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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.

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.

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.

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.

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.

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.

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.

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 ).

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.

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.