Variations in DNA elucidate molecular networks that cause disease - Chen, Yanqing; Zhu, Jun; Lum, Pek Yee; Yang, Xia; Pinto, Shirly; MacNeil, Douglas J.; Zhang, Chunsheng; Lamb, John; Edwards, Stephen; Sieberts, Solveig K.; Leonardson, Amy; Castellini, Lawrence W.; et al. (Nature, 2008)
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.