Corces MR, Shcherbina A, Kundu S, Gloudemans MJ, Frésard L, Granja JM, Louie BH, Eulalio T, Shams S, Bagdatli ST, Mumbach MR, Liu B, Montine KS, Greenleaf WJ, Kundaje A, Montgomery SB, Chang HY, Montine TJ. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and Parkinson's diseases. Nat Genet. 2020 Nov;52(11):1158-1168. Epub 2020 Oct 26 PubMed.

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  1. This work has several objectives: (i) to establish a systematic mapping of transposase-accessible chromatin (ATAC) regions in seven brain regions from 39 healthy individuals; (ii) from some of the samples used in the previous bulk assay, to perform a similar ATAC analysis at the cell-type level by incorporating HiCHIP analyses in these samples (chromatin immunoprecipitation for H3K27ac); (iii) to use these data to develop a pipeline to prioritize the functional variants responsible for GWAS signals in noncoding regions.

    This work is well-performed, really interesting, and generates a large amount of data that will be very useful to the scientific community as it works to make sense of GWAS data.

    The pipeline has been tested on Alzheimer's and Parkinson's data, but could obviously be extended to other brain diseases or phenotypic traits. Moreover, this work could also make it possible to elucidate why a gene can be involved in two different pathologies through differential patterns of expression in different cells that, for example, depend on different epigenetic characteristics.

    Of course, it is necessary to keep in mind that these omic approaches can systematically generate false-positive and -negative results (even if the authors took some precautions to control for them). This study also did not interrogate potential specific disease-related epigenetic characteristics. In addition, no biological validation has been realized and the final functional prioritization is mainly based on in silico and statistical approaches. As a consequence, these data have to be used with some caution, keeping in mind that they cannot be considered fully exhaustive or fully biologically validated.

    In addition, the selection of the SNPs of interest in AD and PD led to the potential exclusion of secondary signals in the loci analyzed. Such fine mapping would have been very useful to reinforce the implication of some risk factors in specific cellular types. However, it is difficult to assess whether the machine-learning approach used in this paper would have been able to handle such complexity at the genetic level.

    View all comments by Jean-Charles Lambert

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