Variations in DNA elucidate molecular networks that cause disease
Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease...
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| Published in | Nature Vol. 452; no. 7186; pp. 429 - 435 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
27.03.2008
Nature Publishing Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0028-0836 1476-4687 1476-4687 1476-4679 |
| DOI | 10.1038/nature06757 |
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| Abstract | Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (
Lpl
), lactamase β (
Lactb
) and protein phosphatase 1-like (
Ppm1l
), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.
Obesity gets complicated
Complex human diseases result from the interplay of many genetic and environmental factors. To build up a picture of the factors contributing to one such disease, obesity, gene expression was evaluated as a quantitative trait in blood and adipose tissue samples from hundreds of Icelandic subjects aged 18 to 85. The results reveal a tendency to certain characteristic patterns of gene activation in the fatty tissues — though to a much lesser extent in the blood — of people with a higher body mass index. A transcriptional network constructed from the adipose tissue data has significant overlap with a network based on mouse adipose tissue data. Experimental support for the idea that complex diseases are emergent properties of molecular networks influenced by genes and environment comes from a study in mice. Mice were examined for disturbances in genetic expression networks that correlate with metabolic traits associated with obesity, diabetes and atherosclerosis. Three genes —
Lpl
,
Lactb
and
Ppm1l
— were identified as previously unknown obesity genes. This 'molecular network' approach raises the prospect that therapies might be directed at whole 'disease networks', rather than at one or two specific genes.
Standard approaches to identify the genetic changes that lead to disease are reversed by examination of genetic networks for perturbations that are associated with disease states, and following up candidate genes from there. This begins with three genes in mice that lead to obesity when mutated, demonstrating that complex genetic–environmental traits can be dissected with this new approach. |
|---|---|
| AbstractList | Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase ( Lpl ), lactamase β ( Lactb ) and protein phosphatase 1-like ( Ppm1l ), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. Obesity gets complicated Complex human diseases result from the interplay of many genetic and environmental factors. To build up a picture of the factors contributing to one such disease, obesity, gene expression was evaluated as a quantitative trait in blood and adipose tissue samples from hundreds of Icelandic subjects aged 18 to 85. The results reveal a tendency to certain characteristic patterns of gene activation in the fatty tissues — though to a much lesser extent in the blood — of people with a higher body mass index. A transcriptional network constructed from the adipose tissue data has significant overlap with a network based on mouse adipose tissue data. Experimental support for the idea that complex diseases are emergent properties of molecular networks influenced by genes and environment comes from a study in mice. Mice were examined for disturbances in genetic expression networks that correlate with metabolic traits associated with obesity, diabetes and atherosclerosis. Three genes — Lpl , Lactb and Ppm1l — were identified as previously unknown obesity genes. This 'molecular network' approach raises the prospect that therapies might be directed at whole 'disease networks', rather than at one or two specific genes. Standard approaches to identify the genetic changes that lead to disease are reversed by examination of genetic networks for perturbations that are associated with disease states, and following up candidate genes from there. This begins with three genes in mice that lead to obesity when mutated, demonstrating that complex genetic–environmental traits can be dissected with this new approach. Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase b (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. 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. [PUBLICATION ABSTRACT] Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase ( Lpl ), lactamase β ( Lactb ) and protein phosphatase 1-like ( Ppm1l ), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. |
| Audience | Academic |
| Author | Pinto, Shirly Emilsson, Valur Zhang, Chunsheng Wang, Susanna Lum, Pek Yee MacNeil, Douglas J. Leonardson, Amy Horvath, Steve Drake, Thomas A. Castellini, Lawrence W. Chen, Yanqing Edwards, Stephen Sieberts, Solveig K. Lamb, John Schadt, Eric E. Yang, Xia Doss, Sudheer Lusis, Aldons J. Champy, Marie-France Zhang, Bin Zhu, Jun Ghazalpour, Anatole |
| AuthorAffiliation | 4 Department of Human Genetics, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA 5 Department of Pathology and Laboratory Medicine, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA 1 Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA 6 Institut de Genetique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/ULP, 67404 Illkirch, France 3 Department of Microbiology, Molecular Genetics, and Immunology, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA 2 Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA |
