Evaluation of next generation sequencing platforms for population targeted sequencing studies

Human sequence generated from three next-generation sequencing platforms reveals systematic variability in sequence coverage due to local sequence characteristics

Population sequencing of two endocannabinoid metabolic genes identifies rare and common regulatory variants associated with extreme obesity and metabolite level

Abstract Background Targeted re-sequencing of candidate genes in individuals at the extremes of a quantitative phenotype distribution is a method of choice to gain information on the contribution of rare variants to disease susceptibility. The endocannabinoid system mediates signaling in the brain and peripheral tissues involved in the regulation of energy balance, is highly active in obese patients, and represents a strong candidate pathway to examine for genetic association with body mass index (BMI). Results We sequenced two intervals (covering 188 kb) encoding the endocannabinoid metabolic enzymes fatty-acid amide hydrolase (FAAH) and monoglyceride lipase (MGLL) in 147 normal controls and 142 extremely obese cases. After applying quality filters, we called 1,393 high quality single nucleotide variants, 55% of which are rare, and 143 indels. Using single marker tests and collapsed marker tests, we identified four intervals associated with BMI: the FAAH promoter, the MGLL promoter, MGLL intron 2, and MGLL intron 3. Two of these intervals are composed of rare variants and the majority of the associated variants are located in promoter sequences or in predicted transcriptional enhancers, suggesting a regulatory role. The set of rare variants in the FAAH promoter associated with BMI is also associated with increased level of FAAH substrate anandamide, further implicating a functional role in obesity. Conclusions Our study, which is one of the first reports of a sequence-based association study using next-generation sequencing of candidate genes, provides insights into study design and analysis approaches and demonstrates the importance of examining regulatory elements rather than exclusively focusing on exon sequences

Genome-Wide Association of Implantable Cardioverter-Defibrillator Activation With Life-Threatening Arrhythmias

OBJECTIVES: To identify genetic factors that would be predictive of individuals who require an implantable cardioverter-defibrillator (ICD), we conducted a genome-wide association study among individuals with an ICD who experienced a life-threatening arrhythmia (LTA; cases) vs. those who did not over at least a 3-year period (controls). BACKGROUND: Most individuals that receive implantable cardioverter-defibrillators never experience a life-threatening arrhythmia. Genetic factors may help identify who is most at risk. METHODS: Patients with an ICD and extended follow-up were recruited from 34 clinical sites with the goal of oversampling those who had experienced LTA, with a cumulative 607 cases and 297 controls included in the analysis. A total of 1,006 Caucasian patients were enrolled during a time period of 13 months. Arrhythmia status of 904 patients could be confirmed and their genomic data were included in the analysis. In this cohort, there were 704 males, 200 females, and the average age was 73.3 years. We genotyped DNA samples using the Illumina Human660 W Genotyping BeadChip and tested for association between genotype at common variants and the phenotype of having an LTA. RESULTS AND CONCLUSIONS: We did not find any associations reaching genome-wide significance, with the strongest association at chromosome 13, rs11856574 at P = 5×10⁻⁶. Loci previously implicated in phenotypes such as QT interval (measure of the time between the start of the Q wave and the end of the T wave as measured by electrocardiogram) were not found to be significantly associated with having an LTA. Although powered to detect such associations, we did not find common genetic variants of large effect associated with having a LTA in those of European descent. This indicates that common gene variants cannot be used at this time to guide ICD risk-stratification. TRIAL REGISTRATION: ClinicalTrials.gov NCT00664807

SERIES:eHealth in primary care. Part 2: Exploring the ethical implications of its application in primary care practice

Background: eHealth promises to increase self-management and personalised medicine and improve cost-effectiveness in primary care. Paired with these promises are ethical implications, as eHealth will affect patients' and primary care professionals' (PCPs) experiences, values, norms, and relationships.Objectives: We argue what ethical implications related to the impact of eHealth on four vital aspects of primary care could (and should) be anticipated.Discussion: (1) EHealth influences dealing with predictive and diagnostic uncertainty. Machine-learning based clinical decision support systems offer (seemingly) objective, quantified, and personalised outcomes. However, they also introduce new loci of uncertainty and subjectivity. The decision-making process becomes opaque, and algorithms can be invalid, biased, or even discriminatory. This has implications for professional responsibilities and judgments, justice, autonomy, and trust. (2) EHealth affects the roles and responsibilities of patients because it can stimulate self-management and autonomy. However, autonomy can also be compromised, e.g. in cases of persuasive technologies and eHealth can increase existing health disparities. (3) The delegation of tasks to a network of technologies and stakeholders requires attention for responsibility gaps and new responsibilities. (4) The triangulate relationship: patient-eHealth-PCP requires a reconsideration of the role of human interaction and 'humanness' in primary care as well as of shaping Shared Decision Making.Conclusion: Our analysis is an essential first step towards setting up a dedicated ethics research agenda that should be examined in parallel to the development and implementation of eHealth. The ultimate goal is to inspire the development of practice-specific ethical recommendations

Influence of Genetic Polymorphisms on the Effect of High- and Standard-Dose Clopidogrel After Percutaneous Coronary Intervention The GIFT (Genotype Information and Functional Testing) Study

ObjectivesThis study sought to evaluate the influence of single nucleotide polymorphisms (SNPs) on the pharmacodynamic effect of high- or standard-dose clopidogrel after percutaneous coronary intervention (PCI).BackgroundThere is a lack of prospective, multicenter data regarding the effect of different genetic variants on clopidogrel pharmacodynamics over time in patients undergoing PCI.MethodsThe GRAVITAS (Gauging Responsiveness with A VerifyNow assay–Impact on Thrombosis And Safety) trial screened patients with platelet function testing after PCI and randomly assigned those with high on-treatment reactivity (OTR) to either high- or standard-dose clopidogrel; a cohort of patients without high OTR were also followed. DNA samples obtained from 1,028 patients were genotyped for 41 SNPs in 17 genes related to platelet reactivity. After adjusting for clinical characteristics, the associations between the SNPs and OTR using linear regression were evaluated.ResultsCYP2C19*2 was significantly associated with OTR at 12 to 24 h (R2 = 0.07, p = 2.2 × 10−15), 30 days (R2 = 0.10, p = 1.3 × 10−7), and 6 months after PCI (R2 = 0.07, p = 1.9 × 10−11), whereas PON1, ABCB1 3435 C→T, and other candidate SNPs were not. Carriers of 1 and 2 reduced-function CYP2C19 alleles were significantly more likely to display persistently high OTR at 30 days and 6 months, irrespective of treatment assignment. The portion of the risk of persistently high OTR at 30 days attributable to reduced-function CYP2C19 allele carriage was 5.2% in the patients randomly assigned to high-dose clopidogrel.ConclusionsCYP2C19, but not PON1 or ABCB1, is a significant determinant of the pharmacodynamic effects of clopidogrel, both early and late after PCI. In patients with high OTR identified by platelet function testing, the CYP2C19 genotype provides limited incremental information regarding the risk of persistently high reactivity with clopidogrel 150-mg maintenance dosing. (Genotype Information and Functional Testing Study [GIFT]; NCT00992420

Longitudinal Genome-Wide Association of Cardiovascular Disease Risk Factors in the Bogalusa Heart Study

Peer reviewe

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition