NEW YORK (GenomeWeb News) – New obesity research is revealing the power of a network-based approach to finding disease genes and drug targets.
Whereas most genome-wide association studies link single gene mutations with disease, two new international studies involving researchers at DeCode Genetics, Merck’s Rosetta Inpharmatics, and several other research centers highlight the role of gene networks in disease. The studies, both published online in Nature yesterday, pinpoint networks of genes whose expression changes during obesity in mice or humans.
“These studies strongly support the theory that common diseases such as obesity result from genetic and environmental disturbances in entire networks of genes rather than in a handful of genes,” Eric Schadt, Merck Research Laboratories’ scientific executive director of genetics and a senior author of both papers, said in a statement.
For the first study, led by Schadt and DeCode CEO Kari Stefansson, researchers looked at the expression of 23,720 genes in blood and fat tissues collected from an Icelandic population, aged 18 to 85 years old. The so-called Icelandic Family Blood cohort contained samples from 1,002 subjects in 209 families, while the Icelandic Family Adipose cohort contained samples from 673 subjects in 124 families.
They also collected data on individuals’ differential blood cell count, body mass index, percentage body weight, and waist-to-hip ratio. These biometrics were reportedly similar to those in a typical Western population. After adjusting for age and sex, the researchers were able to pinpoint groups of genes whose expression varied with specific biometrics.
Specifically, the expression of 2,172 blood genes correlated with body mass index. In adipose tissue, a whopping 17,080 genes correlated with BMI, while 16,977 were associated with changes in percentage body fat and 14,901 were associated with waist-to-hip ratio measurements. Depending on the type of analysis used, it appears that as many as 58.6 percent and 70.9 percent of blood and adipose transcripts assessed are heritable. These dipped slightly when adjusted for age, sex, and cell-count or BMI.
The team also did linkage analysis of genes near expression trait genes, genotyping blood and adipose tissues based on 1,732 microsatellites. This revealed thousands more cis-acting expression quantitative trait loci in blood and fat tissue. Thousands more obesity-related gene variants fell out of a whole-genome genotyping experiment assessing 317,503 SNPs in 150 unrelated individuals using instrumentation from Illumina. About half of these overlapped in both tissues.
Finally, Schadt, Stefansson, and colleagues created sex-specific gene co-expression transcriptional networks for genes associated with obesity in adipose tissue. This revealed a core group of genes, including several related to energy homeostasis and immune response, particularly related to macrophages, defense cells that engulf and destroy foreign or defective cells.
These networks also overlapped with a network of mouse genes — described in the other paper — that appear to link obesity with inflammation and immune response.
For that paper, researchers looked at gene expression and quantitative trait loci in liver and adipose tissues from 334 mice with various obesity, diabetes, and atherosclerosis-like traits using an Agilent custom murine gene expression microarray to genotype more the 1,300 SNPs. In particular, they focused on a region on chromosome one that was previously linked to metabolic traits. Again, a sub-network containing macrophage-related genes tended to associated with altered metabolism and obesity.
“Unlike classic genetics approaches that aim to identify genes underlying genetic loci associated with disease, the approach developed here seeks to identify whole gene networks that respond in trans to genetic loci driving disease, and that in turn lead to variations in the disease traits,” the authors wrote. “Our results demonstrate that there may in fact be thousands of genes capable of increasing the susceptibility to metabolic disease traits such as obesity, diabetes and atherosclerosis.”
This genetic approach not only reveals the interconnected nature of disease-related genes, researchers say, it may provide a new avenue for therapeutic development. “If diseases like obesity are the result of complex networks of genes, the accurate reconstruction of these networks will be critical to identifying the best therapeutic targets,” Schadt said.