Simulating systems genetics data with SysGenSIM

Predicting the genetic component of gene expression using gene regulatory networks

Motivation

Gene expression prediction plays a vital role in transcriptome-wide association studies. Traditional models rely on genetic variants in close genomic proximity to the gene of interest to predict the genetic component of gene expression. Here, we propose a novel approach incorporating distal genetic variants acting through gene regulatory networks, in line with the omnigenic model of complex traits.

Results

Using causal and coexpression Bayesian networks reconstructed from genomic and transcriptomic data, inference of gene expression from genotypic data is achieved through a two-step process. Initially, the expression level of each gene is predicted using its local genetic variants. The residual differences between the observed and predicted expression levels are then modeled using the genotype information of parent and/or grandparent nodes in the network. The final predicted expression level is obtained by summing the predictions from both models, effectively incorporating both local and distal genetic influences. Using regularized regression techniques for parameter estimation, we found that gene regulatory network-based gene expression prediction outperformed the traditional approach on simulated data and real data from yeast and humans. This study provides important insights into the challenge of gene expression prediction for transcriptome-wide association studies.

Availability and implementation

The code is available on Github at github.com/guutama/GRN-TI.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

Gene regulatory network inference methodology for genomic and transcriptomic data acquired in genetically related heterozygote individuals

MOTIVATION

Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids.

RESULTS

First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component.

AVAILABILITY AND IMPLEMENTATION

Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the Yeastract database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.

sismonr: simulation of in silico multi-omic networks with adjustable ploidy and post-transcriptional regulation in R

SUMMARY

We present sismonr, an R package for an integral generation and simulation of in silico biological systems. The package generates gene regulatory networks, which include protein-coding and non-coding genes along with different transcriptional and post-transcriptional regulations. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations but can be defined by the user to higher ploidies. A choice of stochastic simulation algorithms allows us to simulate the expression profiles of the genes in the in silico system. We illustrate the use of sismonr by simulating the anthocyanin biosynthesis regulation pathway for three genetically distinct in silico plants.

AVAILABILITY AND IMPLEMENTATION

The sismonr package is implemented in R and Julia and is publicly available on the CRAN repository (https://CRAN.R-project.org/package=sismonr). A detailed tutorial is available from GitHub at https://oliviaab.github.io/sismonr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Understanding how genetic variation between individuals determines variation in observable traits or disease risk is one of the core aims of genetics. It is known that genetic variation often affects gene regulatory DNA elements and directly causes variation in expression of nearby genes. This effect in turn cascades down to other genes via the complex pathways and gene interaction networks that ultimately govern how cells operate in an ever changing environment. In theory, when genetic variation and gene expression levels are measured simultaneously in a large number of individuals, the causal effects of genes on each other can be inferred using statistical models similar to those used in randomized controlled trials. We developed a novel method and ultra-fast software Findr which, unlike existing methods, takes into account the complex but unknown network context when predicting causality between specific gene pairs. Findr’s predictions have a significantly higher overlap with known gene networks compared to existing methods, using both simulated and real data. Findr is also nearly a million times faster, and hence the only software in its class that can handle modern datasets where the expression levels of ten-thousands of genes are simultaneously measured in hundreds to thousands of individuals.

GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis

Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity. We use the defined clusters to perform an exhaustive search to retrieve the best predictor gene subsets for each target gene, according to multivariate criterion functions. GeNICE greatly reduces the search space because predictor candidates are restricted to one gene per cluster. Finally, a multivariate analysis is performed for each defined predictor subset to retrieve minimal subsets and to simplify the network. In our experiments with in silico generated data sets, GeNICE achieved substantial computational time reduction when compared to solutions without the clustering step, while preserving the gene expression prediction accuracy even when the number of clusters is small (about 50) relative to the number of genes (order of thousands). For a Plasmodium falciparum microarray data set, the prediction accuracy achieved by GeNICE was roughly 97%, while the respective topologies involving glycolytic and apicoplast seed genes had a very large intramodularity, very small interconnection between modules, and some module hub genes, reflecting small-world and scale-free topological properties, as expected.

Prediction of Genetic Interactions Using Machine Learning and Network Properties

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

Bayesian network reconstruction using systems genetics data: comparison of MCMC methods

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.

Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation

Background

The data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects.

Results

In this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges.

Conclusions

This paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation.

An information-theoretic machine learning approach to expression QTL analysis

Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL), and on other chromosomes (trans-eQTL). Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What’s more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS), were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL) with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA). In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.

An effective framework for reconstructing gene regulatory networks from genetical genomics data

MOTIVATION

Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible.

RESULTS

We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast.

AVAILABILITY

A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html

CONTACT

klamt@mpi-magdeburg.mpg.de.

Teamwork: improved eQTL mapping using combinations of machine learning methods

Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth "Dialogue for Reverse Engineering Assessments and Methods" (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on "Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.

A bayesian mixed regression based prediction of quantitative traits from molecular marker and gene expression data

Both molecular marker and gene expression data were considered alone as well as jointly to serve as additive predictors for two pathogen-activity-phenotypes in real recombinant inbred lines of soybean. For unobserved phenotype prediction, we used a bayesian hierarchical regression modeling, where the number of possible predictors in the model was controlled by different selection strategies tested. Our initial findings were submitted for DREAM5 (the 5th Dialogue on Reverse Engineering Assessment and Methods challenge) and were judged to be the best in sub-challenge B3 wherein both functional genomic and genetic data were used to predict the phenotypes. In this work we further improve upon this previous work by considering various predictor selection strategies and cross-validation was used to measure accuracy of in-data and out-data predictions. The results from various model choices indicate that for this data use of both data types (namely functional genomic and genetic) simultaneously improves out-data prediction accuracy. Adequate goodness-of-fit can be easily achieved with more complex models for both phenotypes, since the number of potential predictors is large and the sample size is not small. We also further studied gene-set enrichment (for continuous phenotype) in the biological process in question and chromosomal enrichment of the gene set. The methodological contribution of this paper is in exploration of variable selection techniques to alleviate the problem of over-fitting. Different strategies based on the nature of covariates were explored and all methods were implemented under the bayesian hierarchical modeling framework with indicator-based covariate selection. All the models based in careful variable selection procedure were found to produce significant results based on permutation test.