A Multiomics Approach to Heterogeneity in Alzheimer's Disease

Focused Review and Roadmap

AmanPreet Badhwar; G. Peggy McFall; Shraddha Sapkota; Sandra E. Black; Howard Chertkow; Simon Duchesne; Mario Masellis; Liang Li; Roger A. Dixon; Pierre Bellec


Brain. 2020;143(5):1315-1331. 

In This Article

Genomics-derived Polygenic Risk Scores

Polygenic Risk Score Approach

Constructed of multiple single nucleotide polymorphisms (SNPs) that implicate one or more biological mechanisms, PRSs (Figure 3) are better at discriminating Alzheimer's disease from cognitively normal than single-gene analysis (Escott-Price et al., 2017b; Torkamani et al., 2018). We reviewed 11 Alzheimer's disease PRS studies (Supplementary Table 4 and Supplementary Table 5), the majority of which comprised genome-wide association studies (GWAS)-detected SNPs. To identify genetic risk beyond that of APOE alone, several studies assessed PRS with (APOE-PRS) and without (non-APOE-PRS) APOE. Desikan et al. (2017) found that an APOE-PRS associated with age-at-onset of Alzheimer's disease symptoms, decreased amyloid-β and increased tau in CSF, and increased atrophy, tau, and amyloid-β load in brain. APOE-PRS also associated with plasma inflammatory markers in Alzheimer's disease patients (Morgan et al., 2017). An APOE-PRS including a rare TREM2 (triggering receptor expressed on myeloid cells 2) variant discriminated Alzheimer's disease dementia and cognitively normal, with increasing scores associating with decreasing age at onset, and CSF amyloid-β42 (Sleegers et al., 2015). Discriminative power of APOE-PRS was found to improve with diagnostic accuracy, as demonstrated using a pathologically confirmed Alzheimer's disease cohort (Escott-Price et al., 2017a). In four separate studies, a non-APOE-PRS was reported to discriminate between Alzheimer's disease dementia and cognitively normal (Xiao et al., 2015), as well as associate with MCI (Adams et al., 2015), increased risk of Alzheimer's disease dementia (Adams et al., 2015; Chouraki et al., 2016; Tosto et al., 2017), and earlier Alzheimer's disease onset (Tosto et al., 2017). Inclusion of APOE either resulted in a modest increase in discriminative power (Xiao et al., 2015), stronger clinical or biomarker associations (Adams et al., 2015; Chouraki et al., 2016), or had no additional effect (Tosto et al., 2017). In another non-APOE-PRS study in Alzheimer's disease patients, PRS scores correlated negatively with CSF amyloid-β42 levels, and positively with temporal cortex amyloid-β pathology, and γ-secretase activity (Martiskainen et al., 2015). Naj et al. (2014) found that although APOE contributed to 3.7% of age at onset variability in Alzheimer's disease dementia patients, adding a non-APOE-PRS accounted for an additional 2.2%. Overall, Alzheimer's disease heritability has a large polygenic contribution beyond APOE, which makes PRS approaches pivotal for Alzheimer's disease risk prediction (Escott-Price et al., 2015).

Figure 3.

