. Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer's disease. Alzheimer's & Dementia, December 7, 2020

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  1. This is an elegant study by Kim and colleagues harnessing the gain in statistical power by virtue of focusing on the genetic variants that modify the risk of ApoE e4 genotype based on exome-sequencing data and using the evolutionary-action approach, i.e., a computational method to estimate the phenotypical impact of mutations.

    The study revealed a large number of genes harboring detrimental or protective mutations in ApoE e2 and ApoE e4 carriers, respectively. These exploratory results will encourage future investigations to follow up these findings in independent exome-sequencing replication cohorts as well as by genome-wide association analyses in larger cohorts.

    A major question is whether any variants in the depicted genes are modifiers, specifically, of the effect of ApoE genotype on AD, or are associated with AD risk in general. A comparison of the results with those of the recent whole-exome-sequencing study, which was also based on data from the Alzheimer’s Disease Sequencing Project, would have been great (Bis et al., 2018). 

    References:

    . Whole exome sequencing study identifies novel rare and common Alzheimer's-Associated variants involved in immune response and transcriptional regulation. Mol Psychiatry. 2018 Aug 14; PubMed.

    View all comments by Michael Ewers
  2. This study capitalizes on subsets of samples deemed “low genetic risk cases” (carriers of the protective APOE ε2 haplotype) and “high genetic risk controls” (carriers of the risk-increasing APOE ε4 haplotype) that underwent whole-exome sequencing (WES) as part of the Alzheimer’s Disease Sequencing Project (ADSP). The purpose in looking at these subsets is to use “extreme” sample sets, i.e., cases without known genetic risk factors and unaffected subjects with known genetic risk factors, to make it easier to try to identify novel coding variants with strong effects on disease risk as new AD susceptibility loci, by biasing the study against “the usual suspects” such as APOE and other known loci. This strategy has the potential to be richly rewarding in identifying new disease susceptibility loci, candidate pathways, and relevant gene networks.

    The genes identified in this study are interesting, as they are found in a number of pathways and networks involving known AD susceptibility loci from prior GWAS, however few prior AD GWAS loci themselves were identified. This may be because, as the authors noted, most GWAS loci fall in noncoding regions while in contrast this study of WES focuses on coding region variation, and also because their APOE-based sampling should have controlled for associations in or near that locus. However, because of the selected nature of a sample, a major question remains about the generalizability of their findings: How much do these genes/variants contribute to AD risk in aggregate in representative samples of the population, and among groups not selected for APOE e4 enrichment and case-control status? Quantifying their contribution to disease heritability overall may speak to the importance of recruiting sample sets with this kind of enrichment.

    There are some concerns about the study design. First, there are sample size limitations inherent in the design and there was no validation in independent samples. Second, the ADSP WES dataset was sequenced at three different sequencing centers using two different sequence capture kits, which may lead to variation in genotype quality. Specifically, it is unclear if the variants examined fell within targeted capture regions in all samples (in both kits), which is a concern because of highly variable but overall reduced genotype calling quality and accuracy among off-target variants, even those in sequence immediately flanking capture regions. ADSP best practices recommend exclusion of these variants, which account for nearly 50 percent of all called variants, and it is unclear whether these lower-quality variants were excluded by the filtering criteria implemented. While this may present a problem, validation in other data is highly encouraged and may still yield support for their findings.

    Finally, acknowledging the wealth of genes identified in this study that are further supported by integrating functional genomics, we have a major question about genomic search strategies like this to consider: Does a larger search space for AD candidate genes/loci help or hurt the hunt for therapies to slow or stop progression of the disease? Among the best examples of successful translations of genetic studies to clinical treatment are the development of lipid-lowering drugs based on studies that identified risk variants in HMGCR and PCSK2. Does this enhanced-AD search space filled with new candidate genes make it easier to identify therapeutic targets likely to have an effect of the disease? Also, with so many potential therapeutic targets emerging, would strategies to prioritize specific genes for further investigation help to make the studies like these more appealing and informative? This is a broad and emerging philosophical debate with which the field will grapple in coming years.

    View all comments by Adam Naj
  3. Kim et al. applied an elegant evolution-based variant functional impact weighting method and a novel discordant phenotype subsampling approach to identify genes that modify predicted AD outcomes in APOE-ε4 and ε2 carriers. Their analyses suggest etiological roles for synaptic maintenance in several cell types and the endolysosomal system in microglia. This is consistent with findings from previous analyses by our group and others (Aug 2019 newsDourlen et al., 2019). 

    However, this unorthodox association analysis methodology is not described in detail, rendering it difficult to assess the significance and robustness of the findings. It is unclear what “# of observed variants” refers to (cumulative minor allele count, number of variants), what variant and sample level QC was performed (e.g., minor allele frequency cutoff), and what technical (batch, capture, sequencing center, etc.) or biological (age, sex, population structure) covariates were included in the association analyses. Lack of replication is another issue limiting confidence in the findings. Yet it appears that only about half (5,686) of the 10,000 individuals available in the ADSP Discovery cohort were used in this study, such that the remaining half could be used as an independent replication dataset to strengthen its findings.

    There are other areas of concern. Diagnostic plots (e.g., residuals vs. fitted, normal Q-Q) for the gene discovery linear regressions would aid interpretation but are not shown. Nominal significance is used as a threshold in the permutation analysis that generated the final list of 216 iDEAL genes. Multiple testing correction and thus a much higher number of permutations would be required to constrain the number of false positive findings in this list.

    Additional evidence is provided to support the statistical association results. Differential gene expression of the iDEAL genes in AD vs. control brains, although widely used, is not very convincing for causal involvement of a gene because it could arise from cell-type proportion changes in the degenerating brain tissue or reactive (rather than causal) changes in gene expression programs within cells.

    Moreover, it is unclear how the authors generated the list of AD GWAS candidate genes used in their STRING network analysis that showed significant interconnectivity of iDEAL genes with known AD genes. GWAS are designed to identify loci, not genes, and (with a few exceptions like APOE, TREM2, PLCG2, ABI3, ABCA7, SORL1, SPI1, etc.) the underlying causal gene(s) remain(s) unknown for most AD-associated loci. Interestingly, the Drosophila Aβ/Tau model experiments identified a few promising examples, validating the dAD-ε2 and dAD-ε4 predictions, and it would be worthwhile exploring the effect of these iDEAL genes on disease-relevant phenotypes in vertebrate models.

    Despite these concerns, the authors' use of extreme phenotype sampling to improve statistical power and identify novel modifier genes holds great promise for elucidating AD pathogenic mechanisms.

    References:

    . The new genetic landscape of Alzheimer's disease: from amyloid cascade to genetically driven synaptic failure hypothesis?. Acta Neuropathol. 2019 Aug;138(2):221-236. Epub 2019 Apr 13 PubMed.

    View all comments by Alan Renton

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