CNV and HIV susceptibility

Copy number at CCL3L1 is one of the genetic predictors of HIV susceptibility in humans (Gonzalez et al), with individuals with copy number higher than the population median being less susceptible. A new paper at PLoS Genetics shows that the gene is also copy number variable in rhesus macaque, and that this variation is also responsible for differences between individuals in susceptibility (to SIV, the simian form of HIV) in macaques. One of the interesting things about this story is that while differences between populations in average copy number may be predictive of the average population susceptibility in macaques (Indian macaques have on average lower copy number and are more susceptible than Chinese Macaques) this does not appear to be the case in humans, despite a relatively large difference in average copy number between human populations.

The other nice thing about the study is that it points to CNVs as a source of 'repeatable' phenotypic variation across species, the high rate of polymorphism at CNVs means that a CNV associated with a phenotype in one species may often be polymorphic and associated with similar phenotypic variation in other species. As more associations between CNVs and phenotypes are discovered this may allow them to be relatively easily followed up across a range of species (like for example amino-acid mutations in MC1R).

references
Degenhardt et al. Copy Number Variation of CCL3-like Genes Affects Rate of Progression to Simian-AIDS in Rhesus Macaques (Macaca mulatta) PLoS Genetics 2009
Gonzalez et al. The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 2005

A gut feeling about history

A interesting article in Science on using the (mainly vertically transmitted) human pathogen bacteria Helicobacter pylori to reconstruct the peopling of the Pacific. Which I guess is a finer-scale followup to a previous paper doing this for worldwide populations.

Chakravarti on race and kinship

There's an interesting opinion piece at Nature by Aravinda Chakravarti. The part on populations and race covers some pretty familiar ground, but the article is obviously aimed at a more general audience. I'm not really sure what the "population view dominates in genetics" means. I think geneticists use population labels as handy identifiers, which can sometimes lead to some confusing/misleading statements, but I think the majority think that most genetic variation is relatively smoothly distributed (though perhaps I know a biased subset of geneticists). The debate rather is in what forces that have shaped this smoothness and how to interpret deviations.

R Rocks

I use R pretty much constantly, so I was pleased to see it getting some attention in the NYT (see also Mailund, Hawks, and Dechronization). There is also a followup post by the article's author here.

One of the best things about R is that it is freely available, this is a huge boon to any kind of statistical research as most other statistical packages are prohibitively expensive putting them out of the reach of many researchers. Because of this the R community is really great. I often find that the problems I come across when coding have already been solved and code is available freely online.

The idea expressed in the article by the SAS rep. that some how propriety software is safer than open source is laughable (and really just makes them look silly). I would far rather work with something that thousands of statisticians spent every day picking apart the code, than a blackbox from some corporation that has a strong interest in saving face.