PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models

González-Seoane, Borja; Ponte-Fernández, Christian; González-Domínguez, Jorge; Martín, María J.

doi:10.1186/s12859-022-04645-7

Cited by 5 publications

(8 citation statements)

References 15 publications

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“…The probability of 3-loci epistasis is taken from penetrance tables that were generated using PyTOXO package [37]. In this experiment 3 penetrance tables were created for 2-loci and 3 tables for 3-loci epistatic models with heritability of 0.10, 0.25 and 0.50 (the details, including frequencies, can be found in Supplemental Tables 1,2).…”

Section: Epistasis Simulation Experimentsmentioning

confidence: 99%

Deep Learning captures the effect of epistasis in multifactorial diseases

Perelygin,

Kamelin,

Syzrantsev

et al. 2024

Preprint

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Background Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer's disease, diabetes, cardiovascular disease, cancer, and others, association between individual SNPs and disease could be non-linear due to epistatic interactions. The aim of the presented study is to explore the power of non-linear machine learning algorithms and deep learning models to predict the risk of multifactorial diseases with epistasis. Results First, we tested ensemble tree methods and deep learning neural networks against LASSO linear regression model on simulated data with different types and strength of epistasis. The results showed that with the increase of strength of epistasis effect, non-linear models significantly outperform linear. Then the higher performance of non-linear models over linear was confirmed on real genetic data for multifactorial phenotypes such as obesity, type 1 diabetes, and psoriasis. From non-linear models, gradient boosting appeared to be the best model in obesity and psoriasis while deep learning methods significantly outperform linear approaches in type 1 diabetes. Conclusions Overall, our study underscores the efficacy of non-linear models and deep learning approaches in more accurately accounting for the effects of epistasis in simulations with specific configurations and in the context of certain diseases.

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Section: Epistasis Simulation Experimentsmentioning

confidence: 99%

Deep Learning captures the effect of epistasis in multifactorial diseases

Perelygin,

Kamelin,

Syzrantsev

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…To know more about PyToxo, we recommend reading our original paper, published in BMC Bioinformatics [1] this year.…”

Section: Discussionmentioning

confidence: 99%

“…As we explain in more detail in our BMC Bioinformatics paper [1], the mathematical approach followed for Toxo, and on which PyToxo is settled, is based on fixing heritability or prevalence and maximizing the other. So, instead of finding a specific combination of heritability and prevalence, like in original Equation 1, the Toxo library maximizes one of the two parameters, when the other is fixed.…”

Section: Mathematical Methodsmentioning

confidence: 99%

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Generating penetrance tables of high-order epistasis models with PyToxo

González-Seoane¹,

Ponte-Fernández²,

González-Domínguez³

et al.

Kalpa Publications in Computing

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The interaction among different genes when expressing a particular phenotype is known as epistasis. High-order epistasis, when more than two loci are involved, is an active research area because it could be the cause of many complex traits. The most common abstraction for specifying an epistasis interaction is through a penetrance table, which captures the probability of expressing the studied phenotype given a particular genotype.Although it is very common for simulators to use penetrance tables, most of them do not allow the user to generate them directly, or present limitations for high-order interac- tions and/or realistic prevalence and heritability values. In this work, we present PyToxo, a Python tool for generating penetrance tables from any-order epistasis models. PyToxo allows to work with more appropriate scenarios than other state-of-the-art tools. Addi- tionally, it also improves in terms of accuracy, speed and ease of use, being available as a library, through a CLI or through a cross-platform GUI.

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“…The onset and most phenotypic manifestations of heritable diseases can not be explained by a small number of genomic loci or only by the additive combination of loci associated with the diseases [127][128][129] . Consequently, studying such progressions/manifestations is more exacting and does not conform to the classic Mendelian paradigm that a few rare genomic variants are responsible for a disease, [130][131][132] .…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks to Identify Genetic Modifiers of Rare Complex Inheritable Diseases

Niktab¹

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Genome-wide association analyses (GWAS) studies based on frequentist statistics have often proven ineffective in deriving biological insights from sequencing data. These GWAS lack the machinery to safeguard against technical noise inherent to high throughput sequencing platforms and are not conceptually designed for processing large sets of high-dimensional genomic data. However, such shortcomings are not peculiar to GWAS and have been studied in other fields of science, such as signal processing and computer science, for a long time. In particular, machine learning techniques, especially deep learning models, have proven highly successful in dealing with noisy high-dimensional data. Recently it has been shown that these techniques can be effective for handling genomic data even when directly transferred from modern computer vision and natural language processing applications. This thesis builds off the existing suites of such methodologies and presents a robust computational pipeline to functionally annotate whole-genome sequencing data. Moreover, it discusses and presents a data solution to eﬀiciently process the large, heterogeneous datasets required for such analyses. The main objective of this thesis is to put forward a solution to identify variants that modify disease-causing mutations of complex heritable diseases. This is not a trivial problem given that the current gold standard approach, GWAS methodology, suffers not only from the drawbacks just described but is also underpowered by multiple testing (not useful for rare diseases) and fails to account for the epistatic nature of genetic interactions responsible for the onset and manifestation of complex diseases. Here, a set of cell-specific Gene Regulatory Networks (GRNs) inferred from dynamic genomic data was constructed. Most attempts to construct GRNs delineating such complex interactions relied on combining non-standardized high-throughput static datasets that contained false positive interactions and missing data points without insights into cell developmental states. To illuminate these intricate dynamic regulatory interconnections of the genome, specific to a tissue or a cell type, the Non-Stiff Dynamic Invertible Model of CO-Regulatory Networks (NS-DIMCORN) that allows unrestricted neural network architectures (to accommodate arbitrary depth increase for larger sets of genes) and training without partitioning the data dimensions was developed. NS-DIMCORN was trained on not-homogenized bulk tissue-specific RNA-seq and single-cell RNA-seq as a surrogate for cells’ continuous developmental states and modeled these highly dynamic systems with a set of ordinary differential equations. NS-DIMCORN yielded a continuous-time invertible generative model with unbiased density estimation only from RNA-seq read-count data and allowed time-flexible sampling of each gene’s expression level for ab initioassembly of genes regulatory network of specific cells. Secondly, Precise Graph-based Genome-Wide Annotation Sofware (PG-GWAS) was developed. For this purpose, embedding was used to map genomic variables to a vector of continuous numbers. Thus, each genomic variant was assigned a unique contextualized score that encoded the likelihood of effects on its respective gene products. These scores were pan-genomic by constructing a k-mer representation of all the haplotypes, independent of any “reference genome,” and were based only on each variant’s evolutionary constraints. Next, a graph representation of individuals’ genomes was constructed that integrated genomic variation scores, tissue-specific gene-gene interaction, and regulatory networks (assembled from GRNs) to allow the study of the genomic variants in aggregate and accounting for epistasis. Utilizing the Graph Attention mechanism identified these networks’ most critical interactions and allowed annotating the entire whole-genome graphs to determine the most prominent genomic features (i.e., groups of interacting genes) within each genome that could be responsible for different symptoms and onset in patients with the same disease-causing mutations. Eventually, to demonstrate the eﬀicacy of this approach, PG-GWAS was tested on new sets of sequencing data, where the result improved in standard GWAS and provided insight into disease epistasis.

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PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models

Cited by 5 publications

References 15 publications

Deep Learning captures the effect of epistasis in multifactorial diseases

Deep Learning captures the effect of epistasis in multifactorial diseases

Generating penetrance tables of high-order epistasis models with PyToxo

Graph Neural Networks to Identify Genetic Modifiers of Rare Complex Inheritable Diseases

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