In This Issue
Human Variation 2.0: Using GWAS to Probe Intermediate Phenotypes
Article first published online: 19 FEB 2013
© 2013 Wiley Periodicals, Inc.
Volume 34, Issue 3, page iv, March 2013
How to Cite
Chanock, S. J. (2013), Human Variation 2.0: Using GWAS to Probe Intermediate Phenotypes. Hum. Mutat., 34: iv. doi: 10.1002/humu.22181
- Issue published online: 18 FEB 2013
- Article first published online: 19 FEB 2013
In this issue, Hong et al. (Hum Mutat 34:515–524. 2013) have published an intriguing paper entitled, “A Genome-wide Assessment of Variability in Human Serum Metabolism”. This study was conducted as a secondary analysis of a genome-wide scan of prostate cancer in Swedish men and the findings provide new insights into dynamic intermediates of serum metabolism. Their work has yielded a treasure trove of loci associated with intermediate traits, such as measured levels of cholesterol or lipids.
Though metabolism is hard to quantify, the authors have utilized the quantitative trait loci (QTL) approach, but are using a global non-targeted metabolite profile to test “metabolic QTLs”. They advanced the most promising findings from their preliminary study and validated seven distinct loci with small p values (less than 10−13) in an independent data set, suggesting that these loci could represent intermediate metabolic markers. Further biological work is needed to confirm the genetic observations, based on stable SNP markers.
This approach represents a logical next step in using genetic association studies to probe intermediate outcomes, ones that could begin to explain the molecular basis of associations observed in complex diseases. Furthermore, it addresses, albeit in a preliminary manner, the challenge of the timing of events. This is difficult to investigate in most complex diseases, as the phenotypes are frequently defined as binary outcomes (cases vs. disease-free controls) and do not reflect the different pathways that lead to a complex disease. The fruits of GWAS have generally yielded loci with small estimated odds effects, underscoring the complex nature of many loci contributing to a disease, and not all doing so in a synchronized manner. Through a clever application of the data analysis, Hong et al. have pushed the limits and provided new insights. It should be interesting to see how this approach unfolds across a spectrum of cohorts as well as in laboratory investigation of the regions herein reported.