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Search Results: 1 - 10 of 208671 matches for " Brooke L. Fridley "
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Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG
Ryan Abo, Gregory D. Jenkins, Liewei Wang, Brooke L. Fridley
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0043301
Abstract: Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set – expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.
Self-Contained Gene-Set Analysis of Expression Data: An Evaluation of Existing and Novel Methods
Brooke L. Fridley,Gregory D. Jenkins,Joanna M. Biernacka
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0012693
Abstract: Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher's method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.
Genome-wide association study for biomarker identification of Rapamycin and Everolimus using a lymphoblastoid cell line system
Brooke L. Fridley,Abra Brisbin,Gregory Jenkins,Liewei Wang
Frontiers in Genetics , 2013, DOI: 10.3389/fgene.2013.00166
Abstract: The mammalian target of rapamycin (mTOR) inhibitors, a set of promising potential anti-cancer agents, has shown response variability among individuals. This study aimed to identify novel biomarkers and mechanisms that might influence the response to Rapamycin and Everolimus. Genome-wide association (GWA) analyses involving single nucleotide polymorphisms (SNPs), mRNA, and microRNAs microarray data were assessed for association with area under the cytotoxicity dose response curve (AUC) of two mTOR inhibitors in 272 human lymphoblastoid cell lines (LCLs). Integrated analysis among SNPs, expression data, microRNA data and AUC values were also performed to help select candidate genes for further functional characterization. Functional validation of candidate genes using siRNA screening in multiple cell lines followed by MTS assays for the two mTOR inhibitors were performed. We found that 16 expression probe sets (genes) that overlapped between the two drugs were associated with AUC values of two mTOR inhibitors. One hundred and twenty seven and one hundred SNPs had P < 10?4, while 8 and 10 SNPs had P < 10?5 with Rapamycin and Everolimus AUC, respectively. Functional studies indicated that 13 genes significantly altered cell sensitivity to either one or both drugs in at least one cell line. Additionally, one microRNA, miR-10a, was significantly associated with AUC values for both drugs and was shown to repress expression of genes that were associated with AUC and desensitize cells to both drugs. In summary, this study identified genes and a microRNA that might contribute to response to mTOR inhibitors.
Utilizing Genotype Imputation for the Augmentation of Sequence Data
Brooke L. Fridley,Gregory Jenkins,Matthew E. Deyo-Svendsen,Scott Hebbring,Robert Freimuth
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0011018
Abstract: In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.
Gemcitabine and Arabinosylcytosin Pharmacogenomics: Genome-Wide Association and Drug Response Biomarkers
Liang Li,Brooke L. Fridley,Krishna Kalari,Gregory Jenkins,Anthony Batzler,Richard M. Weinshilboum,Liewei Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0007765
Abstract: Cancer patients show large individual variation in their response to chemotherapeutic agents. Gemcitabine (dFdC) and AraC, two cytidine analogues, have shown significant activity against a variety of tumors. We previously used expression data from a lymphoblastoid cell line-based model system to identify genes that might be important for the two drug cytotoxicity. In the present study, we used that same model system to perform a genome-wide association (GWA) study to test the hypothesis that common genetic variation might influence both gene expression and response to the two drugs. Specifically, genome-wide single nucleotide polymorphisms (SNPs) and mRNA expression data were obtained using the Illumina 550K? HumanHap550 SNP Chip and Affymetrix U133 Plus 2.0 GeneChip, respectively, for 174 ethnically-defined “Human Variation Panel” lymphoblastoid cell lines. Gemcitabine and AraC cytotoxicity assays were performed to obtain IC50 values for the cell lines. We then performed GWA studies with SNPs, gene expression and IC50 of these two drugs. This approach identified SNPs that were associated with gemcitabine or AraC IC50 values and with the expression regulation for 29 genes or 30 genes, respectively. One SNP in IQGAP2 (rs3797418) was significantly associated with variation in both the expression of multiple genes and gemcitabine and AraC IC50. A second SNP in TGM3 (rs6082527) was also significantly associated with multiple gene expression and gemcitabine IC50. To confirm the association results, we performed siRNA knock down of selected genes with expression that was associated with rs3797418 and rs6082527 in tumor cell and the knock down altered gemcitabine or AraC sensitivity, confirming our association study results. These results suggest that the application of GWA approaches using cell-based model systems, when combined with complementary functional validation, can provide insights into mechanisms responsible for variation in cytidine analogue response.
Density of Common Complex Ocular Traits in the Aging Eye: Analysis of Secondary Traits in Genome-Wide Association Studies
Albert O. Edwards, Sung J. Lee, Brooke L. Fridley, Nirubol Tosakulwong
PLOS ONE , 2008, DOI: 10.1371/journal.pone.0002510
Abstract: Genetic association studies are identifying genetic risks for common complex ocular traits such as age-related macular degeneration (AMD). The subjects used for discovery of these loci have been largely from clinic-based, case-control studies. Typically, only the primary phenotype (e.g., AMD) being studied is systematically documented and other complex traits (e.g., affecting the eye) are largely ignored. The purpose of this study was to characterize these other or secondary complex ocular traits present in the cases and controls of clinic-based studies being used for genetic study of AMD. The records of 100 consecutive new patients (of any diagnosis) age 60 or older for which all traits affecting the eye had been recorded systematically were reviewed. The average patient had 3.5 distinct diagnoses. A subset of 10 complex traits was selected for further study because they were common and could be reliably diagnosed. The density of these 10 complex ocular traits increased by 0.017 log-traits/year (P = 0.03), ranging from a predicted 2.74 at age 60 to 4.45 at age 90. Trait-trait association was observed only between AMD and primary vitreomacular traction (P = 0.0009). Only 1% of subjects age 60 or older had no common complex traits affecting the eye. Extrapolations suggested that a study of 2000 similar subjects would have sufficient power to detect genetic association with an odds ratio of 2.0 or less for 4 of these 10 traits. In conclusion, the high prevalence of complex traits affecting the aging eye and the inherent biases in referral patterns leads to the potential for confounding by undocumented secondary traits within case-control studies. In addition to the primary trait, other common ocular phenotypes should be systematically documented in genetic association studies so that adjustments for potential trait-trait associations and other bias can be made and genetic risk variants identified in secondary analyses.
Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?
Patrick Breheny, Prabhakar Chalise, Anthony Batzler, Liewei Wang, Brooke L. Fridley
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0034262
Abstract: Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.
A Latent Model for Prioritization of SNPs for Functional Studies
Brooke L. Fridley,Ed Iversen,Ya-Yu Tsai,Gregory D. Jenkins,Ellen L. Goode,Thomas A. Sellers
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0020764
Abstract: One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial/time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model (BLVM) for incorporating “features” about a SNP to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study (GWAS). In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases/traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2nd and 7th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value (which ranked 197th for invasive case analysis), was ranked 8th based on the posterior probability of being in the top 5 markers (0.13). In summary, the application of the BLVM allows for the systematic integration of multiple SNP “features” for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking.
Evaluation of Clustering and Genotype Distribution for Replication in Genome Wide Association Studies: The Age-Related Eye Disease Study
Albert O. Edwards, Brooke L. Fridley, Katherine M. James, Anil S. Sharma, Julie M. Cunningham, Nirubol Tosakulwong
PLOS ONE , 2008, DOI: 10.1371/journal.pone.0003813
Abstract: Genome-wide association studies (GWASs) assess correlation between traits and DNA sequence variation using large numbers of genetic variants such as single nucleotide polymorphisms (SNPs) distributed across the genome. A GWAS produces many trait-SNP associations with low p-values, but few are replicated in subsequent studies. We sought to determine if characteristics of the genomic loci associated with a trait could be used to identify initial associations with a higher chance of replication in a second cohort. Data from the age-related eye disease study (AREDS) of 100,000 SNPs on 395 subjects with and 198 without age-related macular degeneration (AMD) were employed. Loci highly associated with AMD were characterized based on the distribution of genotypes, level of significance, and clustering of adjacent SNPs also associated with AMD suggesting linkage disequilibrium or multiple effects. Forty nine loci were highly associated with AMD, including 3 loci (CFH, C2/BF, LOC387715/HTRA1) already known to contain important genetic risks for AMD. One additional locus (C3) reported during the course of this study was identified and replicated in an additional study group. Tag-SNPs and haplotypes for each locus were evaluated for association with AMD in additional cohorts to account for population differences between discovery and replication subjects, but no additional clearly significant associations were identified. Relying on a significant genotype tests using a log-additive model would have excluded 57% of the non-replicated and none of the replicated loci, while use of other SNP features and clustering might have missed true associations.
No association between a candidate TCF7L2 variant and risk of breast or ovarian cancer
Ellen L Goode, Csilla Szabo, Ludmila Prokunina-Olsson, Robert A Vierkant, Zachary S Fredericksen, Francis S Collins, Kristin L White, Michele Schmidt, Brooke L Fridley, Fergus J Couch
BMC Cancer , 2009, DOI: 10.1186/1471-2407-9-312
Abstract: Two clinic-based case-control studies at the Mayo Clinic were assessed including 798 breast cancer cases, 843 breast cancer controls, 391 ovarian cancer cases, and 458 ovarian cancer controls. Genotyping at TCF7L2 rs12255372 used a 5' endonuclease assay, and statistical analysis used logistic regression among participants as a whole and among a priori-defined subsets.No associations with risk of breast or ovarian cancer were observed (ordinal model, p = 0.62 and p = 0.75, respectively). In addition, no associations were observed among sub-groups defined by age, BMI, family history, stage, grade, histology, or tumor behavior.Although the biology of the Wnt/β-catenin signaling pathway and prior association between rs12255372 and numerous phenotypes warranted examination of this TCF7L2 SNP, no compelling evidence for association with breast or ovarian cancer was observed.Transcription factor 7-like 2 (TCF7L2) encodes a transcription factor involved in Wnt/β-catenin signaling pathway which encompasses an intronic single-nucleotide polymorphism (SNP, rs12255372) that has been associated with risk of Type 2 diabetes in linkage studies and genome-wide association studies [1-4] with potential modification by obesity [5]. TCF7L2 forms an active nuclear complex with β-catenin that binds and induces the expression of target genes involved in cellular proliferation, evasion of apoptosis, and tissue invasion and metastasis. Because of the protein's relevance in this canonical cancer pathway, several cancer association studies have been conducted. Studies report associations with increased risk of familial breast cancer [6] and aggressiveness of prostate cancer [7]; there are conflicting reports in colon cancer showing decreased risk [8], increased risk [9], or differential risk by NSAIDs use [10]. We sought to assess the role of this polymorphism in risk of breast and ovarian cancer and, based on other reports, in subsets defined by body mass index (BMI), family history, and mea
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