排序方式: 共有4条查询结果,搜索用时 15 毫秒
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Emilsson V Thorleifsson G Zhang B Leonardson AS Zink F Zhu J Carlson S Helgason A Walters GB Gunnarsdottir S Mouy M Steinthorsdottir V Eiriksdottir GH Bjornsdottir G Reynisdottir I Gudbjartsson D Helgadottir A Jonasdottir A Jonasdottir A Styrkarsdottir U Gretarsdottir S Magnusson KP Stefansson H Fossdal R Kristjansson K Gislason HG Stefansson T Leifsson BG Thorsteinsdottir U Lamb JR Gulcher JR Reitman ML Kong A Schadt EE Stefansson K 《Nature》2008,452(7186):423-428
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An integrative genomics approach to infer causal associations between gene expression and disease 总被引:2,自引:0,他引:2
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Experimental annotation of the human genome using microarray technology 总被引:59,自引:0,他引:59
Shoemaker DD Schadt EE Armour CD He YD Garrett-Engele P McDonagh PD Loerch PM Leonardson A Lum PY Cavet G Wu LF Altschuler SJ Edwards S King J Tsang JS Schimmack G Schelter JM Koch J Ziman M Marton MJ Li B Cundiff P Ward T Castle J Krolewski M Meyer MR Mao M Burchard J Kidd MJ Dai H Phillips JW Linsley PS Stoughton R Scherer S Boguski MS 《Nature》2001,409(6822):922-927
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Chen Y Zhu J Lum PY Yang X Pinto S MacNeil DJ Zhang C Lamb J Edwards S Sieberts SK Leonardson A Castellini LW Wang S Champy MF Zhang B Emilsson V Doss S Ghazalpour A Horvath S Drake TA Lusis AJ Schadt EE 《Nature》2008,452(7186):429-435
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. 相似文献
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