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Opportunities and Challenges in Cardiovascular Pharmacogenomics: From Discovery to Implementation.
Roden DM, Van Driest SL, Wells QS, Mosley JD, Denny JC, Peterson JF
(2018) Circ Res 122: 1176-1190
MeSH Terms: Biological Variation, Individual, Biotransformation, Cardiovascular Agents, Drug Development, Drug-Related Side Effects and Adverse Reactions, Forecasting, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genomics, Genotyping Techniques, Human Genome Project, Humans, Pharmacogenetics, Precision Medicine, Randomized Controlled Trials as Topic, Risk Assessment, Sample Size
Show Abstract · Added March 24, 2020
This review will provide an overview of the principles of pharmacogenomics from basic discovery to implementation, encompassing application of tools of contemporary genome science to the field (including areas of apparent divergence from disease-based genomics), a summary of lessons learned from the extensively studied drugs clopidogrel and warfarin, the current status of implementing pharmacogenetic testing in practice, the role of genomics and related tools in the drug development process, and a summary of future opportunities and challenges.
© 2018 American Heart Association, Inc.
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1 Members
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MeSH Terms
The genetic architecture of type 2 diabetes.
Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, Ma C, Fontanillas P, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, van der Schouw YT, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Burtt NP, Mohlke KL, Meitinger T, Groop L, Abecasis G, Florez JC, Scott LJ, Morris AP, Kang HM, Boehnke M, Altshuler D, McCarthy MI
(2016) Nature 536: 41-47
MeSH Terms: Alleles, DNA Mutational Analysis, Diabetes Mellitus, Type 2, Europe, Exome, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotyping Techniques, Humans, Sample Size
Show Abstract · Added April 13, 2017
The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
0 Communities
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11 MeSH Terms
Continuously moving table MRI with golden angle radial sampling.
Sengupta S, Smith DS, Welch EB
(2015) Magn Reson Med 74: 1690-7
MeSH Terms: Algorithms, Beds, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Patient Positioning, Phantoms, Imaging, Reproducibility of Results, Sample Size, Sensitivity and Specificity, Whole Body Imaging
Show Abstract · Added January 20, 2015
PURPOSE - Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.
THEORY AND METHODS - CMT MRI with GA and linear angle (LA) schemes were developed for whole-body imaging at 3T with a table speed of 20 mm/s. Imaging was performed in phantoms and a human volunteer with extended z fields of view of up to 1.8 meters. Four separate LA and a single GA scan were performed to enable slice reconstructions at four different thicknesses.
RESULTS - GA CMT MRI produced high image quality in phantoms and humans and allowed complete flexibility in reconstruction of slices with arbitrary slice thickness and position from a single data set. LA CMT MRI was constrained by predetermined parameters, required multiple scans and suffered from stair step artifacts that were not present in GA images.
CONCLUSION - GA sampling provides a robust flexible approach to CMT whole-body MRI with the ability to reconstruct slices at arbitrary positions and thicknesses from a single scan.
© 2014 Wiley Periodicals, Inc.
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12 MeSH Terms
Size matters: how population size influences genotype-phenotype association studies in anonymized data.
Heatherly R, Denny JC, Haines JL, Roden DM, Malin BA
(2014) J Biomed Inform 52: 243-50
MeSH Terms: Algorithms, Biomedical Research, Computer Simulation, Confidentiality, Databases, Genetic, Electronic Health Records, Genetic Association Studies, Genotype, Humans, Phenotype, Polymorphism, Single Nucleotide, Sample Size
Show Abstract · Added March 14, 2018
OBJECTIVE - Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.
METHODS - We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.
RESULTS - Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.
CONCLUSIONS - This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.
Copyright © 2014 Elsevier Inc. All rights reserved.
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12 MeSH Terms
Using ancestry-informative markers to identify fine structure across 15 populations of European origin.
