Genetics of gene expression and its effect on disease

Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.

[1]  C. Amos.  Robust variance-components approach for assessing genetic linkage in pedigrees. , 1994, American journal of human genetics.

[2]  R. Doerge,et al.  Empirical threshold values for quantitative trait mapping. , 1994, Genetics.

[3]  N J Cox,et al.  Allele-sharing models: LOD scores and accurate linkage tests. , 1997, American journal of human genetics.

[4]  L. Almasy,et al.  Multipoint quantitative-trait linkage analysis in general pedigrees. , 1998, American journal of human genetics.

[5]  L R Goldin,et al.  Optimal ascertainment strategies to detect linkage to common disease alleles. , 1998, American journal of human genetics.

[6]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[7]  Daniel F. Gudbjartsson,et al.  Allegro, a new computer program for multipoint linkage analysis , 2000, Nature genetics.

[8]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[9]  Kári Stefánsson,et al.  Protection of privacy by third-party encryption in genetic research in Iceland , 2000, European Journal of Human Genetics.

[10]  D. Lockhart,et al.  Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Stoughton,et al.  Experimental annotation of the human genome using microarray technology , 2001, Nature.

[12]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.

[13]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[14]  D. Gudbjartsson,et al.  A high-resolution recombination map of the human genome , 2002, Nature Genetics.

[15]  Ash A. Alizadeh,et al.  Individuality and variation in gene expression patterns in human blood , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[17]  Robert Tibshirani,et al.  Statistical methods for identifying differentially expressed genes in DNA microarrays. , 2003, Methods in molecular biology.

[18]  K. Wellen,et al.  Obesity-induced inflammatory changes in adipose tissue. , 2003, The Journal of clinical investigation.

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

[20]  J. Castle,et al.  Genome-Wide Survey of Human Alternative Pre-mRNA Splicing with Exon Junction Microarrays , 2003, Science.

[21]  C. Molony,et al.  Genetic analysis of genome-wide variation in human gene expression , 2004, Nature.

[22]  E. Schadt,et al.  Genetic inheritance of gene expression in human cell lines. , 2004, American journal of human genetics.

[23]  J. Zhu,et al.  An integrative genomics approach to the reconstruction of gene networks in segregating populations , 2004, Cytogenetic and Genome Research.

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

[25]  Andrew I Su,et al.  Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics' , 2005, Nature Genetics.

[26]  C. Peterson,et al.  Expression of CD68 and macrophage chemoattractant protein-1 genes in human adipose and muscle tissues: association with cytokine expression, insulin resistance, and reduction by pioglitazone. , 2005, Diabetes.

[27]  Robert W. Williams,et al.  Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function , 2005, Nature Genetics.

[28]  E E Schadt,et al.  Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits , 2005, Nature Genetics.

[29]  Joshua T. Burdick,et al.  Mapping determinants of human gene expression by regional and genome-wide association , 2005, Nature.

[30]  J. Castle,et al.  An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.

[31]  S. Friend,et al.  Embracing Complexity, Inching Closer to Reality , 2005, Science's STKE.

[32]  K. Gunderson,et al.  Illumina, Inc. , 2005, Pharmacogenomics.

[33]  Damian Smedley,et al.  Ensembl 2005 , 2004, Nucleic Acids Res..

[34]  Rachel B. Brem,et al.  The landscape of genetic complexity across 5,700 gene expression traits in yeast. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[35]  John D. Storey,et al.  Genetic interactions between polymorphisms that affect gene expression in yeast , 2005, Nature.

[36]  J. Olefsky,et al.  Inflamed fat: what starts the fire? , 2005, The Journal of clinical investigation.

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

[38]  J. Lamb,et al.  Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes , 2006, Journal of neurochemistry.

[39]  Ravi Kiran Reddy Kalathur,et al.  Expression profiling of genes regulated by TGF-beta: Differential regulation in normal and tumour cells , 2007, BMC Genomics.

[40]  A. Saltiel,et al.  Obesity induces a phenotypic switch in adipose tissue macrophage polarization. , 2007, The Journal of clinical investigation.

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