Immune-related genetic enrichment in frontotemporal dementia: An analysis of genome-wide association studies

Summary

Rahul Desikan and colleagues use summary data from genome-wide association studies to investigate genetic overlap between frontotemporal dementia and a several immune-mediated diseases, and identify microglia and inflammation-associated genes that may play a role in FTD pathogenesis.

Introduction

Frontotemporal dementia (FTD) is a fatal neurodegenerative disorder and the leading cause of dementia among individuals younger than 65 years of age [1]. Given rapid disease progression and the absence of disease-modifying therapies, there is an urgent need to better understand FTD pathobiology to accelerate development of novel preventive and therapeutic strategies.

Converging molecular, cellular, genetic, and clinical evidence suggests that neuroinflammation plays a role in FTD pathogenesis. Complement factors and activated microglia, key components of inflammation, have been established as histopathologic features in brains of patients [2] and in mouse models of FTD [3,4]. Genome-wide association studies (GWASs) have shown that single nucleotide polymorphisms (SNPs) within the immune-regulating human leukocyte antigen (HLA) region on Chromosome (Chr) 6 are associated with elevated FTD risk [5]. Importantly, there is increased prevalence of immune-mediated diseases among patients with FTD [6,7]. Together, these findings suggest that immune-related mechanisms may contribute to and potentially drive FTD pathology.

Recent work in human molecular genetics has emphasized “pleiotropy,” where variations in a single gene can affect multiple, seemingly unrelated phenotypes [8]. In the present study, we systematically evaluated genetic pleiotropy between FTD and immune-mediated diseases. Using large neurodegenerative GWASs and recently developed tools to estimate polygenic pleiotropy, we sought to identify SNPs jointly associated with FTD-related disorders [9,10]—namely, FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS)—and 1 or more immune-mediated diseases including Crohn disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis (PSOR).

Methods

Participant samples

We conducted a meta-analysis of summary data obtained from published data. More specifically, we evaluated complete GWAS results in the form of summary statistics (p-values and odds ratios) for FTD, CBD, PSP, and ALS and 6 immune-mediated diseases, including CD [11], UC [12], RA [13], T1D [14], CeD [15], and PSOR [16] (see Table 1). We obtained FTD GWAS summary statistic data from phase I of the International FTD-Genomics Consortium (IFGC), which consisted of 2,154 clinical FTD cases and 4,308 controls with genotyped and imputed data at 6,026,384 SNPs (Table 1; for additional details, see [5]). The FTD dataset included multiple clinically diagnosed FTD subtypes: behavioral variant (bvFTD), semantic dementia (sdFTD), primary nonfluent progressive aphasia (pnfaFTD), and FTD overlapping with motor neuron disease (mndFTD). These FTD cases and controls were recruited from 44 international research groups and diagnosed according to the Neary criteria [17]. The institutional review boards of all participating institutions approved the procedures for all IFGC sub-studies. Written informed consent was obtained from all participants or surrogates. We obtained CBD GWAS summary statistic data from 152 CBD cases and 3,311 controls at 533,898 SNPs (Table 1; for additional details, see [18]). The CBD cases were collected from 8 institutions, and controls were recruited from the Children’s Hospital of Philadelphia. CBD was neuropathologically diagnosed using the National Institutes of Health Office of Rare Diseases Research criteria [19]. The institutional review boards of all participating institutions approved the procedures for CBD GWAS data. Written informed consent was obtained from all participants or surrogates. We obtained PSP GWAS summary statistic data (stage 1) from the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, https://www.niagads.org) for 1,114 individuals with autopsy-confirmed PSP and 3,247 controls at 531,451 SNPs (Table 1; for additional details, see [20]). The institutional review boards of all participating institutions approved the procedures for all NIAGADS sub-studies. Written informed consent was obtained from all participants or surrogates. We obtained ALS GWAS summary statistic data from 12,577 ALS cases and 23,475 controls at 18,741,501 SNPs (Table 1; for additional details, see [21]). The ALS GWAS summary statistics and sequenced variants are publicly available through the Project MinE Data Browser (http://databrowser.projectmine.com). The institutional review boards of all participating institutions approved the procedures for all ALS GWAS sub-studies. Written informed consent was obtained from all participants or surrogates.

