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Yu L, Chibnik LB, Srivastava GP, Pochet N, Yang J, Xu J, Kozubek J, Obholzer N, Leurgans SE, Schneider JA, Meissner A, De Jager PL, Bennett DA. Association of Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease. JAMA Neurol. 2015 Jan;72(1):15-24. PubMed.
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Institute Pasteur de Lille, INSERM
The paper by Yu et al. is a complementary study of a very recent work published by the same team in Nature Neuroscience (De Jager et al., 2014). In the first study, the authors studied the epigenome in the brains of 704 autopsied samples from two very well-characterised cohorts. The authors established that levels of methylation in specific genomic regions can be significantly associated with neuritic plaque burden in the brain.
In this new study, the authors reanalysed their epigenomic data to determine whether methylation levels may be associated with the risk of developing AD. Rather than describing a genome-wide analysis of their complete epigenomic data, they restricted this paper to the study of the known genes/loci reported to be potentially involved in the AD monogenic or sporadic forms. They observed that the global level of methylation in five loci (out of 28 tested) was associated with AD risk. Remarkably, for these five loci, this global level of methylation was also associated with Aβ load and tau tangle density.
These data clearly reinforce the implication that these five loci are involved in AD. Indeed, even if this work utilizes a candidate gene approach (with the risk of “tautological reasoning”), the data point out that the expression level of the gene(s) located within these loci may be particularly important for AD pathophysiology because the epigenomic signal is likely fully independent of genetic variations associated with AD risk in genome-wide association studies (GWAS). This is particularly true for the BIN1 locus, for which some evidence suggests that its expression might be important for tau pathology (Chapuis et al., 2013).
Yu et al.'s work may be also very useful at another level: A GWAS-defined locus can encompass a large number of genes. On a pure genetic basis, it can be difficult to determine which one is responsible for the GWAS signal, all the more since the functional variants can sometimes be quite far away from the real causal gene. With this in mind, we notice two interesting points concerning the HLA-DRB5 and SLC24A4 loci. The most relevant and dense methylation signals are not in the genes exhibiting the highest level of association with AD risk in GWAS. This is particularly true for the signal in the HLA region: The epigenomic data mainly pointed out HLA-DRA, which is also within the GWAS-defined locus. Notably, transcriptomic analysis suggests this gene may be differentially expressed in the brains of AD cases compared to controls (Chapuis et al., 2009). Extending the epigenetic analysis to the complete HLA region would help decipher this highly complex genetic region. Similarly, whereas the best GWAS signal was close to SLC24A4, methylation variations associated with AD risk seemed to point out RIN3. At a biological level, SCL24A4 is involved in iris and skin pigmentation, whereas RIN3 would be a more convincing candidate since it is involved in neuronal function, as noticed in our initial GWAS paper (Lambert et al., 2013) and by Yu et al.
Taken as a whole, these data are clearly interesting and represent a new piece in the complex puzzle of AD genomics. However, as mentioned in the companion editorial in JAMA neurology, “Like all good science, the article by Yu et al. has limitations and raises compelling questions” (Traynor and Renton, 2014). Importantly, these data of course need to be replicated: Using a powerful technology and an appropriate design does not, unfortunately, avoid the classic methodological biases in genomic epidemiology, e.g., sampling variations and underpowering. Since a candidate approach was used, it is difficult to assess the possibility that this study generated false positives. Though the authors took some precautions by performing permutation analyses, knowing the value of a genomic control parameter such as the λ inflation factor, which reflects potential false positives, would have been of interest.
As for all observational studies, it is not possible to answer to the crucial question: cause or consequence? For instance, it cannot be excluded that these differences in methylation are late responses to earlier dysregulation of gene expression.
Finally, statistical approaches cannot replace biological evidence and it will be necessary to understand the real impact of these methylation fluctuations on gene expression in different types of cells and in a pathophysiological context.
References:
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