Complex gene networks for obesity and metabolic disease risk
Analysis of a study published in a science journal | By Dr Gurdeep Sagoo | Published 23 May 2011
Study: Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes.
By: Small K.S. et al. (20 authors total)
In: Nature Genetics
What this study set out to do:
The study authors looked at whether a genetic variant (SNP rs4731702) in the KLF14 gene, associated with both type 2 diabetes and cholesterol levels, alters the expression levels of other genes.
How they went about it:
The researchers looked for associations between rs4731702 and the expression levels of over 16,000 genes in fat samples from a cohort of 776 healthy female twins of European descent (from the multiple tissue human expression resource study). Ten genes were then followed up in an additional 589 fat samples from the Icelandic deCODE study, along with further functional lab-based work.
The authors found that rs4731702 was associated with gene expression levels in fat tissue for many genes. Of these they then focused on the expression levels of ten genes (TPMT, ARSD, SLC7A10, C8orf82, APH1B, PRMT2, NINJ2, KLF13, GNB1 and MYL5) on various metabolic phenotypes (including BMI and levels of HDL cholesterol, trigylcerides, fasting insulin and HOMA-IR). Expression levels of six of these genes were in turn associated with BMI and HDL cholesterol, five with triglycerides and fasting insulin levels, and four with HOMA-IR (an index measure of insulin sensitivity).
The study authors concluded that their data provided convincing evidence of the involvement of KLF14 on risk of metabolic disease. KLF14 is associated with HDL cholesterol and type 2 diabetes and exerts regulatory influence on other genes that influence metabolic disease risk such as APH1B (associated with HDL and triglycerides), MSRA (associated with waist circumference and triglycerides) and several other genes (associated cholesterol, BMI and triglycerides).
This work not only provides a hypothesis-driven framework for further investigative work but also highlights the complexity that underlies common complex diseases such as type 2 diabetes and obesity. This study shows how a single gene may influence disease risk both through its own actions and by regulating the actions of other genes, which in turn also influence disease risk. The next step will be to identify how these genes work in obese people and how this can be used to develop treatments.
PHG Foundation | Complex gene networks for obesity and metabolic disease risk