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1 & 2: This is setting the bar rather high, no? I hate GWAS, but am surrounded by people who do them all the time. To show any kind of function for a SNP is har

by cgiles 9y ago

1 & 2: This is setting the bar rather high, no? I hate GWAS, but am surrounded by people who do them all the time. To show any kind of function for a SNP is hard, to do it in humans is very, very hard for something like this involving nervous system function, and to prove the difference in function is "adaptive" virtually impossible. The furthest people go, and fairly rarely, is to show an eQTL and spin some unconvincing tale about how the difference in expression might have some effect. But array-based methods do have one advantage which is that the 450K for example is heavily concentrated in genes and promoters. So the variant, which indeed does not span just one nucleotide, does have at least putative functions, which are simply the known functions of the gene, although yes, you cannot say whether the variant itself is helpful, harmful, or null on the functions of the gene. 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. 3. Putting it in an abstract is fairly desperate, I agree. But broadly speaking the way you know you aren't getting total nonsense is if a lot of related terms cluster towards the top. Which looks to be what they got. It looks a lot cleaner than most GOEA results I see. Perhaps you could educate me on what would be proof of positive selection. Would it be at least convincing evidence, if not proof, if you showed that phenotype P is associated with variant V, and variant V is associated with positive traits X,Y,and Z after controlling for P (so you can rule out direct links between P and X/Y/Z which bypass V)? From what I have seen, investigations into functions of individual SNPs are almost invariably a waste of time ending in handwavy stories so I wonder if there is a better approach.

searine 9y ago
>This is setting the bar rather high, no? 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 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 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 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.
- cgiles 9y ago
  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?
  - searine 9y ago
    > Given that, what do you suggest they should have done Not used the term positive selection. If they hadn't done that this would have been boring, but fine.

by cgiles 9y ago

searine 9y ago

>This is setting the bar rather high, no? 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 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 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 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.

cgiles 9y ago
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?
- searine 9y ago
  > Given that, what do you suggest they should have done Not used the term positive selection. If they hadn't done that this would have been boring, but fine.