| AuthorAffiliation_xml | – name: 2 Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA – name: 6 Institut de Genetique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/ULP, 67404 Illkirch, France – name: 1 Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – name: 3 Department of Microbiology, Molecular Genetics, and Immunology, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA – name: 4 Department of Human Genetics, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA – name: 5 Department of Pathology and Laboratory Medicine, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA |
| Author_xml | – sequence: 1 givenname: Yanqing surname: Chen fullname: Chen, Yanqing organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 2 givenname: Jun surname: Zhu fullname: Zhu, Jun organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 3 givenname: Pek Yee surname: Lum fullname: Lum, Pek Yee organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 4 givenname: Xia surname: Yang fullname: Yang, Xia organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 5 givenname: Shirly surname: Pinto fullname: Pinto, Shirly organization: Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA – sequence: 6 givenname: Douglas J. surname: MacNeil fullname: MacNeil, Douglas J. organization: Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA – sequence: 7 givenname: Chunsheng surname: Zhang fullname: Zhang, Chunsheng organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 8 givenname: John surname: Lamb fullname: Lamb, John organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 9 givenname: Stephen surname: Edwards fullname: Edwards, Stephen organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 10 givenname: Solveig K. surname: Sieberts fullname: Sieberts, Solveig K. organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 11 givenname: Amy surname: Leonardson fullname: Leonardson, Amy organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 12 givenname: Lawrence W. surname: Castellini fullname: Castellini, Lawrence W. organization: Department of Microbiology, Molecular Genetics, and Immunology – sequence: 13 givenname: Susanna surname: Wang fullname: Wang, Susanna organization: Department of Microbiology, Molecular Genetics, and Immunology – sequence: 14 givenname: Marie-France surname: Champy fullname: Champy, Marie-France organization: Institut de Genetique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/ULP, 67404 Illkirch, France – sequence: 15 givenname: Bin surname: Zhang fullname: Zhang, Bin organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 16 givenname: Valur surname: Emilsson fullname: Emilsson, Valur organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA – sequence: 17 givenname: Sudheer surname: Doss fullname: Doss, Sudheer organization: Department of Microbiology, Molecular Genetics, and Immunology – sequence: 18 givenname: Anatole surname: Ghazalpour fullname: Ghazalpour, Anatole organization: Department of Microbiology, Molecular Genetics, and Immunology – sequence: 19 givenname: Steve surname: Horvath fullname: Horvath, Steve organization: Department of Human Genetics, and – sequence: 20 givenname: Thomas A. surname: Drake fullname: Drake, Thomas A. organization: Department of Pathology and Laboratory Medicine, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA – sequence: 21 givenname: Aldons J. surname: Lusis fullname: Lusis, Aldons J. organization: Department of Microbiology, Molecular Genetics, and Immunology, Department of Human Genetics, and – sequence: 22 givenname: Eric E. surname: Schadt fullname: Schadt, Eric E. email: eric_schadt@merck.com organization: Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA |
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| Snippet | Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However,... Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel... |
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| SubjectTerms | Adipose Tissue Adipose Tissue - metabolism Animals Apolipoprotein A-II Apolipoprotein A-II - genetics Biochemistry, Molecular Biology Biological and medical sciences Biomedical research Blood Chromosomes, Mammalian Chromosomes, Mammalian - genetics Deoxyribonucleic acid Disease susceptibility DNA Environmental factors Female Gene expression Gene Regulatory Networks Gene Regulatory Networks - genetics Genetic aspects Genetic Predisposition to Disease Genetic Predisposition to Disease - genetics Genetic variation Genetic Variation - genetics Genetics Genomics Humanities and Social Sciences Immune response Integrated approach Life Sciences Linkage Disequilibrium Lipoprotein Lipase Lipoprotein Lipase - genetics Liver Liver - metabolism Lod Score Macrophages Macrophages - metabolism Male Medical sciences Membrane Proteins Membrane Proteins - genetics Metabolic diseases Metabolic disorders Metabolic Syndrome - enzymology Metabolic Syndrome - genetics Metabolic Syndrome - metabolism Metabolic Syndrome X Mice Miscellaneous Molecular biology Molecular neurobiology multidisciplinary Multifactorial traits Obesity Obesity - enzymology Obesity - genetics Obesity - metabolism Other metabolic disorders Phenotype Phosphoprotein Phosphatases Phosphoprotein Phosphatases - deficiency Phosphoprotein Phosphatases - genetics Phosphoprotein Phosphatases - metabolism Quantitative Trait Loci Reproducibility of Results Ribosomal Proteins Ribosomal Proteins - genetics Science Science (multidisciplinary) Variation (Genetics) |
| Title | Variations in DNA elucidate molecular networks that cause disease |
| URI | https://link.springer.com/article/10.1038/nature06757 https://www.ncbi.nlm.nih.gov/pubmed/18344982 https://www.proquest.com/docview/204553891 https://www.proquest.com/docview/1776644921 https://www.proquest.com/docview/19749441 https://www.proquest.com/docview/70437380 https://hal.science/hal-00283069 https://pubmed.ncbi.nlm.nih.gov/PMC2841398 |
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