Molecular Genetics
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/21690
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Browsing Molecular Genetics by Author "Chakraborty, Ranajit"
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Item Genetic Differentiation of Hispanic Populations Using Ancestry Informative Markers(2017-03-14) Cross, Deanna; Chakraborty, Ranajit; Planz, John; Eisenberg, Arthur; Barber, Robert; Setser, CasandraHypothesis: 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.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.