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ItemCYP3A5 genotype and its impact on vincristine pharmacokinetics and development of neuropathy in Kenyan children with cancer(Wiley, 2018-03) Skiles, Jodi L.; Chiang, ChienWei; Li, Claire H.; Martin, Steve; Smith, Ellen L.; Olbara, Gilbert; Jones, David R.; Vik, Terry A.; Mostert, Saskia; Abbink, Floor; Kaspers, Gertjan J.; Li, Lang; Njuguna, Festus; Sajdyk, Tammy J.; Renbarger, Jamie L.; Pediatrics, School of MedicineBACKGROUND: Vincristine (VCR) is a critical part of treatment in pediatric malignancies and is associated with dose-dependent peripheral neuropathy (vincristine-induced peripheral neuropathy [VIPN]). Our previous findings show VCR metabolism is regulated by the CYP3A5 gene. Individuals who are low CYP3A5 expressers metabolize VCR slower and experience more severe VIPN as compared to high expressers. Preliminary observations suggest that Caucasians experience more severe VIPN as compared to nonCaucasians. PROCEDURE: Kenyan children with cancer who were undergoing treatment including VCR were recruited for a prospective cohort study. Patients received IV VCR 2 mg/m2 /dose with a maximum dose of 2.5 mg as part of standard treatment protocols. VCR pharmacokinetics (PK) sampling was collected via dried blood spot cards and genotyping was conducted for common functional variants in CYP3A5, multi-drug resistance 1 (MDR1), and microtubule-associated protein tau (MAPT). VIPN was assessed using five neuropathy tools. RESULTS: The majority of subjects (91%) were CYP3A5 high-expresser genotype. CYP3A5 low-expresser genotype subjects had a significantly higher dose and body surface area normalized area under the curve than CYP3A5 high-expresser genotype subjects (0.28 ± 0.15 hr·m2 /l vs. 0.15 ± 0.011 hr·m2 /l, P = 0.027). Regardless of which assessment tool was utilized, minimal neuropathy was detected in this cohort. There was no difference in the presence or severity of neuropathy assessed between CYP3A5 high- and low-expresser genotype groups. CONCLUSION: Genetic factors are associated with VCR PK. Due to the minimal neuropathy observed in this cohort, there was no demonstrable association between genetic factors or VCR PK with development of VIPN. Further studies are needed to determine the role of genetic factors in optimizing dosing of VCR for maximal benefit. ItemDevelopment of Hepatitis C Virus Genotyping by Real-Time PCR Based on the NS5B Region(Public Library of Science, 2010-04-13) Nakatani, Sueli M.; Santos, Carlos A.; Riediger, Irina N.; Krieger, Marco A.; Duarte, Cesar A. B.; Lacerda, Marco A.; Biondo, Alexander W.; Carilho, Flair J.; Ono-Nita, Suzane K.; Medicine, School of MedicineHepatitis C virus (HCV) genotyping is the most significant predictor of the response to antiviral therapy. The aim of this study was to develop and evaluate a novel real-time PCR method for HCV genotyping based on the NS5B region. Methodology/Principal Findings Two triplex reaction sets were designed, one to detect genotypes 1a, 1b and 3a; and another to detect genotypes 2a, 2b, and 2c. This approach had an overall sensitivity of 97.0%, detecting 295 of the 304 tested samples. All samples genotyped by real-time PCR had the same type that was assigned using LiPA version 1 (Line in Probe Assay). Although LiPA v. 1 was not able to subtype 68 of the 295 samples (23.0%) and rendered different subtype results from those assigned by real-time PCR for 12/295 samples (4.0%), NS5B sequencing and real-time PCR results agreed in all 146 tested cases. Analytical sensitivity of the real-time PCR assay was determined by end-point dilution of the 5000 IU/ml member of the OptiQuant HCV RNA panel. The lower limit of detection was estimated to be 125 IU/ml for genotype 3a, 250 IU/ml for genotypes 1b and 2b, and 500 IU/ml for genotype 1a. Conclusions/Significance The total time required for performing this assay was two hours, compared to four hours required for LiPA v. 1 after PCR-amplification. Furthermore, the estimated reaction cost was nine times lower than that of available commercial methods in Brazil. Thus, we have developed an efficient, feasible, and affordable method for HCV genotype identification. ItemExpand your research: Next-Gen Sequencing, Genotyping, Gene Expression, and Epigenetics at the Center for Medical Genomics at IUSM(Office of the Vice Chancellor for Research, 2015-04-17) Xuei, Xiaoling; McClintick, Jeanette; Liu, Yunlong; Edenberg, Howard J.The Center for Medical Genomics (CMG) provides Indiana researchers with next-generation sequencing, SNP genotyping, gene expression and epigenetics. We provide expertise in experimental design, carry out the procedures, and assist with analyses and interpretation. These state-of-the-art technologies have enabled a large number of grants to be funded over the years, and have resulted in a very large number of publications. Our next-generation sequencing technology includes SOLiD5500xl, Ion Proton and Ion Torrent PGM (Personal Genome Machine). This set of instruments covers a wide range of nextgeneration sequencing capabilities from small bacterial genomes to the whole human genome, transcriptome (total RNA), small RNA, targeted DNA fragments, exome, ChIP-seq, and methylseq, with high sequencing accuracy. We have generated sequencing data for 74 projects over the past two-three years. Our SNP genotyping facility, using the Sequenom MassArray platform, specializes in targeted genotyping of 20-30 SNPs per assay and is an excellent choice for candidate gene studies and for following up results from GWAS and next-generation sequencing. It has been a central part of several large, multi-site collaborative genetic studies, including Genetics of Alcoholism (COGA), bipolar disorder, osteoporosis and hypertension, as well as many smaller projects; it is most efficient for sets of approximately 370 samples. We have produced more than 20 million targeted SNP genotypes to date. This platform is also capable of measuring allele-specific gene expression, and targeted quantitative DNA methylation for epigenetics study. For many projects, microarrays offer a good alternative to next-generation sequencing for measuring gene expression. We use Affymetrix GeneChip microarrays, capable of measuring expression of nearly all genes in humans (and all exons within them), rats, mice and most model organisms, and can measure expression of miRNAs. We can also use RNA extracted from FFPE samples. We have carried out more than 6,700 GeneChip hybridizations to date in support of many different projects. The CMG partners with the Center for Computational Biology and Bioinformatics for data analysis. We are recognized as a Core Facility of the Indiana CTSI and available to faculty not only from IU and IUPUI, but also from Purdue and Notre Dame Universities. ItemA New Statistic to Evaluate Imputation Reliability(Public Library of Science, 2010-03-15) Lin, Peng; Hartz, Sarah M.; Zhang, Zhehao; Saccone, Scott F.; Wang, Jia; Tischfield, Jay A.; Edenberg, Howard J.; Kramer, John R.; Goate, Alison M.; Bierut, Laura J.; Rice, John P.; COGA Collaborators COGEND Collaborators, GENEVA; Biochemistry and Molecular Biology, School of MedicineBackground As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems. Methodology/Principal Findings We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into “cases” and “controls”, we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (λ = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms. Conclusions/Significance IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.