A scalable permutation approach reveals replication and preservation patterns of gene coexpression modules

Gene coexpression network modules provide a framework for identifying shared biological functions. Analysis of topological preservation of modules across datasets is important for assessing reproducibility, and can reveal common function between tissues, cell types, and species. Although module preservation statistics have been developed, heuristics have been required for significance testing. However, the scale of current and future analyses requires accurate and unbiased p-values, particularly to address the challenge of multiple testing. Here, we developed a rapid and efficient approach (NetRep) for assessing module preservation and show that module preservation statistics are typically non-normal, necessitating a permutation approach. Quantification of module preservation across brain, liver, adipose, and muscle tissues in a BxH mouse cross revealed complex patterns of multi-tissue preservation with 52% of modules showing unambiguous preservation in one or more tissues and 25% showing preservation in all four tissues. Phenotype association analysis uncovered a liver-derived gene module which harboured housekeeping genes and which also displayed adipose and muscle tissue specific association with body weight. Taken together, our study presents a rapid unbiased approach for testing preservation of gene network topology, thus enabling rigorous assessment of potentially conserved function and phenotype association analysis.

[1]  Liam G. Fearnley,et al.  The Biomarker GlycA Is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection. , 2015, Cell systems.

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

[3]  Dmitri D. Pervouchine,et al.  The human transcriptome across tissues and individuals , 2015, Science.

[4]  N. Hacohen,et al.  ImmVar project: Insights and design considerations for future studies of "healthy" immune variation. , 2015, Seminars in immunology.

[5]  Daphne Koller,et al.  Sharing and Specificity of Co-expression Networks across 35 Human Tissues , 2014, PLoS Comput. Biol..

[6]  Pablo Villoslada,et al.  Modules, networks and systems medicine for understanding disease and aiding diagnosis , 2014, Genome Medicine.

[7]  Peter J. Bickel,et al.  Comparative analysis of regulatory information and circuits across distant species , 2014, Nature.

[8]  Peter J. Bickel,et al.  Comparative Analysis of the Transcriptome across Distant Species , 2014, Nature.

[9]  M. Inouye,et al.  Towards a Molecular Systems Model of Coronary Artery Disease , 2014, bioRxiv.

[10]  Joonsoo Kang,et al.  Immunological Genome Project and systems immunology. , 2013, Trends in immunology.

[11]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[12]  L. Tran,et al.  Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease , 2013, Cell.

[13]  Peter Langfelder,et al.  When Is Hub Gene Selection Better than Standard Meta-Analysis? , 2013, PloS one.

[14]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

[15]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[16]  M. Porter,et al.  Critical Truths About Power Laws , 2012, Science.

[17]  Rui Luo,et al.  Is My Network Module Preserved and Reproducible? , 2011, PLoS Comput. Biol..

[18]  Markus Perola,et al.  Metabonomic, transcriptomic, and genomic variation of a population cohort , 2010, Molecular systems biology.

[19]  G. Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Permutation P -values Should Never Be Zero: Calculating Exact P -values When Permutations Are Randomly Drawn , 2011 .

[20]  Peter Langfelder,et al.  Is human blood a good surrogate for brain tissue in transcriptional studies? , 2010, BMC Genomics.

[21]  Markus Perola,et al.  An Immune Response Network Associated with Blood Lipid Levels , 2010, PLoS genetics.

[22]  S. Horvath,et al.  Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways , 2010, Proceedings of the National Academy of Sciences.

[23]  Daniel Marbach,et al.  Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PloS one.

[24]  A. Lusis,et al.  Cardiovascular networks: systems-based approaches to cardiovascular disease. , 2010, Circulation.

[25]  E. Schadt Molecular networks as sensors and drivers of common human diseases , 2009, Nature.

[26]  Ben Lehner,et al.  Tissue specificity and the human protein interaction network , 2009, Molecular systems biology.

[27]  Eric E Schadt,et al.  Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. , 2009, Endocrinology.

[28]  Masato Kasuga,et al.  [Obesity and insulin resistance]. , 2009, Nihon rinsho. Japanese journal of clinical medicine.

[29]  S. Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[30]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

[31]  Eric E Schadt,et al.  Cycle Regulation in Islets with Diabetes Susceptibility a Gene Expression Network Model of Type 2 Diabetes Links Cell P

, 2008 .

[32]  John D. Storey,et al.  Mapping the Genetic Architecture of Gene Expression in Human Liver , 2008, PLoS biology.

[33]  H. Stefánsson,et al.  Genetics of gene expression and its effect on disease , 2008, Nature.

[34]  S. Horvath,et al.  Variations in DNA elucidate molecular networks that cause disease , 2008, Nature.

[35]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[36]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[37]  E. Schadt,et al.  Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics , 2007, Proceedings of the National Academy of Sciences.

[38]  S. Kahn,et al.  Mechanisms linking obesity to insulin resistance and type 2 diabetes , 2006, Nature.

[39]  J. Han,et al.  Identification of the Proliferation/Differentiation Switch in the Cellular Network of Multicellular Organisms , 2006, PLoS Comput. Biol..

[40]  A. Arnold,et al.  Tissue-specific expression and regulation of sexually dimorphic genes in mice. , 2006, Genome research.

[41]  S. Horvath,et al.  Evidence for anti-Burkitt tumour globulins in Burkitt tumour patients and healthy individuals. , 1967, British Journal of Cancer.

[42]  R. Kitazawa,et al.  MCP-1 contributes to macrophage infiltration into adipose tissue, insulin resistance, and hepatic steatosis in obesity. , 2006, The Journal of clinical investigation.

[43]  S. Horvath,et al.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks , 2006, BMC Genomics.

[44]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[45]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[46]  Stephen W. Edwards,et al.  Microarray Standard Data Set and Figures of Merit for Comparing Data Processing Methods and Experiment Designs , 2003, Bioinform..

[47]  D. Loskutoff,et al.  Monocyte chemoattractant protein 1 in obesity and insulin resistance , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[48]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[49]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[50]  S. Lange,et al.  Adjusting for multiple testing--when and how? , 2001, Journal of clinical epidemiology.

[51]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[52]  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.

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

[54]  A. Lusis,et al.  Dissection of multigenic obesity traits in congenic mouse strains , 2003, Mammalian Genome.

[55]  A. Barabasi,et al.  Emergence of Scaling in Random Networks , 1999 .

[56]  B. Spiegelman,et al.  Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. , 1993, Science.

[57]  Supplemental Information 2: Kyoto Encyclopedia of genes and genomes. , 2022 .