Browsing by Author "Wendt, Frank"
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Item Massively parallel sequencing of 68 insertion/deletion markers identifies novel sequence variation for utility in human identity testing(2016-03-23) Song, Bing; Thompson, Lindsey; King, Jonathan; Budowle, Bruce; LaRue, Bobby; Wendt, FrankShort tandem repeat (STR) loci are traditionally used by the forensic science community for kinship, missing persons, and human identity testing. These markers hold considerable value due to their size, ability to be multiplexed, and highly polymorphic nature. However, they are unable to provide phenotypic and biogeographic ancestry estimates and are too large for use in analysis of DNA from highly compromised substrates such as explosives or human remains. Small bi-allelic polymorphisms, such as insertions/deletions (INDELs), have been of considerable interest within the forensic science community for their utility in filling such gaps. These markers range in size from 2-6 base pairs, making them ideal for highly compromised sample types. Additionally, the ease of multiplexing large INDEL panels allows for comparable discrimination power when compared to STRs. Capillary electrophoresis is a current mainstay in the forensic DNA workflow, generating fluorescent signals to detect alleles separated by size. This method is limited by number of dyes simultaneously utilized, number of loci capable of multiplexing, sample throughput, and required amplicon size. Massively parallel sequencing (MPS) provides a solution to these limitations by targeting many loci across the genomes of multiple samples simultaneously with relatively high sequence coverage. Herein, we describe the utility of MPS, using the Nextera™ Rapid Capture Custom Enrichment Kit (Illumina, Inc., San Diego, CA), to sequence 68 INDELs in four major US population groups on the Illumina MiSeq™. We also define a novel application of the STR Allele Identification Tool: Razor (STRait Razor) to analyze INDEL sequences and capture adjacent sequence variation in the form of single nucleotide polymorphisms (SNPs). This application has enabled the discovery of unique allelic variants, which increase the discrimination power and decrease the single-locus and combined random match probabilities of four well-characterized INDELs. These findings suggest that more valuable INDELs for human identification may exist elsewhere in the genome. As such, it is recommended that these four markers be included in future INDEL multiplex panels for human identification due to their enhanced individualization potential.Item 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, FrankPurpose: 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.