Huckins LM, Boraska V, Franklin CS, Floyd JA, Southam L, GCAN, WTCCC3, Sullivan PF, Bulik CM, Collier DA, Tyler-Smith C, Zeggini E, Tachmazidou I, GCAN, WTCCC3
(2014) Eur J Hum Genet 22: 1190-200
MeSH Terms: Anorexia Nervosa, European Continental Ancestry Group, Gene Frequency, Genetic Markers, Genetics, Population, Genome-Wide Association Study, Genotyping Techniques, Humans, Oligonucleotide Array Sequence Analysis, Phylogeography, Polymorphism, Single Nucleotide, Principal Component Analysis, Reproducibility of Results, Sample Size
Show Abstract · Added February 4, 2016
The Wellcome Trust Case Control Consortium 3 anorexia nervosa genome-wide association scan includes 2907 cases from 15 different populations of European origin genotyped on the Illumina 670K chip. We compared methods for identifying population stratification, and suggest list of markers that may help to counter this problem. It is usual to identify population structure in such studies using only common variants with minor allele frequency (MAF) >5%; we find that this may result in highly informative SNPs being discarded, and suggest that instead all SNPs with MAF >1% may be used. We established informative axes of variation identified via principal component analysis and highlight important features of the genetic structure of diverse European-descent populations, some studied for the first time at this scale. Finally, we investigated the substructure within each of these 15 populations and identified SNPs that help capture hidden stratification. This work can provide information regarding the designing and interpretation of association results in the International Consortia.
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1 Members
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14 MeSH Terms
A powerful association test of multiple genetic variants using a random-effects model.
Cheng KF, Lee JY, Zheng W, Li C
(2014) Stat Med 33: 1816-27
MeSH Terms: Breast Neoplasms, China, Computer Simulation, Female, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Models, Genetic, Receptor, Fibroblast Growth Factor, Type 2, Sample Size
Show Abstract · Added March 20, 2014
There is an emerging interest in sequencing-based association studies of multiple rare variants. Most association tests suggested in the literature involve collapsing rare variants with or without weighting. Recently, a variance-component score test [sequence kernel association test (SKAT)] was proposed to address the limitations of collapsing method. Although SKAT was shown to outperform most of the alternative tests, its applications and power might be restricted and influenced by missing genotypes. In this paper, we suggest a new method based on testing whether the fraction of causal variants in a region is zero. The new association test, T REM , is derived from a random-effects model and allows for missing genotypes, and the choice of weighting function is not required when common and rare variants are analyzed simultaneously. We performed simulations to study the type I error rates and power of four competing tests under various conditions on the sample size, genotype missing rate, variant frequency, effect directionality, and the number of non-causal rare variant and/or causal common variant. The simulation results showed that T REM was a valid test and less sensitive to the inclusion of non-causal rare variants and/or low effect common variants or to the presence of missing genotypes. When the effects were more consistent in the same direction, T REM also had better power performance. Finally, an application to the Shanghai Breast Cancer Study showed that rare causal variants at the FGFR2 gene were detected by T REM and SKAT, but T REM produced more consistent results for different sets of rare and common variants.
Copyright © 2013 John Wiley & Sons, Ltd.
0 Communities
1 Members
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12 MeSH Terms
Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data.
Li CI, Su PF, Shyr Y
(2013) BMC Bioinformatics 14: 357
MeSH Terms: Base Sequence, Computer Simulation, Gene Expression Regulation, Likelihood Functions, Models, Statistical, Poisson Distribution, RNA, Random Allocation, Research Design, Sample Size, Sequence Analysis, RNA, User-Computer Interface
Show Abstract · Added March 10, 2014
BACKGROUND - Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size.
RESULTS - We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data.
CONCLUSIONS - The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.
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12 MeSH Terms
No significant improvement in the rate of accurate ventricular catheter location using ultrasound-guided CSF shunt insertion: a prospective, controlled study by the Hydrocephalus Clinical Research Network.
Whitehead WE, Riva-Cambrin J, Wellons JC, Kulkarni AV, Holubkov R, Illner A, Oakes WJ, Luerssen TG, Walker ML, Drake JM, Kestle JR, Hydrocephalus Clinical Research Network
(2013) J Neurosurg Pediatr 12: 565-74
MeSH Terms: Adolescent, Child, Child, Preschool, Clinical Competence, Echoencephalography, Equipment Failure, Female, Foreign-Body Migration, Humans, Hydrocephalus, Infant, Infant, Newborn, Kaplan-Meier Estimate, Male, Prospective Studies, Sample Size, Time Factors, Treatment Outcome, Ultrasonography, Interventional, Ventriculoperitoneal Shunt
Show Abstract · Added March 7, 2014
OBJECT - Cerebrospinal fluid shunt ventricular catheters inserted into the frontal horn or trigone are associated with prolonged shunt survival. Developing surgical techniques for accurate catheter insertion could, therefore, be beneficial to patients. This study was conducted to determine if the rate of accurate catheter location with intraoperative ultrasound guidance could exceed 80%.
