Abstract 2597: Centralized and Study-specific Phenotyping in a Large-scale Stroke Genetics Study: An Analysis from the NINDS Stroke Genetics Network (SiGN)

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Bradford B Worrall ◽  
Alejandro Rabinstein ◽  
Dale M Gamble ◽  
Kevin M Barrett ◽  
Shaneela Malik ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Large Scale ◽  
Association Studies ◽  
Web Based ◽  
Stroke Treatment ◽  
Automated Algorithm ◽  
Genome Association ◽  
Whole Genome Association ◽  
Toast Classification ◽  
User Friendly

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

Exploring Genetic Basis of GVHD by Whole-Genome Association Studies in a Large Series from the Japan Marrow Donation Program (JMDP).

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3232-3232
Author(s):  
Yasuhito Nannya ◽  
Makoto Onizuka ◽  
Koichi Kashiwase ◽  
Masashi Sanada ◽  
Yoshiki Akatsuka ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Whole Genome ◽  
Minor Histocompatibility Antigens ◽  
Therapeutic Benefits ◽  
Gvhd Prophylaxis ◽  
Genome Association ◽  
Whole Genome Association

Abstract Allogeneic stem cell transplantation (allo-HSCT) has been established as one of the most effective therapeutic options for hematopoietic malignancies. Its major therapeutic benefits are obtained from allo-immunity directed against patients’ tumor cells (graft-versus-tumor effects, GVT). However, the same kind of allo-immune reactions can also be directed to normal host tissues resulting in graft-versus-host disease (GVHD) and subsequently offset the potential benefit from GVT. Because both reactions are initiated by donor T-cells that recognize minor histocompatibility antigens (mHA) that are incompatible between donor and recipient, the knowledge of these mHA is of particular importance to develop a new measure to prevent GVHD while harnessing the antitumor potential of allo-HSCT. On the other hand, the incompatibility of mHAs, as well as other predispositions to development of GVHD, is basically determined genetically, and thus, are plausible targets of genetic association studies, which have recently been enabled with the advent of large-scale genotyping technology. In the current study, we explored genetic basis of GVHD by whole genome association studies, in which relevant SNPs to GVHD development were explored by genotyping a large cohort of GVHD-positive and negative transplantations performed through the Japan Marrow Donor Program (JMDP). Among 7,800 transplants genotyped for HLA types, we selected 1856 transplants (3712 donors/recipients) for large scale genotyping of more than 500,000 SNPs using Affymetrix® GeneChip® 500K arrays, according to the following criteria: HLA A, B, C, DR, DQ loci were completely matched at the DNA level Short term MTX (stMTX) + CyA or stMTX + FK506 was employed as GVHD prophylaxis, and Genome DNA from both donor and recipient and record of clinical course was available. Based on the genotyping data, possible association was tested for each SNP in donors and recipients with regard to the development of GVHD. Association of genotype incompatibilities was also investigated for major HLA subtypes to identify the mHA loci that are relevant to GVHD pathogenesis. In the intermediate analysis, a number of candidate polymorphisms in donors and recipients have been identified together with possible mHA loci.

scholarly journals Prospects and pitfalls in whole genome association studies

2005 ◽  
Vol 360 (1460) ◽  
pp. 1589-1595 ◽  
Author(s):  
Robert W Lawrence ◽  
David M Evans ◽  
Lon R Cardon
Keyword(s):  
Genetic Variation ◽  
Genetic Markers ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Whole Genome ◽  
Common Genetic Variation ◽  
Close Attention ◽  
Genome Association ◽  
Whole Genome Association

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype–phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.

scholarly journals Sampling strategies for whole genome association studies in aquaculture and outcrossing plant species

2009 ◽  
Vol 91 (6) ◽  
pp. 367-371 ◽  
Author(s):  
B. J. HAYES ◽  
I. M. MACLEOD ◽  
M. BARANSKI
Keyword(s):  
Plant Species ◽  
Association Studies ◽  
Whole Genome ◽  
Minimal Effect ◽  
Sampling Strategies ◽  
False Discovery ◽  
Breeding Populations ◽  
Genome Association ◽  
Whole Genome Association ◽  
Sib Families

SummaryA number of farmed species are characterized by breeding populations of large full-sib families, including aquaculture species and outcrossing plant species. Whole genome association studies in such species must account for stratification arising from the full-sib family structure to avoid high rates of false discovery. Here, we demonstrate the value of selective genotyping strategies which balance the contribution of families across high and low phenotypes to greatly reduce rates of false discovery with a minimal effect on power.

scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

scholarly journals A web-based, branching logic questionnaire for the automated classification of migraine

2018 ◽  
Author(s):  
Eric A. Kaiser ◽  
Aleksandra Igdalova ◽  
Geoffrey K. Aguirre ◽  
Brett Cucchiara
Keyword(s):  
Migraine Without Aura ◽  
Large Scale ◽  
Automated Analysis ◽  
Automated Classification ◽  
Online Questionnaire ◽  
Demographic Differences ◽  
Web Based ◽  
Online Evaluation ◽  
Automated Algorithm

AbstractObjectiveTo identify migraineurs and headache-free individuals with an online questionnaire and automated analysis algorithm.MethodsWe created a branching-logic, web-based questionnaire—the Penn Online Evaluation of Migraine (POEM)—to obtain standardized headache history from a previously studied cohort. Responses were analyzed with an automated algorithm to assign subjects to one of several categories based on ICHD-3 (beta) criteria. Following a pre-registered protocol, this result was compared to prior diagnostic classification by a neurologist following a direct interview.ResultsOf 118 subjects contacted, 90 (76%) completed the questionnaire; of these 31 were headache-free, 29 migraine without aura (MwoA), and 30 migraine with aura (MwA). Mean age was 41 ± 6 years and 76% were female. There were no significant demographic differences between groups. The median time to complete the questionnaire was 2.5 minutes. Sensitivity of the POEM tool was 42%, 59%, and 70%, and specificity was 100%, 84%, and 94% for headache-free, MwoA, and MwA, respectively. Sensitivity and specificity of the POEM tool for migraine overall (with or without aura), was 83% and 90%, respectively.ConclusionsThe POEM web-based questionnaire, and associated analysis routines, identifies headache-free and migraine subjects with good specificity. It may be useful for classifying subjects for large-scale research studies.Trial Registration:https://osf.io/sq9ef

scholarly journals Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

PLoS Genetics ◽  
2008 ◽  
Vol 4 (6) ◽  
pp. e1000109 ◽  
Author(s):  
Ke Hao ◽  
Eric E. Schadt ◽  
John D. Storey
Keyword(s):  
Association Studies ◽  
Whole Genome ◽  
Snp Arrays ◽  
Genome Association ◽  
Whole Genome Association

scholarly journals DNA Content in Ischemic Stroke Thrombi Can Help Identify Cardioembolic Strokes Among Strokes of Undetermined Cause

Stroke ◽  
2020 ◽  
Vol 51 (9) ◽  
pp. 2810-2816
Author(s):  
Lucas Di Meglio ◽  
Jean-Philippe Desilles ◽  
Mialitiana Solonomenjanahary ◽  
Julien Labreuche ◽  
Véronique Ollivier ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Dna Content ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Embolic Stroke ◽  
Unique Identifier ◽  
Stroke Treatment ◽  
Glycoprotein Vi ◽  
Biochemical Assays ◽  
Toast Classification

Background and Purpose: Identification of acute ischemic stroke (AIS) cause is crucial for guidance of secondary prevention. Previous studies have yielded inconsistent results regarding possible correlations between AIS cause and thrombus composition, as assessed by semiquantitative histological analysis. Here, we performed a correlation analysis between AIS cause and AIS thrombus cellular composition and content, as assessed using quantitative biochemical assays. Methods: Homogenates of 250 patients with AIS thrombi were prepared by mechanical grinding. Platelet, red blood cell, and leukocyte content of AIS thrombi were estimated by quantification of GP (glycoprotein) VI, heme, and DNA in thrombus homogenates. AIS cause was defined as cardioembolic, noncardioembolic, or embolic stroke of undetermined source, according to the TOAST classification (Trial of ORG 10172 in Acute Stroke Treatment). Results: Cardioembolic thrombi were richer in DNA (35.8 versus 13.8 ng/mg, P <0.001) and poorer in GPVI (0.104 versus 0.117 ng/mg, P =0.045) than noncardioembolic ones. The area under the receiver operating characteristic curve of DNA content to discriminate cardioembolic thrombi from noncardioembolic was 0.72 (95% CI, 0.63–0.81). With a threshold of 44.7 ng DNA/mg thrombus, 47% of thrombi from undetermined cause would be classified as cardioembolic with a specificity of 90%. Conclusions: Thrombus DNA content may provide an accurate biomarker for identification of cardioembolic thrombi in patients with AIS with embolic stroke of undetermined source. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT03268668.

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