Extending the use of GWAS data by combining data from different genetic platforms

BACKGROUND: In the past decade many Genome-wide Association Studies (GWAS) were performed that discovered new associations between single-nucleotide polymorphisms (SNPs) and various phenotypes. Imputation methods are widely used in GWAS. They facilitate the phenotype association with variants that are not directly genotyped. Imputation methods can also be used to combine and analyse data genotyped on different genotyping arrays. In this study we investigated the imputation quality and efficiency of two different approaches of combining GWAS data from different genotyping platforms. We investigated whether combining data from different platforms before the actual imputation performs better than combining the data from different platforms after imputation. METHODS: In total 979 unique individuals from the AMC-PAS cohort were genotyped on 3 different platforms. A total of 706 individuals were genotyped on the MetaboChip, a total of 757 individuals were genotyped on the 50K gene-centric Human CVD BeadChip, and a total of 955 individuals were genotyped on the HumanExome chip. A total of 397 individuals were genotyped on all 3 individual platforms. After pre-imputation quality control (QC), Minimac in combination with MaCH was used for the imputation of all samples with the 1,000 genomes reference panel. All imputed markers with an r2 value of <0.3 were excluded in our post-imputation QC. RESULTS: A total of 397 individuals were genotyped on all three platforms. All three datasets were carefully matched on strand, SNP ID and genomic coordinates. This resulted in a dataset of 979 unique individuals and a total of 258,925 unique markers. A total of 4,117,036 SNPs were available when imputation was performed before merging the three datasets. A total of 3,933,494 SNPs were available when imputation was done on the combined set. Our results suggest that imputation of individual datasets before merging performs slightly better than after combining the different datasets. CONCLUSIONS: Imputation of datasets genotyped by different platforms before merging generates more SNPs than imputation after putting the datasets together

The design of a learning environment based on the theory of multiple intelligence and the study its effectiveness on the achievements, attitudes and retention of students

AbstractMultiple intelligences theory (MIT) which takes into account students’ individual differences has an important role in the teaching and learning process. In this study a novel learning environment based on MIT which takes students’ interests and needs into an account has been developed. Many activities were carried out depending on students’ intelligence types. The effects of different activities on students’ achievements, attitudes toward chemistry and retention of knowledge in periodical features’ variety at the 10th class were measured and compared. The comparison between experimental group, which was instructed through MIT learning strategies and materials, and control group thought by traditional methods was observed. The research was applied in the first semester of 2009-2010 education years. The study carried out on 75 high school students in Izmir. The application of the study was lasted for 8 weeks containing methods and tests’ practices. Following MIT assessment survey, achievement test and attitude scales were used to analyze its effectiveness. Based on the theories and literature data, an instructional material included concept maps, puzzles, stories, classical music in background, group games, and photos about periodic features’ variety as an alternative to traditional written material. As a result of statistical analysis there were significant differences between achievement post-test and attitudes post-test towards chemistry course of control and experimental groups. Consequently, instructional methods needed to be varied so students could use their intellectual strengths to better understand topics, increase their intrinsic motivation, intervention and encourage active student engagement to improve learning at middle school level

Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers–reaching the frontiers of individual risk prediction

Aims: This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. / Methods and results: Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27-55]. During a median follow-up of 4.3 years (IQR 1.7-7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability {C-statistic 0.83 [95% confidence interval (CI) 0.78-0.88]}. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61-0.75). / Conclusion: This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Background and purpose: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. / Methods: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. / Results: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. / Conclusion: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features