Genetic enrichment statistical analyses

The pleiotropic enrichment strategies implemented here were derived from previously published stratified false discovery rate (FDR) methods [22,23]. For given phenotypes A and B, pleiotropic “enrichment” between phenotype A and phenotype B exists if the proportion of SNPs or genes associated with phenotype A increases as a function of increased association with phenotype B. To assess for enrichment, we constructed fold enrichment plots of nominal −log10(p)-values for all FTD-related-disorder SNPs and for subsets of SNPs determined by the significance of their association with the 6 immune-mediated diseases. In fold enrichment plots, the presence of enrichment is reflected as an upward deflection of the curve for phenotype A with increasing strength of association with phenotype B. To assess for polygenic effects below the standard GWAS significance threshold, we focused the fold enrichment plots on SNPs with nominal −log10(p) < 7.3 (corresponding to p > 5 × 10⁻⁸). The enrichment seen can be directly interpreted in terms of the true discovery rate (1 − FDR).

To identify specific loci jointly involved with each of the 4 FTD-related disorders and the 6 immune-mediated diseases, we computed conjunction FDRs. The conjunction FDR is a test of association between 2 traits [22]. Briefly, the conjunction FDR, denoted by FDR_{trait1& trait2}, is defined as the posterior probability that a SNP is null for either trait or for both simultaneously, given that the p-values for both traits are as small, or smaller, than the observed p-values. Unlike the conditional FDR, which ranks disease/primary-phenotype-associated SNPs based on genetic “relatedness” with secondary phenotypes [24], the conjunction FDR minimizes the possibility/likelihood of a single phenotype driving the common association signal. The conjunction FDR therefore tends to be more conservative and specifically pinpoints pleiotropic loci shared between the traits/diseases of interest. We used an overall FDR threshold of <0.05, which means 5 expected false discoveries per 100 reported. To visualize the results of our conjunction FDR analysis, we constructed Manhattan plots to illustrate the genomic location of the pleiotropic loci. We ranked all SNPs based on the conjunction FDR and removed SNPs in linkage disequilibrium (r² > 0.2) with any higher ranked SNP. Key aspects and detailed information on fold enrichment plots, Manhattan plots, and conjunction FDRs can be found in prior reports [22,23,25,26].

Functional evaluation of shared risk loci

To assess whether SNPs that are shared between FTD and immune-mediated disease modify gene expression, we identified cis-expression quantitative trait loci (cis-eQTLs, defined as variants within 1 Mb of a gene’s transcription start site) associated with shared FTD–immune SNPs and measured their regional brain expression in a publicly available dataset of normal control brains (UK Brain Expression Consortium; http://braineac.org/) [27]. We also evaluated cis-eQTLs using a blood-based dataset [28]. We applied an analysis of covariance (ANCOVA) to test for associations between genotypes and gene expression. We tested SNPs using an additive model.

Network-based functional association analyses

To evaluate potential protein and genetic interactions, co-expression, co-localization, and protein domain similarity for the functionally expressed (i.e., with significant cis-eQTLs) pleiotropic genes, we used GeneMANIA (http://genemania.org), an online web portal for bioinformatic assessment of gene networks [29]. In addition to visualizing the composite gene network, we also assessed the weights of individual components within the network [30].

Gene expression alterations in FTD brains

To determine whether functionally expressed (i.e., with significant cis-eQTLs) pleiotropic genes are differentially expressed in the brains of FTD patients, we analyzed the gene expression of pleiotropic genes. Postmortem expression data from the brains of 17 patients with frontotemporal lobar degeneration with ubiquitinated inclusions (FTD-U) (with and without progranulin [GRN] mutations) and 11 controls were obtained from a publically available dataset (Gene Expression Omnibus [GEO] dataset GSE13162; for additional details, see [31]). These data consist of global gene expression profiles from all histopathologically available regions from human FTD-U and control brains (frontal cortex, hippocampus, and cerebellum) analyzed on the Affymetrix U133A microarray platform. Given the small sample size of each individual region, we combined all 3 regions to maximize statistical power. Details about this dataset and analysis—including the human brain samples used, RNA extraction and hybridization methods, microarray quality control, and microarray data analysis—are provided in the original report [31].

