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A comparison of two collapsing methods in different approaches.

ABSTRACT: Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal. Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the functional principal component analysis [FPCA]-based statistic) using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful.

SUBMITTER: Dering C

PROVIDER: S-EPMC4143760 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A comparison of two collapsing methods in different approaches.

Dering Carmen C Schillert Arne A König Inke R IR Ziegler Andreas A

BMC proceedings 20140617 Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo

Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal. Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the func ...[more]

PMID: 25519408

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Project description:BackgroundSystematic reviews are used widely to guide health care decisions. Several tools have been created to assess systematic review quality. The measurement tool for assessing the methodological quality of systematic reviews known as the AMSTAR tool applies a yes/no score to eleven relevant domains of review methodology. This tool has been reworked so that each domain is scored based on a four point scale, producing R-AMSTAR.Methods and findingsWe aimed to compare the AMSTAR and R-AMSTAR tools in assessing systematic reviews in the field of assisted reproduction for subfertility. All published systematic reviews on assisted reproductive technology, with the latest search for studies taking place from 2007-2011, were considered. Reviews that contained no included studies or considered diagnostic outcomes were excluded. Thirty each of Cochrane and non-Cochrane reviews were randomly selected from a search of relevant databases. Both tools were then applied to all sixty reviews. The results were converted to percentage scores and all reviews graded and ranked based on this. AMSTAR produced a much wider variation in percentage scores and achieved higher inter-rater reliability than R-AMSTAR according to kappa statistics. The average rating for Cochrane reviews was consistent between the two tools (88.3% for R-AMSTAR versus 83.6% for AMSTAR) but inconsistent for non-Cochrane reviews (63.9% R-AMSTAR vs. 38.5% AMSTAR). In comparing the rankings generated between the two tools Cochrane reviews changed an average of 4.2 places, compared to 2.9 for non-Cochrane.ConclusionR-AMSTAR provided greater guidance in the assessment of domains and produced quantitative results. However, there were many problems with the construction of its criteria and AMSTAR was much easier to apply consistently. We recommend that AMSTAR incorporates the findings of this study and produces additional guidance for its application in order to improve its reliability and usefulness.

Project description:ObjectivesTo explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.MethodsA total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.ResultsFor radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.ConclusionRadiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.Critical relevance statementFor quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.Key pointsThere is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.

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Project description:OBJECTIVES:This study compared the impact of two different 8-week mindfulness based courses (.b Foundations and Mindfulness-Based Stress Reduction (MBSR)), delivered to school teachers, on quantitative (stress, anxiety and depression) and qualitative (experience, acceptability and implementation) outcomes. DESIGN:A mixed-methods design was employed. Matched-paired t-tests were used to examine change from baseline, with imputation conducted to account for those lost to follow-up. Qualitative methods involved 1:1 semistructured interviews (n=10). Thematic analysis was used to explore differences in experience between courses. SETTING:Courses took place in UK primary schools or nearby leisure centres, 1:1 interviews took place via telephone. PARTICIPANTS:44/69 teachers from schools in the UK were recruited from their attendance at mindfulness courses (.b and MBSR). INTERVENTIONS:Participants attended either an MBSR (experiential style learning, 2 hours per week) or .b Foundations (more classroom focused learning, 1.5 hours per week) 8-week mindfulness course. OUTCOME MEASURES:Stress (Perceived Stress Scale), anxiety and depression (Hospital Anxiety and Depression Scale) were evaluated in both groups at baseline (n=44), end of intervention (n=32) and 3-month follow-up (n=19). RESULTS:Both courses were associated with significant reductions in stress (.b 6.38; 95% CI 1.74 to 11.02; MBSR 9.69; 95% CI 4.9 to 14.5) and anxiety (.b 3.36; 95% CI 1.69 to 5.0; MBSR 4.06; 95% CI 2.6 to 5.5). MBSR was associated with improved depression outcomes (4.3; 95% CI 2.5 to 6.11). No differences were found in terms of experience and acceptability. Four main themes were identified including preconceptions, factors influencing delivery, perceived impact and training desires/practical application. CONCLUSION:.b Foundations appears as beneficial as MBSR in anxiety and stress reduction but MBSR may be more appropriate for depression. Consideration over implementation factors may largely improve the acceptability of mindfulness courses for teachers. Further research with larger samples is needed.

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A comparison of two collapsing methods in different approaches.

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A comparison of two collapsing methods in different approaches.

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