BOX 19 RISK MAPPING: A NEW TOOL TO ASSESS FOOD SECURITY The Save the Children Fund (SCF) is working in conjunction with FAO on a project supported by the EU to design a computer-assisted method for more reliable famine early warning.
But top of the vulnerability list put together by the risk-mapping firm Maplecroft was the Comoros islands, off the eastern coast of Africa.
Such an approach would create a dynamic risk-mapping tool that can be used to generate public health notices to filter information back to community users.
For the vector-borne Rift Valley fever virus, the risk-mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007 (29).
Although the risk-mapping model has been designed for famine early warning and is potentially a very useful tool to that end, it seems probable that the approach will have much wider applicability; the effects of natural disasters, such as drought or flooding, and of disasters caused by humans, such as war, are susceptible to this type of analysis, as are the "normal" patterns of food insecurity.
In complex trait genetics, the two most well-known applications of multi-ethnic GWA studies are meta-analyses, combining the association signals in different populations to increase the power for detecting new risk loci, and fine-mapping, using the divergent genomic structure between different populations for finding the causal genetic variant in a shared risk locus.
Understanding how coding and non-coding variants together can affect disease risk through such fine-mapping and resequencing efforts is arguably the most challenging and exciting area for genomic medicine today, because it offers opportunities for drug discovery or repositioning (by targeting specific mutations, signaling receptors or biological pathways).
We then discuss the interpretation of the means of the posterior distribution of the relative risk estimates typically reported in disease-mapping studies and displayed as summary maps, and we illustrate quantitatively how much smoothing is performed.
Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations.
However, a recent multi-ethnic analysis of prostate cancer risk SNPs found that even after fine-mapping, most risk-associated SNPs are located in non-coding regions [ 21].