Evaluation of cell classes within the brain

Using a publicly available RNA-sequencing transcriptome and splicing database [32], we ascertained whether the functionally expressed (i.e., with significant cis-eQTLs) pleiotropic genes were expressed by specific cell classes within the brain. The 8 cell types surveyed were neurons, fetal and mature astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia/macrophages (henceforth “microglia”), endothelial cells, and pericytes (for additional details, see [32]).

Results

Shared genetic risk between FTD and immune-mediated disease

Using progressively stringent p-value thresholds for FTD SNPs (i.e., increasing values of nominal −log₁₀[p]), we observed genetic enrichment for FTD as a function of several immune-mediated diseases (Fig 1). More specifically, we found strong (up to 270-fold) genetic enrichment between FTD and RA, and comparable enrichment between FTD and UC, T1D, and CeD, with weaker enrichment between FTD and PSOR and CD.

At a conjunction FDR < 0.05, we identified 21 SNPs that were associated with both FTD and immune-mediated diseases (Fig 2; Table 2). Five of these SNPs demonstrated the opposite direction of allelic effect between FTD and the immune-mediated diseases (Table 2): (1) rs9261536, nearest gene = TRIM15; (2) rs3094138, nearest gene = TRIM26; (3) rs9268877, nearest gene = HLA-DRA; (4) rs10484561, nearest gene = HLA-DQB1; and (5) rs2269423, nearest gene = AGPAT1. Of the remaining 16, 2 SNPs showed strong linkage disequilibrium (LD), suggesting that they reflected the same signal: rs204991 and rs204989 (nearest gene: GPSM3; pairwise D′ = 1, r² = 1). After excluding SNPs that demonstrated the opposite direction of allelic effect and SNPs that were in LD, we found that 8 of the remaining 15 identified loci mapped to the HLA region, suggesting that HLA markers were critical in driving our results. To test this hypothesis, we repeated our enrichment analysis after removing all SNPs in LD with r² > 0.2 within 1 Mb of HLA variants (based on 1000 Genomes Project LD structure). After removing HLA SNPs, we saw considerable attenuation of genetic enrichment in FTD as a function of immune-mediated disease (Fig 3), suggesting that the observed overlap between immune-related diseases and FTD was largely driven by the HLA region. Further, to determine causal associations for FTD and the 6 immune-mediated diseases, we applied the recently developed summary-data-based Mendelian randomization (SMR; http://cnsgenomics.com/software/smr/) method. This approach is described in detail within the original report [33]. As shown in S1 Table, results from the SMR analysis have identified significant loci that are consistent with the main findings, which suggest that HLA markers on Chr 6 are critical in driving our pleiotropic results.

Outside the HLA region, we found 7 other FTD- and immune-associated SNPs (Fig 2; Table 2), including 2 in strong LD that mapped to the H1 haplotype of microtubule associated protein tau (MAPT) (LD: rs199533 and rs17572851; nearest genes: NSF and MAPT, pairwise D′ = 1, r² = 0.94). Beyond MAPT, we found 5 additional novel loci associated with increased FTD risk, namely, (1) rs2192493 (Chr 7, nearest gene = TWISTNB), (2) rs7778450 (Chr 7, nearest gene = TNS3), (3) rs10216900 (Chr 8, nearest gene = CR590356), (4) rs10784359 (Chr 12, nearest gene = SLC2A13), and (5) rs2134297 (Chr 18, nearest gene = DCC) (see Table 2 for additional details).

