Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, Christiansen MW, Fairfax BP, Schramm K, Powell JE, Zhernakova A, Zhernakova DV, Veldink JH, van den Berg LH, Karjalainen J, Withoff S, Uitterlinden AG, Hofman A, Rivadeneira F, 't Hoen PA, Reinmaa E, Fischer K, Nelis M, Milani L, Melzer D, Ferrucci L, Singleton AB, Hernandez DG, Nalls MA, Homuth G, Nauck M, Radke D, Völker U, Perola M, Salomaa V, Brody J, Suchy-Dicey A, Gharib SA, Enquobahrie DA, Lumley T, Montgomery GW, Makino S, Prokisch H, Herder C, Roden M, Grallert H, Meitinger T, Strauch K, Li Y, Jansen RC, Visscher PM, Knight JC, Psaty BM, Ripatti S, Teumer A, Frayling TM, Metspalu A, van Meurs JB, Franke L. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013 Oct;45(10):1238-43. PubMed.
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Washington University School of Medicine
These papers have numerous implications. Together with the paper published by ENCODE (ENCODE Project Consortium, 2012), this work clearly highlights the importance of the non-coding DNA on gene regulation and expression. One repercussion of these findings is their impact on interpreting GWAS results. Most GWAS, including those for risk for Alzheimer’s disease, have identified several loci located in intergenic regions. In these cases it is difficult to understand what is the mechanism by which those loci are associated with disease and to identify the functional variant responsible for that association. By integrating GWAS data with these results, the reason why some loci are associated with a specific disease or trait can be explained. This also may help to identify potential therapeutic interventions.
In terms of AD, it is worth checking the known AD GWAS signals against this data to help us interpret AD GWAS data. That said, overall, these methods can be translated to neurodegenerative disease research more as a general approach than being directly relevant. The main limitation with direct extrapolation is that these studies focused on gene expression in blood, not brain tissue. Blood and brain will have some of these eQTL in common, but not all. As AD is clearly a brain disease, it will be good to analyze how much overlap there is between brain and blood eQTLs. To date this is unknown.
View all comments by Carlos CruchagaI would like to see the results of this type of analysis for brain gene expression. That could help us identify the functional variant driving the association in a given AD GWAS analyses. For most of the AD GWAS loci located on PICALM, CR1, CD33, for example, we do not know which is the functional variant of the disease mechanism. As an example, it looks as if the SNP associated with AD in CD33 modifies CD33 expression (Bradshaw et al., Nat Neurosi 2013 and Griciuc A et al., Neuron 2013), and that this different CD33 expression leads to different Abeta clearance and accumulation. In this case, these two groups focused on CD33 and found an association with expression. But with genome-wide expression data, it would be possible to analyze all AD GWAS hits at the same time, and potentially identify additional functional variants. After that, additional functional analysis will be needed to fully understand the “pathogenic” mechanism, but the identification of the functional variant will be a great step.
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