>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

>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.