Modest genetic enrichment between immune-mediated disease and PSP, CBD, and ALS

To evaluate the specificity of the shared genetic overlap between FTD and immune-mediated disease, we also evaluated overlap between the 6 immune-mediated diseases and CBD, PSP, and ALS. For CBD and PSP, a few of the immune-mediated diseases produced genetic enrichment comparable to that seen for FTD (S1–S3 Figs; S2–S4 Tables). For example, we found 150-fold genetic enrichment between CBD and CeD and 180-fold enrichment between PSP and RA. In contrast, we found minimal enrichment between ALS and the immune-mediated diseases tested, with the highest levels of enrichment between ALS and RA (up to 20-fold) and between ALS and CeD (up to 15-fold).

At a conjunction FDR < 0.05, we identified several SNPs associated with both immune-mediated disease and CBD, PSP, or ALS (S4–S6 Figs; S2–S4 Tables). Few of the SNPs shared between CBD, PSP, or ALS and immune-mediated disease mapped to the HLA region. Only 2 PSP–immune SNPs mapped to the region of MLN and IRF4 on Chr 6, and no CBD–immune or ALS–immune SNPs mapped to the HLA region (S4–S6 Figs; S2–S4 Tables).

Beyond the HLA region, we found several overlapping loci between the immune- mediated diseases and CBD, PSP, and ALS (S4–S6 Figs; S2–S4 Tables). For PSP, these were (1) rs7642229 with CeD (Chr 3, nearest gene = XCR1, FDR = 1.74 × 10⁻²); (2) rs11718668 with CeD (Chr 3, nearest gene = TERC, FDR = 3.00 × 10⁻²); (3) rs12203592 with CeD (Chr 6, nearest gene = IRF4, FDR = 4.17 × 10⁻²); (4) rs1122554 with RA (Chr 6, nearest gene = MLN, FDR = 2.09 × 10⁻²); and (5) rs3748256 with RA (Chr 11, nearest gene = FAM76B, FDR = 2.09 × 10⁻²). For ALS, these were (1) rs3828599 with CeD (Chr 5, nearest gene = GPX3, FDR = 2.27 × 10⁻²) and (2) rs10488631 with RA (Chr 7, nearest gene = TNPO3, FDR = 3.42 × 10⁻²).

cis-eQTL expression

To investigate whether shared FTD–immune SNPs modify gene expression, we evaluated cis-eQTLs in both brain and blood tissue types. At a previously established conservative Bonferroni-corrected p-value < 3.9 × 10⁻⁵ [34], we found significant cis-associations between shared SNPs and genes in the HLA region on Chr 6 in peripheral blood mononuclear cells, lymphoblasts, and the human brain (see S5 Table for gene expression associated with each SNP). We also found that rs199533 and rs17572851 on Chr 17 were significantly associated with MAPT (p = 2 × 10⁻¹²) expression in the brain. Beyond the HLA and MAPT regions, notable cis-eQTLs included rs10784359 and LRRK2 (p = 1.40 × 10^{− 7}) and rs2192493 and TBKBP1 (p = 1.29 × 10⁻⁶) (see S5 Table).

Protein–protein and co-expression networks

We found physical interaction and gene co-expression networks for the FTD–immune pleiotropic genes with significant cis-eQTLs (at a Bonferroni-corrected p-value < 3.9 × 10⁻⁵). We found robust co-expression between various HLA classes, further suggesting that large portions of the HLA region, rather than a few individual loci, may be involved with FTD (Fig 4; S6 Table). Interestingly, we found that several non-HLA functionally expressed FTD–immune genes, namely, LRRK2, PGBD5, and TBKBP1, showed co-expression with HLA-associated genes (Fig 4).

Genetic expression in FTD brains compared to controls

To investigate whether the FTD–immune pleiotropic genes with significant cis-eQTLs are differentially expressed in FTD brains, we compared gene expression in FTD-U brains to that in brains from neurologically healthy controls. We found significantly different levels of HLA gene expression in FTD-U brains compared to control brains (Table 3). This was true of FTD-U brains regardless of progranulin gene (GRN) mutation status. In spite of the fact that the FTD GWAS used to identify these genes was based on patients with sporadic FTD (without GRN mutations), GRN mutation carriers showed the greatest differences in HLA gene expression (Fig 5; Table 3). These findings are compatible with prior work showing microglial-mediated immune dysfunction in GRN mutation carriers [3].

