Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems. From morphological characters, to transcriptional or metabolic networks, the correlations we routinely observe between traits reflect a shared regulation that remains poorly understood and difficult to study. To address this problem, we developed a new and flexible approach that allows us to identify factors associated with variation in correlation between individuals. Here, we use data from three large human cohorts to study the effects of genetic variation and environmental perturbation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (namely, infection and disease) lead to a systematic loss of correlation, which we define as ‘decoherence’. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we show that correlation itself is a trait under genetic control: specifically, we mapped and replicated hundreds of ‘correlation QTLs’, which often involve transcription factors or their known target genes. Together, this work furthers our understanding of how and why coordinated biological processes break down, and highlights the role of decoherence in disease emergence.

[1]  M. Siegal,et al.  Decanalizing thinking on genetic canalization. , 2019, Seminars in cell & developmental biology.

[2]  M. Schatz,et al.  Addressing confounding artifacts in reconstruction of gene co-expression networks , 2018, bioRxiv.

[3]  Barbara Kracher,et al.  The Defense Phytohormone Signaling Network Enables Rapid, High-Amplitude Transcriptional Reprogramming during Effector-Triggered Immunity[OPEN] , 2018, Plant Cell.

[4]  M. G. van der Wijst,et al.  Single-cell RNA sequencing identifies cell type-specific cis-eQTLs and co-expression QTLs , 2018, Nature Genetics.

[5]  Stephen A Ramsey,et al.  Differential gene regulatory networks in development and disease , 2017, Cellular and Molecular Life Sciences.

[6]  Jason A. Corwin,et al.  Plastic Transcriptomes Stabilize Immunity to Pathogen Diversity: The Jasmonic Acid and Salicylic Acid Networks within the Arabidopsis/Botrytis Pathosystem[OPEN] , 2017, Plant Cell.

[7]  Luke R. Lloyd-Jones,et al.  Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood , 2017, Nature Communications.

[8]  Lauren M. McIntyre,et al.  Back to the Future: Multiparent Populations Provide the Key to Unlocking the Genetic Basis of Complex Traits , 2017, Genetics.

[9]  E. Ehler,et al.  Tropomyosin 1: Multiple roles in the developing heart and in the formation of congenital heart defects , 2017, Journal of molecular and cellular cardiology.

[10]  João Pedro de Magalhães,et al.  Gene co-expression analysis for functional classification and gene–disease predictions , 2017, Briefings Bioinform..

[11]  J. Satoh,et al.  Plasma microRNA biomarker detection for mild cognitive impairment using differential correlation analysis , 2016, Biomarker Research.

[12]  Liam G. Fearnley,et al.  An interaction map of circulating metabolites, immune gene networks, and their genetic regulation , 2016, Genome Biology.

[13]  Alexis Battle,et al.  Co-expression networks reveal the tissue-specific regulation of transcription and splicing , 2019 .

[14]  M. Fukuda,et al.  Neuronal Rap1 Regulates Energy Balance, Glucose Homeostasis, and Leptin Actions. , 2016, Cell reports.

[15]  S. Brunak,et al.  Network biology concepts in complex disease comorbidities , 2016, Nature Reviews Genetics.

[16]  Chuan Gao,et al.  Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering , 2016, PLoS Comput. Biol..

[17]  G. Deep,et al.  Oxidative stress and metabolic disorders: Pathogenesis and therapeutic strategies. , 2016, Life sciences.

[18]  Roland R. Regoes,et al.  Investigating the Consequences of Interference between Multiple CD8+ T Cell Escape Mutations in Early HIV Infection , 2016, PLoS Comput. Biol..

[19]  Anushya Muruganujan,et al.  PANTHER version 10: expanded protein families and functions, and analysis tools , 2015, Nucleic Acids Res..

[20]  Hsuan-Cheng Huang,et al.  Functional Analysis and Characterization of Differential Coexpression Networks , 2015, Scientific Reports.

[21]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[22]  Jung Eun Shim,et al.  TRRUST: a reference database of human transcriptional regulatory interactions , 2015, Scientific Reports.

[23]  G. Kempermann Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[24]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[25]  E. Stone,et al.  Genetic basis of transcriptome diversity in Drosophila melanogaster , 2015, Proceedings of the National Academy of Sciences.

[26]  A. Wingo,et al.  Blood gene expression profiles suggest altered immune function associated with symptoms of generalized anxiety disorder , 2015, Brain, Behavior, and Immunity.

[27]  A. Long,et al.  Dissecting complex traits using the Drosophila Synthetic Population Resource. , 2014, Trends in genetics : TIG.

[28]  W. Buttemer,et al.  Early-developmental stress, repeatability, and canalization in a suite of physiological and behavioral traits in female zebra finches. , 2014, Integrative and comparative biology.

[29]  T. Lehtimäki,et al.  Cardiovascular risk factors in 2011 and secular trends since 2007: The Cardiovascular Risk in Young Finns Study , 2014, Scandinavian journal of public health.

[30]  Pieter B. T. Neerincx,et al.  Supplementary Information Whole-genome sequence variation , population structure and demographic history of the Dutch population , 2022 .

[31]  P. Sullivan,et al.  Heritability and Genomics of Gene Expression in Peripheral Blood , 2014, Nature Genetics.

[32]  R. Andrews,et al.  Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression , 2014, Science.

[33]  Angelo Andriulli,et al.  Loss of Connectivity in Cancer Co-Expression Networks , 2014, PloS one.

[34]  G. Tseng,et al.  Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders , 2014, Genes, brain, and behavior.