Polygenic risk scores. High-throughput genotyping technologies have revolutionized studies in diseases with complex genetics by enabling detection of common genetic variants with low effect sizes, and rarer variants with relatively higher effect sizes (A). In Alzheimer's disease, the prevalent late-onset variant is genetically complex and demonstrates high heritability (up to 80%) (Gatz et al., 2006), whereas the early-onset familial variant is deterministically driven by single gene mutation(s) in PSEN1 (presenilin 1), PSEN2 (presenilin 2) or APP (amyloid precursor protein) (Guerreiro et al., 2013). The genetics of late-onset Alzheimer's disease has been predominantly investigated using GWAS. Designed to rapidly scan for statistical links between a set of known SNPs and a phenotype of interest, GWAS can identify common variants with minor allele frequency >5% (Torkamani et al., 2018) (A). Up to 24 key Alzheimer's disease-risk genes have been identified using GWAS (Supplementary Table 4). The major limitation (and strength) of GWAS is the data-driven, hypothesis-free approach in which multiple genes are identified, though the majority of significant SNPs are (i) located in non-coding or gene-rich areas of the genome making it difficult to identify which gene is being modified by the SNP; and (ii) in high linkage disequilibrium with many SNPs making it difficult to identify which functional variant is responsible for modifying Alzheimer's disease risk (Karch et al., 2016). Identification of rarer Alzheimer's disease-associated SNPs (minor allele frequency >0.5% and <5%), that often escape detection with GWAS, is being enabled by next-generation genome sequencing (NGS) technologies, such as whole-exome sequencing and targeted resequencing of disease-associated genes (Bras et al., 2012; Masellis et al., 2013) (Supplementary Table 4). NGS technologies provide transcriptome-wide coverage without requiring any a priori knowledge of SNPs (A). To date, Alzheimer's disease prediction using individual, high-throughput genotyping technologies identified, risk genes have been predominantly non-significant, with the exception of APOE, which accounts for up to 30% of the genetic risk (Daw et al., 2000). Therefore, the search for risk genes beyond APOE now include PRS (also referred to as genetic risk scores, risk indexes or scales) approaches (B). A PRS is a calculation (e.g. weighted sum) based on the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by GWAS-detected loci and their measured effects (Torkamani et al., 2018). In the most common scenario, only SNPs reaching conventional GWAS significance (P < 5 × 10−8) are included (C). A threshold lower than the genome-wide statistical significance (e.g. P = 10−5) can also be used to improve or estimate total predictability (Torkamani et al., 2018) (C). SNPs representing multiple hits among Alzheimer's disease risk genes from one or more major mechanistic pathways can also be included into a PRS (C). Displayed are six main mechanistic clusters, each populated by genetic variants thought to represent the cluster (D). Genetic variants have been placed within the cluster according to population frequency (horizontal axis) and level of estimated risk (vertical axis). For example, an amyloid-β/APP metabolism cluster is made up of rare ADAM10 (a disintegrin and metalloproteinase domain-containing protein 10) and common APOE4+ higher risk genes, and rare PLD3 (phospholipase D family member 3) and common PICALM (phosphatidylinositol binding clathrin assembly protein) lower risk genes. Some genes are involved with multiple mechanisms as can be seen for PICALM's involvement in nervous function, basic cellular processes, and amyloid-β/APP metabolism. As implied in the figure, when creating PRS, it may be very useful to select genes within mechanistic groups, and select groups depending on the purpose of the research. In sum, PRS reflects a large number of SNPs and a complex set of biological mechanisms related to Alzheimer's disease pathogenesis, and can improve the precision of early Alzheimer's disease risk or diagnosis (Desikan et al., 2017; Escott-Price et al., 2017b; Morgan et al., 2017).

Mechanism-based Interaction and Network Approaches

Alzheimer's disease risk genes can be clustered into functional/mechanistic pathways (Figure 3D), and the information gained used to improve Alzheimer's disease discrimination and/or risk prediction (Gaiteri et al., 2016; Hu et al., 2017). We reviewed six mechanism-based Alzheimer's disease studies (Supplementary Table 5). Functional variants of Alzheimer's disease GWAS-significant SNPs (e.g. CELF1 or CUGBP Elav-like family member 1) was reported to associate with human brain expression quantitative trait loci, and preferentially expressed in specific cell types (e.g. microglia) (Karch et al., 2016). Rosenthal et al. (2014) highlighted the potential regulatory functions of non-coding Alzheimer's disease GWAS SNPs. Protein-protein interaction network analyses highlighted that Alzheimer's disease risk genes, whose protein products interact physically, may be under positive evolutionary selection (e.g. PICALM or phosphatidylinositol binding clathrin assembly protein, BIN1 or bridging integrator 1, CD2AP or CD2 associated protein, EPHA1 or EPH receptor A1) (Raj et al., 2012). Ebbert et al. (2014) reported that while an APOE-PRS did not improve discrimination of Alzheimer's disease from cognitively normal over APOE, a model allowing for epistatic interactions between SNPs increased discriminative power. Patel et al. (2016) applied a stratified false discovery rate approach, used to increase GWAS power by adjusting significance levels to the amount of overall signal present in data, to identify gene networks and provide links with structural MRI phenotypes: e.g. linking genes involved in transport [e.g. SLC4A10 or (solute carrier family 4 member 10), KCNH7 (potassium voltage-gated channel subfamily H member 7)] with hippocampal volume. Huang et al. (2018) integrated Alzheimer's disease GWAS genes with human brain-specific gene network using machine learning to identify additional Alzheimer's disease-risk genes.


In summary, PRSs may contribute substantially to accounting for the genetic variability that distinguishes Alzheimer's disease from MCI and cognitively normal groups. They may also be used to probe genetic underpinnings of Alzheimer's disease subtypes as well as related and disparate NDDs. Thus far, research reporting PRSs in relation to conversion rates of cognitively normal or MCI to Alzheimer's disease dementia have been mixed (Adams et al., 2015; Lacour et al., 2017), however, early PRS prediction of cognitive trajectories and clinical outcomes have also been reported (Desikan et al., 2017; Sapkota and Dixon, 2018).