Re:Signatures of positive selection in autism spectrum disor...
by cgiles on Friday February 23, 2018 @10:02PM
(#16451383)
Thanks for the links, I will check them out. An embarassing correction: the 450K is (as you well know) not a SNP array. Can you tell I've been doing more than my fair share of methylation stuff recently?
The fundamental problem I have with your reply, though, is that the authors were not really concerned with any particular SNP. They wanted to make statements about the group of ASD-related SNPs. Clearly it is infeasible to do most of the things you have suggested here with >100 SNPs. Given that, what do you suggest they should have done?
I understand what you are saying, but "signatures of positive selection" has a very specific technical definition and associations are not it. It means allele fixation in a population due to quantifiable selective pressure (or quantifiable selective pressure proxy statistics).
When it comes to quantifying evolution, I want to see them attempt to validate molecularly or an attempt to infer selection mathematically. For example, show that the SNP is non-synonymous and causes protein change, and then rescue. Show that it's in a promoter, splice site or enhancer and rescue. Show extended runs of haplotype homozygosity thats significant. Show a selective sweep in the region, or a peak of LD. Show a higher than expected diversity in the target gene between species, and a derth within-species. Run PAML or do some comparative genomics to show the SNP being novel and adaptive. Show me a skewed allele frequency spectrum. There are lots of ways to show positive selection that are outside the toolbox of a lot of GWAS people. HAR1 for example was an absolutely beautiful story of a bioinformatic prediction of adaptive evolution, followed by amazingly solid functional validation : https://www.ncbi.nlm.nih.gov/pubmed/16915236
Alternatively, this paper is a tour-de-force of detecting adaptive evolution bioinformatically. https://www.nature.com/articles/nature10944
If feel like there is this big disconnect between the world of GWAS and the world of population genomics, and this paper was really frustrating because I was open to the hypothesis but it fell flat on the delivery.
Anyway, if you want to know more, check out https://www.ncbi.nlm.nih.gov/pubmed/16285858 this review. It's by one of the eminent population geneticists in the field and really covers the different signals of selection well, and intuitively.
> It is at least a step up from people who come to me (bioinformatics) and ask me to use my divining rod to guess what their SNP 100Kbp from the nearest gene might do.
Hey, I feel you. I know the struggle all too well.