Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

Lee, Sung Young; Kim, Sunmee; Kim, Yongkang; Oh, Bermseok; Hwang, Heungsun; Park, Taesung

doi:10.1186/s12920-019-0517-4

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…WES in larger cohorts also will allow robust bioinformatic analysis and hierarchical clustering to visualize and explore functional pathways associated with epilepsy after acute symptomatic seizures. 50 , 51 …”

Section: Discussionmentioning

confidence: 99%

Whole-exome sequencing with targeted analysis and epilepsy after acute symptomatic neonatal seizures

et al. 2021

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Background The contribution of pathogenic gene variants with development of epilepsy after acute symptomatic neonatal seizures is not known. Methods Case–control study of 20 trios in children with a history of acute symptomatic neonatal seizures: 10 with and 10 without post-neonatal epilepsy. We performed whole-exome sequencing (WES) and identified pathogenic de novo, transmitted, and non-transmitted variants from established and candidate epilepsy association genes and correlated prevalence of these variants with epilepsy outcomes. We performed a sensitivity analysis with genes associated with coronary artery disease (CAD). We analyzed variants throughout the exome to evaluate for differential enrichment of functional properties using exploratory KEGG searches. Results Querying 200 established and candidate epilepsy genes, pathogenic variants were identified in 5 children with post-neonatal epilepsy yet in only 1 child without subsequent epilepsy. There was no difference in the number of trios with non-transmitted pathogenic variants in epilepsy or CAD genes. An exploratory KEGG analysis demonstrated a relative enrichment in cell death pathways in children without subsequent epilepsy. Conclusions In this pilot study, children with epilepsy after acute symptomatic neonatal seizures had a higher prevalence of coding variants with a targeted epilepsy gene sequencing analysis compared to those patients without subsequent epilepsy. Impact We performed whole-exome sequencing (WES) in 20 trios, including 10 children with epilepsy and 10 without epilepsy, both after acute symptomatic neonatal seizures. Children with post-neonatal epilepsy had a higher burden of pathogenic variants in epilepsy-associated genes compared to those without post-neonatal epilepsy. Future studies evaluating this association may lead to a better understanding of the risk of epilepsy after acute symptomatic neonatal seizures and elucidate molecular pathways that are dysregulated after brain injury and implicated in epileptogenesis.

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Section: Discussionmentioning

confidence: 99%

Whole-exome sequencing with targeted analysis and epilepsy after acute symptomatic neonatal seizures

et al. 2021

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“…Future investigations of genetic sequencing in larger cohorts, perhaps leveraging existing databases of neonates with acute symptomatic seizures with longer follow-up duration will allow for improved estimates of test sensitivity and specificity as well as multivariate modeling with known risk factors of post-neonatal epilepsy. WES in larger cohorts also will allow robust bioinformatic analysis and hierarchical clustering to visualize and explore functional pathways associated with epilepsy after acute symptomatic seizures (Lee et al, 2016;Lee et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Whole exome sequencing with targeted gene analysis and epilepsy after acute symptomatic neonatal seizures

Numis

Gene

Sherr

et al. 2020

Preprint

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The utility of whole exome sequencing (WES) with targeted gene analysis has been important for establishing diagnosis and prognosis of severe early-onset epileptic encephalopathies, but the contribution of pathogenic gene variants to epileptogenesis after acute symptomatic neonatal seizures is not known. We performed a case-control study of 20 trios in children with a history of acute symptomatic neonatal seizures: 10 with and 10 without post-neonatal epilepsy. We used WES and identified pathogenic de novo, transmitted, and non-transmitted variants from genes with known association with epilepsy and correlated prevalence of these variants with epilepsy outcomes. We performed a sensitivity analysis with genes associated with coronary artery disease (CAD). Among 200 known genes associated with epilepsy, we identified pathogenic variants in six children with post-neonatal epilepsy and in two children without subsequent epilepsy (OR 6.0, 95% CI 0.6-80, p=0.07). There was no difference in the number of children with pathogenic variants in CAD genes between groups. Larger studies evaluating this association may lead to a better understanding of the risk of epilepsy after acute symptomatic neonatal seizures and elucidate molecular pathways that are dysregulated after brain injury and implicated in epileptogenesis. Introduction:Neonatal seizures due to brain injury (acute symptomatic seizures) are typically self-limited in the neonatal period, but as many as 25% of survivors will later develop recurrent unprovoked seizures (epilepsy) and approximately 10% of survivors are diagnosed with infantile spasms (IS)

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“…Moreover, ERA allows researchers to generate predictive models because it searches for components that maximize predictive accuracy, without having to eliminate any predictors of interest to avoid multicollinearity. Indeed, the practical usefulness of ERA lies in its predictive nature; ERA has been well blended with many statistical techniques for prediction problems, e.g., regularizations ( Hwang et al, 2015 ; Lee et al, 2019 ; Kim et al, 2020 ) and a tree-based supervised learning method ( Kim and Hwang, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prediction-Oriented Model Selection Metrics for Extended Redundancy Analysis

Kim

Hwang

2022

Front. Psychol.

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Extended redundancy analysis (ERA) is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid the overly optimistic assessment, we introduce a new model evaluation approach for ERA, which utilizes computer-intensive resampling methods to assess how well a model performs on unseen data. Specifically, we suggest several new model evaluation metrics for ERA that compute a model’s performance on out-of-sample data, i.e., data not used for model development. Although considerable work has been done in machine learning and statistics to examine the utility of cross-validation and bootstrap variants for assessing such out-of-sample predictive performance, to date, no research has been carried out in the context of ERA. We use simulated and real data examples to compare the proposed model evaluation approach with the conventional one. Results show the conventional approach always favor more complex ERA models, thereby failing to prevent the problem of overfitting in model selection. Conversely, the proposed approach can select the true ERA model among many mis-specified (i.e., underfitted and overfitted) models.

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Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

Cited by 7 publications

References 36 publications

Whole-exome sequencing with targeted analysis and epilepsy after acute symptomatic neonatal seizures

Whole-exome sequencing with targeted analysis and epilepsy after acute symptomatic neonatal seizures

Whole exome sequencing with targeted gene analysis and epilepsy after acute symptomatic neonatal seizures

Evaluation of Prediction-Oriented Model Selection Metrics for Extended Redundancy Analysis

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