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Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Alzheimer's Disease Genetics Consortium (ADGC), European Alzheimer's Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in Alzheimer's Disease Consortium (GERAD/PERADES), Schmidt H, Hakonarson H, Munger R, Schmidt, Farrer LA, Broeckhoven CV, O'Donovan MC, Destefano AL, Jones L, Haines JL, Deleuze JF, Owen MJ, Gudnason V, Mayeux RP, Escott-Price V, Psaty BM, Ruiz A, Ramirez A, Wang LS, van Duijn CM, Holmans PA, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Lambert JC, Pericak-Vance MA. Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing. bioRxiv. April 5, 2018. bioRxiv.
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Van Andel Research Institute
A general note on these three manuscripts is that it is very positive that they were initially submitted to BioRχiv, making them open to everyone without publication delays. I hope we continue to see this for large-scale genetic studies.
These are exciting manuscripts that move the field a step forward. The AD-by-proxy approach seems to have validity in the UK Biobank data set, showing high genetic correlation estimates with clinical AD. This opens the door to studies with sample sizes that otherwise would be years away from being possible to achieve by any one group.
There are some aspects that should be kept in mind, however: Independent replication of new loci becomes a tricky issue when one uses over 400,000 samples, simply because there are no other data sets of similar size. Since some of the data sets are shared as summary statistics, is it plausible that the same samples are included in multiple studies. This may become problematic, particularly for the analysis of lower-frequency variants. Also, the overlap in findings isn’t complete, which may suggest that some of these hits are false positives.
The immune system and lipid metabolism continue to come up as pathways strongly associated with AD, but now there’s data suggesting the involvement of APP metabolism and tau binding proteins. The former is particularly interesting, since it links early and late-onset AD to the same mechanism—a finding that recent analyses from DIAN and ADNI have also shown.
These are remarkable studies and it is very exciting to see these novel approaches being applied to the research in dementia.
View all comments by Jose BrasInstitute of Neurology, UCL
Jansen et al. and Marioni et al. add new samples to the established Alzheimer GWAS, and by that means they increase the (n) of these studies and lead to the provisional identification of new loci for the disease. Both studies use family history of dementia as part of their analysis. This is clearly imperfect, as the authors realize, but it does add power, and the fact that just “AD by proxy analyses” find the established GWAS hits gives some justification for this approach.
A problem with these analyses is what I would call the “audit” issue. It is getting increasingly difficult to know whether individuals, in either the control or disease category, have been entered into the combined analyses more than once, and the audit trail is extremely difficult to follow. Clearly, if some cohorts are being counted twice, this makes one less confident in the outcome. Because of the challenges in accessing full data on each sample, it is difficult to assess on an individual sample basis, or even on a cohort basis.
However, the main findings continue to be clear: Microglial activation and lipid metabolism as well as APP metabolism, repeatedly show up as the incriminated pathways to disease. Of course, both lipid metabolism and microglial activation are recognized to be very broad terms, and defining which aspects of these complex (and probably interrelated) pathways are at play is clearly an important task.
The next task, one presumes, is to put these two studies together and do yet another audited meta-analysis. This likely will push even more loci from the same pathways over the magical Bonferroni correction for statistical significance and declaration.
View all comments by John HardyNational Institute on Aging
The current set of GWAS-related studies indicates that there are still significant loci to find for Alzheimer’s disease, and likely for other neurodegenerative conditions, as sample sizes increase. By definition, these will be loci with smaller effect sizes than those previously characterized, usually changing risk of disease by a few percent, but they are important to document as they should lead us to a deeper understanding of the gene pathways underlying neurodegeneration.
The current set of papers supports this contention in that not only are new replicated gene candidates such as ADAM10 nominated, but also a previously discussed enrichment of immune-related genes is confirmed in these larger studies. Taken together, these studies strongly suggest that there are potential therapeutic targets around inflammation that might be helpful in sporadic AD.
The one caveat that I would identify to modify this strong conclusion—and this is a generic concern with GWAS—is that while these groups use sensible strategies to identify best candidate genes at each locus, in some ways this might give a false sense of precision. Due to linkage disequilibrium, GWAS identifies groups of SNPS across each locus. Therefore, the pathway-based analyses are provisional but promising.
View all comments by Mark CooksonAlzforum
To read the paper on bioRχiv, click here.
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