Prostate Cancer Risk Is not Altered by TP53AIP1 Germline Mutations in a German Case-Control Series

Luedeke, Manuel; Coinac, Irina; Linnert, Carmen M.; Bogdanova, Natalia; Rinckleb, Antje E.; Schrader, Mark; Vogel, Walther; Hoegel, Josef; Meyer, Andreas; Dörk, Thilo; Maier, Christiane

doi:10.1371/journal.pone.0034128

Cited by 6 publications

(10 citation statements)

References 9 publications

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“…When considering the application of PheLEx in extracting misclassification, there are two components of the PheLEx framework that lead to significant improvements in overall performance compared to existing methods[49, 73, 77] in datasets simulated with realistic genotype-specific odds ratios[83, 84, 103]. First, PheLEx includes an Adaptive Metropolis-Hastings step within Gibbs sampling that improves posterior sampling resulting in improved performance (Fig 2, S1 and S5 Figs).…”

Section: Discussionmentioning

confidence: 99%

“…The genetic model with 30 disease-associated SNPs out of total 10,000 preserves the characteristic sparsity of “true signal” in GWAS datasets, while keeping the dataset size manageable for rigorous simulations. Number of samples, number of SNPs, number of disease-associated SNPs, and effect sizes were set in accordance with precedence in literature[106-108], where in particular, effect sizes were selected to ensure genotype-specific disease odds ratio remained realistic (in the range: 1-3)[83-85] (S3 Fig). For each simulated true disease phenotype Y’ , differential misclassification was introduced at varying degrees by switching a fraction of randomly selected controls to cases.…”

Section: Methodsmentioning

confidence: 99%

“…The core of PheLEx is a single modeling framework allowing for differential misclassification in GWAS phenotypes with an underlying full mixed model to account for population structure and genetic relatedness. When concentrating only on the problem of phenotype misclassification, we show that the PheLEx framework dramatically improved performance when analyzing simulated GWAS data that included realistic proportions of disease-associated genotypes in a genome-wide scan and genotype-specific disease-odds ratios consistent with empirical observation[83-85]. Other applications of PheLEx include exploring differential patterns between misclassified and non-misclassified cases within GWAS datasets that may point to potential causes such as misdiagnosis or disease subtypes.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shafquat

Crystal

Mezey

2019

Preprint

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“…In contrast, for the case misclassification rate, we assume a flat (uniform) prior, such that we are not making a strong prior assumption on the probability that a case is misclassified. We additionally note that we assume a flat prior on variance parameter σ u 2 [125,126] and normal prior Pr(β)~N(0, 1) on the distribution of SNP effects with true genetic associations, where this latter assumption seems justified given estimates of SNP associations in GWAS [83][84][85][86][87].…”

Section: Phelex Framework Priors and Identifiability Considerationsmentioning

confidence: 99%

“…The core of PheLEx is a single modeling framework allowing for differential misclassification in GWAS phenotypes with an underlying full mixed model to account for genetic relatedness and population structure. When concentrating only on the problem of phenotype misclassification, we show that the PheLEx framework dramatically improved performance when analyzing simulated GWAS data that included realistic effect sizes and proportions of disease-associated genotypes in a genome-wide scan consistent with empirical observation [83][84][85][86][87]. Other applications of PheLEx include exploring differential patterns between misclassified and nonmisclassified cases within GWAS datasets that may point to potential causes such as misdiagnosis or disease subtypes.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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Background: Heterogeneity in the 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. Results: 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 potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation. Conclusion: PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

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Targeting the p53 signaling pathway in cancers: Molecular mechanisms and clinical studies

Shen¹,

Wang²,

Mao³

et al. 2023

MedComm

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Tumor suppressor p53 can transcriptionally activate downstream genes in response to stress, and then regulate the cell cycle, DNA repair, metabolism, angiogenesis, apoptosis, and other biological responses. p53 has seven functional domains and 12 splice isoforms, and different domains and subtypes play different roles. The activation and inactivation of p53 are finely regulated and are associated with phosphorylation/acetylation modification and ubiquitination modification, respectively. Abnormal activation of p53 is closely related to the occurrence and development of cancer. While targeted therapy of the p53 signaling pathway is still in its early stages and only a few drugs or treatments have entered clinical trials, the development of new drugs and ongoing clinical trials are expected to lead to the widespread use of p53 signaling‐targeted therapy in cancer treatment in the future. TRIAP1 is a novel p53 downstream inhibitor of apoptosis. TRIAP1 is the homolog of yeast mitochondrial intermembrane protein MDM35, which can play a tumor‐promoting role by blocking the mitochondria‐dependent apoptosis pathway. This work provides a systematic overview of recent basic research and clinical progress in the p53 signaling pathway and proposes that TRIAP1 is an important therapeutic target downstream of p53 signaling.

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Prostate Cancer Risk Is not Altered by TP53AIP1 Germline Mutations in a German Case-Control Series

Cited by 6 publications

References 9 publications

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

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

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

Targeting the p53 signaling pathway in cancers: Molecular mechanisms and clinical studies

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