METHODS - The authors conducted a prospective, multicenter study of children (< 18 years) requiring first-time treatment for hydrocephalus with a ventriculoperitoneal shunt. Using intraoperative ultrasound, surgeons were required to target the frontal horn or trigone for catheter tip placement. An intraoperative ultrasound image was obtained at the time of catheter insertion. Ventricular catheter location, the primary outcome measure, was determined from the first postoperative image. A control group of patients treated by nonultrasound surgeons (conventional surgeons) were enrolled using the same study criteria. Conventional shunt surgeons also agreed to target the frontal horn or trigone for all catheter insertions. Patients were triaged to participating surgeons based on call schedules at each center. A pediatric neuroradiologist blinded to method of insertion, center, and surgeon determined ventricular catheter tip location.
RESULTS - Eleven surgeons enrolled as ultrasound surgeons and 6 as conventional surgeons. Between February 2009 and February 2010, 121 patients were enrolled at 4 Hydrocephalus Clinical Research Network centers. Experienced ultrasound surgeons (> 15 cases prior to study) operated on 67 patients; conventional surgeons operated on 52 patients. Experienced ultrasound surgeons achieved accurate catheter location in 39 (59%) of 66 patients, 95% CI (46%-71%). Intraoperative ultrasound images were compared with postoperative scans. In 32.7% of cases, the catheter tip moved from an accurate location on the intraoperative ultrasound image to an inaccurate location on the postoperative study. This was the most significant factor affecting accuracy. In comparison, conventional surgeons achieved accurate location in 24 (49.0%) of 49 cases (95% CI [34%-64%]). The shunt survival rate at 1 year was 70.8% in the experienced ultrasound group and 66.9% in the conventional group (p = 0.66). Ultrasound surgeons had more catheters surrounded by CSF (30.8% vs 6.1%, p = 0.0012) and away from the choroid plexus (72.3% vs 58.3%, p = 0.12), and fewer catheters in the brain (3% vs 22.4%, p = 0.0011) and crossing the midline (4.5% vs 34.7%, p < 0.001), but they had a higher proportion of postoperative pseudomeningocele (10.1% vs 3.8%, p = 0.30), wound dehiscence (5.8% vs 0%, p = 0.13), CSF leak (10.1% vs 1.9%, p = 0.14), and shunt infection (11.6% vs 5.8%, p = 0.35).
CONCLUSIONS - Ultrasound-guided shunt insertion as performed in this study was unable to consistently place catheters into the frontal horn or trigone. The technique is safe and achieves outcomes similar to other conventional shunt insertion techniques. Further efforts to improve accurate catheter location should focus on prevention of catheter migration that occurs between intraoperative placement and postoperative imaging. Clinical trial registration no.: NCT01007786 ( ClinicalTrials.gov ).
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20 MeSH Terms
Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution.
Li CI, Su PF, Guo Y, Shyr Y
(2013) Int J Comput Biol Drug Des 6: 358-75
MeSH Terms: Humans, Kidney, Liver, Oligonucleotide Array Sequence Analysis, Poisson Distribution, Sample Size, Sequence Analysis, RNA
Show Abstract · Added March 7, 2014
Sample size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a sample size method widely applicable to differential expression studies utilising RNA-seq technology. In this report, we propose several methods for sample size calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closed-form sample size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our sample size calculation methods are applied to three real RNA-seq data sets.
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7 MeSH Terms
Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.
Skates SJ, Gillette MA, LaBaer J, Carr SA, Anderson L, Liebler DC, Ransohoff D, Rifai N, Kondratovich M, Težak Ž, Mansfield E, Oberg AL, Wright I, Barnes G, Gail M, Mesri M, Kinsinger CR, Rodriguez H, Boja ES
(2013) J Proteome Res 12: 5383-94
MeSH Terms: Algorithms, Biomarkers, Tumor, Blood Proteins, Cohort Studies, Gene Expression Regulation, Neoplastic, Humans, Neoplasm Proteins, Neoplasms, Proteomics, Research Design, Sample Size, Sensitivity and Specificity, Specimen Handling
Show Abstract · Added March 20, 2014
Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.
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13 MeSH Terms