Dong X, Liao Z, Gritsch D, Hadzhiev Y, Bai Y, Locascio JJ, Guennewig B, Liu G, Blauwendraat C, Wang T, Adler CH, Hedreen JC, Faull RL, Frosch MP, Nelson PT, Rizzu P, Cooper AA, Heutink P, Beach TG, Mattick JS, Müller F, Scherzer CR. Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease. Nat Neurosci. 2018 Oct;21(10):1482-1492. Epub 2018 Sep 17 PubMed.
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National Institute on Aging
Classifying gene-expression patterns and their relationship to genetic variants is a very important way to determine proximate genetic mechanisms arising from genome-wide association studies (GWAS). The use of laser capture microdissection and RNA-seq is an interesting way to generate these types of data sets; while there are methods for single-cell analysis from human brain, such as DRONC-seq, these tend to have a lot of “missingness” in the data and therefore would be less definitive.
Nonetheless, I am puzzled by several conceptual aspects of this paper. First, I don’t understand the focus on dopamine neurons across neuropsychiatric conditions. In PD, it is now very clear that DA neurons are important but not the only cell type affected, and several enrichment analyses have highlighted a substantial contribution from immunological cells, including microglia. The logic seems a bit circular here and I think we should keep an open mind as to potential mechanisms that might include non-neuronal cells as well as multiple neuronal types, especially if the Braak hypothesis is correct and PD starts in the enteric or olfactory systems.
Second, I am not convinced by the inference that the chromosome 17 risk gene is KANSL1. I do agree that the practice of listing nearest gene to the lead SNP on GWAS can lead to a false sense of precision—we often assume that this means we know the gene when in fact, because of co-inheritance of multiple variants, GWAS nominate loci, not genes. Consequentially, a major difficulty with interpretation of GWAS is that it is difficult to infer causality for any specific variant or gene. Looking through the GTEX dataset, one can see a regulatory effect on KANSL1 of the lead SNP, but also a reasonably strong eQTL at MAPT. Therefore, depending on what evidence one might examine one could favor either KANSL1 or MAPT. Further complicating the picture, there is no reason to assume that there is in fact one gene for each locus—some could easily be complex with contributions by multiple co-regulated genes, for example, or a mixture of rare variants and regulatory events.
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