Mailund on the Internet

Now that I am getting into the habit of stealing titles, I've done it again. This time from this post that ask that very question.

I am a computer scientist, and I don't know the answer. Having been educated in a very theoretical computer science department, I would probably group computer science with math (and I do not consider math a science, but it isn't engineering either). It is not as simple as that, though. Computer science gets its hands in all kinds of things and not always with pure (math) intentions, so who knows what it really is...

I look forward to the rest of the series.

I must confess, I stole the title from a post at Genetic Future, but I think I'll get away with it since I just linked to the original post ;-)

In this post, Daniel MacArthur discusses genome wide (SNP) association mapping studies and concludes that:

The fact remains that despite the hundreds of millions of dollars spent on genome-wide association studies, most of the genetic variance in risk for most common diseases remains undiscovered.

He then lists a number of reasons why we have problems with figuring out most of the genetic causes of common diseases, starting with the large multiple testing correction needed when scanning the entire genome, listing also rare variants (we cannot find these with our current approach, but see here for why they could be important), and epistasis.

If you are interested in association mapping, you should read this post.

Saw this one at Greg Laden's blog. Thought I'd share it...

Roald Forsberg sent me this link: Study Finds Wide Range of Rare Mutations Linked to Schizophrenia.  It is a summary of the paper

Rare Structural Variants Disrupt Multiple Genes in Neurodevelopmental Pathways in Schizophrenia Walsh et al. Science DOI: 10.1126/science.1155174.

I haven't read the paper yet, but I should.

According to the GenomeWeb link, the study shows that schizophrenia associates with an enhancement of rare mutations.  This is interesting, since the association mapping approaches we use to detect disease / genotype association are almost all relying on the "common disease / common variant" assumption that says that if a disease is common in the population, then the genetic component of it will mainly be from a few common genetic variants.   How true this assumption is, we do not know.  It is not completely false, 'cause we have found gene/disease association with our methods (and we wouldn't have if our assumption was false), but we have found less than we had hoped and rare mutations might be the reason behind this. (Another reason could be gene-gene and gene-environment interaction).

This morning I am reading Among Orangutans, a book recommended on the orangutan genome project mailing list. It is not a genetics or bioinformatics book by any stretch, but I got to bed too late yesterday so I am feeling a bit tired and stupid in the morning, and this seemed like some light reading while I had my morning coffee.

It is a good book (judging from the first few chapters) and for someone like me, who thinks of animals as DNA sequences to be analysed, it gives an interesting other view on  these fascinating creatures.