[35]  David Haussler,et al.  The UCSC Genome Browser database: 2014 update , 2013, Nucleic Acids Res..

[36]  Leopold Parts,et al.  High-Resolution Mapping of Complex Traits with a Four-Parent Advanced Intercross Yeast Population , 2013, Genetics.

[37]  Ram Krishna Thakur,et al.  Engineered reversal of drug resistance in cancer cells—metastases suppressor factors as change agents , 2013, Nucleic acids research.

[38]  A. Fukushima DiffCorr: an R package to analyze and visualize differential correlations in biological networks. , 2013, Gene.

[39]  Greg Gibson,et al.  Blood-Informative Transcripts Define Nine Common Axes of Peripheral Blood Gene Expression , 2013, PLoS genetics.

[40]  Hongyu Zhao,et al.  Statistical Analysis Reveals Co-Expression Patterns of Many Pairs of Genes in Yeast Are Jointly Regulated by Interacting Loci , 2013, PLoS genetics.

[41]  David Levine,et al.  A high-performance computing toolset for relatedness and principal component analysis of SNP data , 2012, Bioinform..

[42]  A. M. James,et al.  Mitochondrial oxidative stress and the metabolic syndrome , 2012, Trends in Endocrinology & Metabolism.

[43]  R. Payne Cardiovascular risk. , 2012, British journal of clinical pharmacology.

[44]  Lisa E. Gralinski,et al.  The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population , 2012, Genetics.

[45]  Jingyuan Fu,et al.  Trans-eQTLs Reveal That Independent Genetic Variants Associated with a Complex Phenotype Converge on Intermediate Genes, with a Major Role for the HLA , 2011, PLoS genetics.

[46]  Andrey A. Shabalin,et al.  Matrix eQTL: ultra fast eQTL analysis via large matrix operations , 2011, Bioinform..

[47]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[48]  Wei Chen,et al.  Longitudinal Genome-Wide Association of Cardiovascular Disease Risk Factors in the Bogalusa Heart Study , 2010, PLoS genetics.

[49]  D. Pisano,et al.  Mammalian Rap1 controls telomere function and gene expression through binding to telomeric and extratelomeric sites , 2010, Nature Cell Biology.

[50]  A. Fuente,et al.  From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases , 2010 .

[51]  Wieslawa I. Mentzen,et al.  Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor , 2009, BMC Genomics.

[52]  Lucinda K. Southworth,et al.  Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules , 2009, PLoS genetics.

[53]  Reino Laatikainen,et al.  High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. , 2009, The Analyst.

[54]  Antonio Reverter,et al.  A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation , 2009, PLoS Comput. Biol..

[55]  Greg Gibson,et al.  Decanalization and the origin of complex disease , 2009, Nature Reviews Genetics.

[56]  Risto Telama,et al.  Cohort profile: the cardiovascular risk in Young Finns Study. , 2008, International journal of epidemiology.

[57]  P. Cuijpers,et al.  The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods , 2008, International journal of methods in psychiatric research.

[58]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[59]  Bryan R. G. Williams,et al.  Interferon-inducible antiviral effectors , 2008, Nature Reviews Immunology.

[60]  Wei Sun,et al.  Trait-trait dynamic interaction: 2D-trait eQTL mapping for genetic variation study , 2008 .

[61]  Michael Watson,et al.  CoXpress: differential co-expression in gene expression data , 2006, BMC Bioinformatics.

[62]  Weida Tong,et al.  Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor α agonists , 2006, BMC Bioinformatics.

[63]  Chih-Hung Jen,et al.  The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis. , 2006, The Plant journal : for cell and molecular biology.

[64]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .

[65]  R. Reithmeier,et al.  Molecular mechanisms of autosomal dominant and recessive distal renal tubular acidosis caused by SLC4A1 (AE1) mutations , 2005, Journal of molecular and genetic medicine : an international journal of biomedical research.

[66]  Fernando Costa,et al.  Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. , 2005, Circulation.

[67]  Garth A. Gibson,et al.  Canalization in evolutionary genetics: a stabilizing theory? , 2000, BioEssays : news and reviews in molecular, cellular and developmental biology.

[68]  C. Stevens,et al.  Aquaporin 4 and glymphatic flow have central roles in brain fluid homeostasis , 2021, Nature Reviews Neuroscience.

[69]  J H Steiger,et al.  Testing Pattern Hypotheses On Correlation Matrices: Alternative Statistics And Some Empirical Results. , 1980, Multivariate behavioral research.

[70]  Ash A. Alizadeh,et al.  SUPPLEMENTARY NOTE , 1879, Botanical Gazette.

[71]  F. Bitton,et al.  Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels. , 2016, Plant science : an international journal of experimental plant biology.

[72]  Enrico Petretto,et al.  Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits. , 2014, Briefings in functional genomics.

[73]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[74]  N. Gulbahce,et al.  Network medicine: a network-based approach to human disease , 2010, Nature Reviews Genetics.

[75]  Ira M. Hall,et al.  BEDTools: a flexible suite of utilities for comparing genomic features , 2010, Bioinform..

[76]  P. Bühlmann,et al.  Survival ensembles. , 2006, Biostatistics.

[77]  Rainer Spang,et al.  Finding disease specific alterations in the co-expression of genes , 2004, ISMB/ECCB.

[78]  B Marshall,et al.  Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource , 2004, Nucleic Acids Res..

[79]  S. Alper Diseases of mutations in the SLC4A1/AE1 (band 3) Cl − /HCO 3 − exchanger , 2003 .

[80]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .