NTRK2 (TrkB gene) variants and temporal lobe epilepsy: A genetic association study

Torres, Carolina Machado; Siebert, Marina; Bock, Hugo; Mota, Suelen Mandelli; Krammer, Bárbara Reis; Duarte, Juliana Ávila; Castan, Juliana Unis; Castro, Luiza Amaral de; Saraiva-Pereira, Maria Luiza; Bianchin, Marino Muxfeldt

doi:10.1016/j.eplepsyres.2017.08.010

Cited by 15 publications

(14 citation statements)

References 67 publications

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“…28 29 Taken together, these data suggest that NTRK2 could be specifically associated with drug-resistant forms of epilepsy, a phenotype that has not been systematically investigated. 28 29 Taken together, these data suggest that NTRK2 could be specifically associated with drug-resistant forms of epilepsy, a phenotype that has not been systematically investigated.…”

Section: Discussionmentioning

confidence: 94%

“…(rs10868235) and temporal lobe epilepsy. 28 Additionally, Hamdan et al, in a study aiming to elucidate the genetic basis of epileptic encephalopathies, found a pathogenic de novo mutation in NTRK2, p.Tyr434Cys, in four unrelated patients with a similar syndrome characterized by severe global developmental delay, optic nerve hypoplasia with visual impairment, and intractable seizures. 29 Taken together, these data suggest that NTRK2 could be specifically associated with drug-resistant forms of epilepsy, a phenotype that has not been systematically investigated.…”

Section: Discussionmentioning

confidence: 99%

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Drug‐resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

et al. 2019

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Objective: Up to 40% of patients with epilepsy become drug resistant (DRE). Genetic factors are likely to play a role. While efforts have focused on the transporter and target hypotheses, neither of them fully explains the pan-pharmacoresistance seen in DRE. Materials and methods:In this study, we developed and used a phenotyping algorithm for the identification of DRE, responders, and epilepsy-free controls that were sequenced using a gene panel developed by the Pharmacogenomics Research Network (PGRN), which includes 82 genes involved in drug response. We tested the transporter hypothesis of DRE, the association between drug resistance and variants in the ATP-binding cassette family of genes previously associated with DRE, and also investigated potential new genetic factors. Results:In the analysis of DRE vs controls, NTRK2 was significantly associated with DRE (rs76950094; P = 1.19 × 10 −7 and gene-based P-value = 1.67 × 10 −4 ). NTRK2encodes TrkB, which is involved in the development and maturation of the central nervous system, and increased activation of TrkB signaling is suggested to promote epilepsy. Conclusion:Although the role of NTRK2 in DRE needs to be elucidated, these results support alternative mechanisms underlying DRE, complementary to the existing hypotheses, that should be evaluated. K E Y W O R D Salgorithm, drug-resistant epilepsy, electronic health records, NTRK2

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Drug‐resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

et al. 2019

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“…The T/T genotype showed a statistical trend for increase in patients with epilepsy [16]. Moreover, an association between rs10868235 was observed in the impulsivity phenotypes as well as in quantitative hopelessness among healthy adolescents [18].…”

Section: Discussionmentioning

confidence: 87%

“…To the best of our knowledge, this is the first work that investigates the association between the TrkB gene polymorphisms and schizophrenia in Caucasian individuals. Data showed that the SNPs of the TrkB gene that were analysed for this paper have been associated with depression [13], suicides in individuals with a psychiatric disorder [14], the response to antidepressant treatment [15], and epilepsy [16].…”

Section: Introductionmentioning

confidence: 94%

An Analysis of Five TrkB Gene Polymorphisms in Schizophrenia and the Interaction of Its Haplotype with rs6265 BDNF Gene Polymorphism

et al. 2020

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Aim. The BDNF dysfunction in the schizophrenia has been soundly documented. The TrkB gene is a high-affinity receptor of the BDNF that is changed in schizophrenia and mood disorders. The study had two aims: first, to identify whether the five nucleotide polymorphisms (SNPs) in TrkB gene are associated with a diagnosis of schizophrenia; and the latter, if any association exists between the TrkB SNPs and psychopathology, suicide attempts, and family history of schizophrenia in a Caucasian population. Methods. Case-control study (401 patients and 657 healthy controls) was used to examine a predisposition for schizophrenia. The tests for psychopathology, suicide attempts, and family history of schizophrenia were conducted only in patient group. The severity of the schizophrenia was measured using the five-factor model of the PANSS. In addition, the haplotype analysis for both the separate for SNPs of TrkB gene and in combination with the rs6265 SNP BDNF gene was conducted. Results. Our case-control study revealed that the genetic variants of rs10868235 (T/T polymorphic genotype) and rs1387923 (G/G polymorphic genotype) of the TrkB gene were associated with a higher risk of developing schizophrenia in men. However, the A/A wild genotype of rs1387923 was connected with a lower risk for both the development of and the family manifestation of schizophrenia in men. The G polymorphic allele of rs1565445 was associated with an increased risk of suicide in schizophrenia. The tested SNPs of the TrkB gene did not modulate the psychopathology of schizophrenia. The haplotype that was built with five SNPs in the TrkB gene was protective for men, but after joining the rs6265 SNP of the BDNF gene, a haplotype that was protective for women was created.

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“…When looking at SNPs in LD with candidate SNP rs12379440 (SNP in OR2S2 ), these include SNPs in genes AK3 (MIM: 609290), DIRAS2 (MIM: 607863), NTRK2 (MIM: 600456), MAMDC2 (MIM: 612879) and OSTF1 (MIM: 610180). At least one or more of these genes have associations with seizure phenotypes[93-95], attention deficit hyperactivity disorder (ADHD)[96]and temporal lobe epilepsy[97].…”

Section: Resultsmentioning

confidence: 99%

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Shafquat

Crystal

Mezey

2019

Preprint

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AbstractHeterogeneity in definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new candidate loci from existing GWAS data by analyzing epilepsy and bipolar disorder phenotypes available from the UK Biobank dataset, where we identify new candidate disease loci not previously reported for these datasets that have biological connections to the disease phenotypes and/or were identified in independent GWAS. In the discussion, we consider both the broader consequences and importance of careful interpretation of misclassification correction in GWAS phenotypes, as well as potential of PheLEx for re-analyzing existing GWAS data to make novel discoveries.Author SummaryPrevalent misdiagnosis of diseases due to lack of understanding and/or gold-standard diagnostic measures can impact any analytics that follow. These misdiagnosis errors are especially significant in the domain of psychiatric or psychological disorders where the definition of disease and/or their diagnostic tools are always in flux or under further improvement. Here, we propose a method to extract misdiagnosis from disease and infer the correct disease phenotype. We examined the performance of this method on rigorous simulations and real disease phenotypes obtained from the UK Biobank database. We found that this method successfully recovered misdiagnosed individuals in simulations using a carefully designed hierarchical Bayesian latent variable model framework. For real disease phenotypes, epilepsy and bipolar disorder, this method not only suggested an alternate phenotype but results from this method were also used to discover new genomic loci that have been previously showed to be associated with the respective phenotypes, suggesting that this method can be further used to reanalyze large-scale genetic datasets to discover novel loci that might be ignored using traditional methodologies.

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NTRK2 (TrkB gene) variants and temporal lobe epilepsy: A genetic association study

Cited by 15 publications

References 67 publications

Drug‐resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

Drug‐resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

An Analysis of Five TrkB Gene Polymorphisms in Schizophrenia and the Interaction of Its Haplotype with rs6265 BDNF Gene Polymorphism

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

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