three genes are specifically mined from the microarray data for the purposes of risk characterization and cohort grouping. Results/Conclusions: Patients enrolled in the PRECISION TEXAS Pain Registry provide updated data to the baseline information. Selected baseline characteristics of the 40 registry patients enrolled during the first three months of low-intensity operation are available, including scores for pain intensity (11-point numerical rating scale), back-specific functioning (Roland-Morris Disability Questionnaire), quality of life (Patient-Reported Outcomes Measurement Information System-29 [PROMIS- 29]), pain catastrophizing, and pain self-efficacy.
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    Genetic Differentiation of Hispanic Populations Using Ancestry Informative Markers
    (2017-03-14) Cross, Deanna; Chakraborty, Ranajit; Planz, John; Eisenberg, Arthur; Barber, Robert; Setser, Casandra
    Hypothesis: There are at least 10,500 unidentified human remains in the US as of August 2015, with 2,041 of presumed Hispanic origin (NamUs 2015). Conventional DNA analysis identifies an individual through comparison with reference profiles. For those with no reference, panels of ancestry informative single nucleotide polymorphisms (SNPs) exist (Kidd 2014, Seldin 2009), but they focus on global differentiation and are not useful for ancestry determination of admixed populations (e.g. Hispanics). We hypothesize that a small panel of SNPs ascertained from appropriate populations with great genetic differentiation can distinguish ancestry within Hispanic populations. Materials: This bioinformatics study uses the Genomic Origins and Ancestry in Latinos (GOAL) data set of 250 individuals with ancestry from Columbia, Cuba, Dominican Republic, Haiti, Honduras, or Puerto Rico, genotyped using the Affymetrix 6.0 chip to develop an informative Hispanic SNP panel. Methods: Starting with 897,336 SNPs, we trimmed to 531,878 SNPs using linkage disequilibrium of 0.7. We then calculated pairwise FST for each SNP with each population pair using PLINK software (Haiti excluded). SNPs that met the 0.15 threshold for the four comparisons were included in a 1217 SNP panel. We used STRUCTURE to visualize population separation. To determine if a smaller SNP set could be utilized while retaining information, we used the SNPs with the top ten mean FST values from each population plus five extra to try to distinguish Cuba vs. Dominican Republic for a condensed panel of 56 SNPs. Additionally, we combined 1000 Genomes and GOAL data to verify whether the countries differentiate ancestrally or geographically. Results: STRUCTURE analysis showed Honduras was easily distinguished from other countries in the 1217 and 56 SNP panels. Other countries were also separated based on contribution from ancestral populations; however, the separation was less than ideal. Notably, Honduras contributed 71% of the SNPs in the 1217 panel. When analyzed with 1000 Genomes data, Honduras separated with the Chinese population for K=1-3, but was the first GOAL population to separate from the ancestral line. Conclusions: Utilizing an efficient SNP panel consistently separated Honduras from other populations demonstrating proof of concept. Greater separation of country of origin may be seen with a larger data set and alternative selection of each population’s number of SNPs by a cumulative mean FST threshold.
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    Bacterial microbiome of the Lone Star tick, Amblyomma americanum, from Arkansas, United States
    (2017-03-14) Zhang, Yan; Mitchell, Elizabeth; Allen, Michael; Thapa, Santosh
    Objective: Amblyomma americanum (the Lone Star tick), an aggressive, human-biting tick abundant in the southern, central, and eastern regions of the United States, is an important vector for many bacterial pathogens, including Rickettsia, Ehrlichia, and Francisella spp. Additionally, these ticks harbor many commensals and symbionts. The state of Arkansas has a disproportionately high incidence of several tick-borne, bacterial diseases. In order to better understand the community structure in which both pathogenic and non-pathogenic, tick-borne bacteria exist, we characterized the bacterial microbiome of A. americanum ticks collected from multiple sites in Arkansas. In addition to knowing the underlying bacterial communities within these ticks, the resultant data provide information which can potentially be useful in establishing effective interventions to control tick-borne diseases. Materials and Methods: Genomic DNA was extracted from a total of 87 questing A. americanum ticks (42 females, 21 males, and 24 nymphs) collected in Arkansas during April-June 2015, and the V4 hypervariable region of the 16S rRNA gene was targeted using the Illumina MiSeq® sequencing platform to investigate the tick bacterial microbiomes. Raw sequence data were processed with open access mothur software. Sequences with 97% similarity were grouped into operational taxonomic units (OTUs) and assigned to differenct taxonomic levels by matching to the Greengenes database. Results: The genus Coxiella, which includes a commonly found bacterial endosymbiont, was detected in all ticks tested, with variable distribution among the females (80%), males (0.17%) and nymphs (65%). The genus Rickettsia, which contains several known pathogens, was detected in all nymphal tick pools (0.10% to 0.90%) and about half of the female ticks (0.20% to 2.10%) but was not found in any males. Of interest, more than three-fourths of the male ticks had high abundance of unclassified bacteria within the Enterobacteriaceae family, while few females carried this group of bacteria. Conclusions: These data demonstrate that differences in the bacterial communities are present, when comparing both life stage and sex of A. americanum ticks from Arkansas. The female ticks exhibited significantly less bacterial diversity and contained numerically dominant levels of Coxiella spp. bacteria, when compared to the males.
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    Gut Microbiome of Phenylketonuria Patients
    (2017-03-14) Zhang, Yan; Durrer, Katherine; Allen, Michael; Mancilla, Viviana J.
    Background: Phenylketonuria (PKU) is a metabolic disease caused by a mutation in the phenylalanine hydroxylase (PAH) gene, resulting in the inability to metabolize phenylalanine. Currently, the main treatment for PKU is dietary Phe restriction. Numerous studies on the gut microbiome have demonstrated impacts on overall health, and both diet and genetics have been shown to impact the composition of the gut microbiome. The gut microbiome in adult PKU patients has not yet been systematically investigated, and the ramifications of dietary Phe restriction are unknown. Objective: Characterize the gut microbiome of PKU patients. Materials and Methods: Gut microbial composition of 16 adult PKU patients were compared to 15 healthy adults by sequencing the 16S RNA gene v4 region using the Illumina MiSeq instrument. Results: The dominant genera found in the gut microbiome of PKU and healthy control were Blautia and Bacteroides. When comparing the microbiome composition of healthy individuals and PKU patients, the abundance of Blautia, Corpococcus, Subdoligranulum, and Psuedonomas were increased in PKU patients, while Bacteriodes, Alistipes, SMB53, Faecalibacterium, and members of the Enterobacteriaceae family were shown to decrease in abundance in PKU patients. Conclusions: The compositions of the PKU gut microbiome showed differences compared to that of healthy controls. This study provides valuable background information on the gut microbiome of PKU patients, which could be beneficial to the development of future treatments.