Ann Gibbons reports [1] from the International Congress of Human Genetics, on papers that examine GWAS risk alleles for type 2 diabetes: "Diabetes Genes Decline Out of Africa" (paywall).
At the poster session, Stanford graduate student Erik Corona stood in front of a Google Earth map of the world that he finds surprising. On this map he had plotted the frequency of 12 gene variants known to be associated with type 2 diabetes in 51 populations from Australia to Zaire. It shows “a clear gradient of red to green from west to east, from Africa to Asia,” Corona says (see map). “Something strange is going on with type 2 diabetes.”
This is of course a challenging problem because risk alleles identified in one population may not replicate in other populations. The most well-known example is ApoE4, strongly associated with Alzheimer's Disease in Europeans, but not in Africans. More generally, looking at a set of risk variants that are identified in one population introduces an ascertainment bias that constrains their likely frequencies in other populations. An allele is more likely to yield a statistically significant association with a trait if the allele is not too rare. If we take many alleles associated with a trait, we're likely to see some gradient across populations due to this bias alone.
Hidden ascertainment bias is a problem we run up against quite a lot. It may not apply in this case, depending on where the risk alleles were identified, in particular since many risk alleles for type 2 diabetes appear to be linked to recent positive selection (explaining why I got interested).
