Reconstructing the Molecular Function of Genetic Variation in Regulatory Networks

ABSTRACTThe impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multi-tissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

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Dynamics of genetic variation in Transcription Factors and its implications for the evolution of regulatory networks in Bacteria

10.1101/785691 ◽

2019 ◽

Author(s):

Farhan Ali ◽

Aswin Sai Narain Seshasayee

Keyword(s):

Genetic Variation ◽

Transcription Factors ◽

Regulatory Networks ◽

Large Scale ◽

Target Genes ◽

Point Mutations ◽

Purifying Selection ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

Global Regulators

AbstractThe evolution of bacterial regulatory networks has largely been explained at macroevolutionary scales through lateral gene transfer and gene duplication. Transcription factors (TF) have been found to be less conserved across species than their target genes (TG). This would be expected if TFs accumulate mutations faster than TGs. This hypothesis is supported by several lab evolution studies which found TFs, especially global regulators, to be frequently mutated. Despite these studies, the contribution of point mutations in TFs to the evolution of regulatory network is poorly understood. We tested if TFs show greater genetic variation than their TGs using whole-genome sequencing data from a large collection of E coli isolates. We found TFs to be less diverse, across natural isolates, due to their regulatory roles. TFs were enriched in mutations in multiple adaptive lab evolution studies but not in mutation accumulation. However, over long-term evolution, relative frequency of mutations in TFs showed a gradual decay after a rapid initial burst. Our results suggest that point mutations, conferring large-scale expression changes, may drive the early stages of adaptation but gene regulation is subjected to stronger purifying selection post adaptation.

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Propagation of genetic variation in gene regulatory networks

Physica D Nonlinear Phenomena ◽

10.1016/j.physd.2013.04.002 ◽

2013 ◽

Vol 256-257 ◽

pp. 7-20 ◽

Cited By ~ 2

Author(s):

Erik Plahte ◽

Arne B. Gjuvsland ◽

Stig W. Omholt

Keyword(s):

Genetic Variation ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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Integration of Genetic Variation as External Perturbation to Reverse Engineer Regulatory Networks from Gene Expression Data

Gene Network Inference ◽

10.1007/978-3-642-45161-4_7 ◽

2013 ◽

pp. 107-118

Author(s):

Francesco Sambo ◽

Tiziana Sanavia ◽

Barbara Di Camillo

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Gene Expression Data ◽

Regulatory Networks ◽

External Perturbation ◽

Expression Data ◽

Reverse Engineer

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Dynamics of genetic variation in transcription factors and its implications for the evolution of regulatory networks in Bacteria

Nucleic Acids Research ◽

10.1093/nar/gkaa162 ◽

2020 ◽

Vol 48 (8) ◽

pp. 4100-4114

Author(s):

Farhan Ali ◽

Aswin Sai Narain Seshasayee

Keyword(s):

Genetic Variation ◽

Transcription Factors ◽

Regulatory Networks ◽

Large Scale ◽

Mutation Frequency ◽

Target Genes ◽

Point Mutations ◽

Whole Genome Sequencing Data ◽

Laboratory Evolution

Abstract The evolution of regulatory networks in Bacteria has largely been explained at macroevolutionary scales through lateral gene transfer and gene duplication. Transcription factors (TF) have been found to be less conserved across species than their target genes (TG). This would be expected if TFs accumulate mutations faster than TGs. This hypothesis is supported by several lab evolution studies which found TFs, especially global regulators, to be frequently mutated. Despite these studies, the contribution of point mutations in TFs to the evolution of regulatory network is poorly understood. We tested if TFs show greater genetic variation than their TGs using whole-genome sequencing data from a large collection of Escherichia coli isolates. TFs were less diverse than their TGs across natural isolates, with TFs of large regulons being more conserved. In contrast, TFs showed higher mutation frequency in adaptive laboratory evolution experiments. However, over long-term laboratory evolution spanning 60 000 generations, mutation frequency in TFs gradually declined after a rapid initial burst. Extrapolating the dynamics of genetic variation from long-term laboratory evolution to natural populations, we propose that point mutations, conferring large-scale gene expression changes, may drive the early stages of adaptation but gene regulation is subjected to stronger purifying selection post adaptation.

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Topological features of gene regulatory networks predict patterns of natural diversity in environmental response

10.1101/080804 ◽

2016 ◽

Author(s):

David L. Des Marais ◽

Rafael F. Guerrero ◽

Jesse R. Lasky ◽

Samuel V. Scarpino

Keyword(s):

Genetic Variation ◽

Drought Stress ◽

Molecular Interactions ◽

Complex Traits ◽

Regulatory Networks ◽

Climatic Factors ◽

Environmental Response ◽

Variable Response ◽

Cold Response ◽

Topological Features

AbstractMolecular interactions affect the evolution of complex traits. For instance, adaptation may be constrained by pleiotropic or epistatic effects, both of which will be reflected in the structure of molecular interaction networks. To date, empirical studies investigating the role of molecular interactions in phenotypic evolution have been idiosyncratic, offering no clear patterns. Here, we investigated the network topology of genes putatively involved in local adaptation to two abiotic stressors—drought and cold—in Arabidopsis thaliana. Our findings suggest that the gene-interaction topologies for both cold and drought stress response are non-random, with genes that show genetic variation in drought response (GxE) being significantly more peripheral and cold response genes being significantly more central than genes not involved in either response. We suggest that the observed topologies reflect different constraints on the genetic pathways involved in the assayed phenotypes. The approach presented here may inform predictive models linking genetic variation in molecular signaling networks with phenotypic variation, specifically traits involved in environmental response.Significance StatementOur study focuses on genes whose transcriptional activity exhibits genetic variation in response to the environment, or “GxE.” GxE is a widely observed phenomenon of critical importance to understanding the genotype-to-phenotype map, the evolution of natural populations, medical genetics, population response to climate change, and agricultural improvement. We investigated expression GxE in plant responses to two abiotic cues: cold and drought. We found that genes showing genetically variable response to cold stress are centrally located in regulatory networks whereas genes showing genetically variable response to drought stress are peripherally located in regulatory networks. This result suggests that selection is presented with vastly different mutational landscapes for shaping evolutionary or breeding response to these two important climatic factors

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High-dimensional Bayesian network inference from systems genetics data using genetic node ordering

10.1101/501460 ◽

2018 ◽

Author(s):

Lingfei Wang ◽

Pieter Audenaert ◽

Tom Michoel

Keyword(s):

Genetic Variation ◽

Bayesian Network ◽

Gene Regulatory Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Network Inference ◽

High Dimensional ◽

Systems Genetics ◽

Gene Regulatory ◽

Bayesian Network Inference

AbstractStudying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher over-lap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

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