Wed, 20 Aug 2008 21:16:26 BST CiteULike: lp2's Sieberts http://www.citeulike.org/user/lp2/author/Sieberts CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/lp2/article/2760213 <i>PLoS Biology, Vol. 6, No. 5. (1 May 2008), e107.</i><br /><br />Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. Mapping the Genetic Architecture of Gene Expression in Human Liver Eric Schadt Cliona Molony Eugene Chudin Ke Hao Xia Yang Pek Lum Andrew Kasarskis Bin Zhang Susanna Wang Christine Suver Jun Zhu Joshua Millstein Solveig Sieberts John Lamb Debraj Guhathakurta Jonathan Derry John Storey Iliana Avila-Campillo Mark Kruger Jason Johnson Carol Rohl Atila van Nas Margarete Mehrabian Thomas Drake Aldons Lusis Ryan Smith Peter Guengerich Stephen Strom Erin Schuetz Thomas Rushmore Roger Ulrich doi:10.1371%2Fjournal.pbio.0060107 PLoS Biology, Vol. 6, No. 5. (1 May 2008), e107. 2008-05-06T08:52:37-00:00 2008 PLoS Biology 6 5 e107 expression networks http://www.citeulike.org/user/lp2/article/1509893 <i>Mamm Genome (26 July 2007)</i><br /><br />Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone. Moving toward a system genetics view of disease. Solveig Sieberts Eric Schadt doi:10.1007/s00335-007-9040-6 Mamm Genome (26 July 2007) 2007-07-28T10:02:22-00:00 2007 Mamm Genome 0938-8990 association disease http://www.citeulike.org/user/lp2/article/2548279 <i>Nature (16 March 2008)</i> Variations in DNA elucidate molecular networks that cause disease Yanqing Chen Jun Zhu Pek Lum Xia Yang Shirly Pinto Douglas Macneil Chunsheng Zhang John Lamb Stephen Edwards Solveig Sieberts Amy Leonardson Lawrence Castellini Susanna Wang Marie-France Champy Bin Zhang Valur Emilsson Sudheer Doss Anatole Ghazalpour Steve Horvath Thomas Drake Aldons Lusis Eric Schadt doi:10.1038/nature06757 Nature (16 March 2008) 2008-03-18T04:25:40-00:00 2008 Nature 0028-0836 Nature Publishing Group expression genetics variation http://www.citeulike.org/user/lp2/article/241639 <i>Nature Genetics, Vol. 37, No. 7. (19 June 2005), pp. 710-717.</i> An integrative genomics approach to infer causal associations between gene expression and disease Eric Schadt John Lamb Xia Yang Jun Zhu Steve Edwards Debraj Guhathakurta Solveig Sieberts Stephanie Monks Marc Reitman Chunsheng Zhang Pek Lum Amy Leonardson Rolf Thieringer Joseph Metzger Liming Yang John Castle Haoyuan Zhu Shera Kash Thomas Drake Alan Sachs Aldons Lusis doi:10.1038/ng1589 Nature Genetics, Vol. 37, No. 7. (19 June 2005), pp. 710-717. 2005-07-01T19:25:52-00:00 2005 Nature Genetics 1061-4036 37 7 710 717 Nature Publishing Group association expression