Microglial enrichment

For the FTD–immune pleiotropic genes with significant cis-eQTLs, across different central nervous system (CNS) cell types, we found significantly greater expression within microglia compared to neurons or fetal astrocytes (Fig 6A; Table 4). Interestingly, HLA genes showed the greatest degree of differential expression. Four of the FTD–immune HLA-associated genes, namely HLA-DRA, AOAH, HLA-A, and HLA-C, showed highest expression in microglia (ranging from 10 to 60 fragments per kilobase of transcript per million; see Fig 6B). In addition, MAPT was predominantly expressed in neurons, LRRK2 in microglia, PGBD5 in neurons, and TBKBP1 in fetal astrocytes (Figs 6B and S7–S9).

Discussion

We systematically assessed genetic overlap between 4 FTD-related disorders and several immune-mediated diseases. We found extensive genetic overlap between FTD and immune-mediated disease particularly within the HLA region on Chr 6, a region rich in genes associated with microglial function. This genetic enrichment was specific to FTD and did not extend to CBD, PSP, or ALS. Further, we found that shared FTD–immune gene variants were differentially expressed in FTD patients compared with controls, and in microglia compared with other CNS cells. Beyond the HLA region, by leveraging statistical power from large immune-mediated GWASs, we detected novel candidate FTD associations requiring validation within LRRK2, TBKBP1, and PGBD5. Considered together, these findings suggest that various microglia and inflammation-associated genes, particularly within the HLA region, may play a critical and selective role in FTD pathogenesis.

By combining GWASs from multiple studies and applying a pleiotropic approach, we identified genetic variants jointly associated with FTD-related disorders and immune-mediated disease. We found that the strength of genetic overlap with immune-mediated disease varies markedly across FTD-related disorders, with the strongest pleiotropic effects associated with FTD, followed by CBD and PSP, and the weakest pleiotropic effects associated with ALS. We identified 8 FTD- and immune-associated loci that mapped to the HLA region, a concentration of loci that was not observed for the other disorders. Indeed, only 2 PSP–immune pleiotropic SNPs and no CBD–immune or ALS–immune pleiotropic SNPs mapped to the HLA region. Previous work has identified particular HLA genes associated with CBD, PSP, and ALS [35,36]. In contrast, our current findings implicate large portions of the HLA region in the pathogenesis of FTD. Together, these results suggest that each disorder across the larger FTD spectrum has a unique relationship with the HLA region.

Our results also indicate that functionally expressed FTD–immune shared genetic variants are differentially expressed in FTD brains compared to controls and in microglia compared to other CNS cell types (Fig 6). Microglia play a role in the pathophysiology of GRN+ FTD. Progranulin is expressed in microglia [37], and GRN haploinsufficiency is associated with abnormal microglial activation and neurodegeneration [3]. It is perhaps expected, therefore, that GRN+ brains show differential expression of FTD–immune genes, even though these genetic variants were derived from a GWAS of patients with sporadic FTD (who lack GRN or other established FTD mutations). More surprising is the presence of similar enrichment in GRN− brains, suggesting that dysfunction of microglial-centered immune networks may be a common feature of FTD pathogenesis.

By leveraging statistical power from the large immune-mediated GWASs, we identified novel candidate FTD associations requiring validation within LRRK2, TBKBP1, and PGBD5 and confirmed previously shown FTD-associated signal within the MAPT region. LRRK2 mutations are a cause of Parkinson disease [38] and CD [39]. We previously described a potential link between FTD and the LRRK2 locus [40], and another study using a small sample showed that LRRK2 mutations may increase FTD risk [41]. Together, these results suggest that the extended LRRK2 locus might influence FTD despite common genetic variants within LRRK2 not reaching genome-wide significance in a large FTD GWAS [5]. We suggest that increased expression of LRRK2 in microglia results in proinflammatory responses, possibly by modulating TNF-alpha secretion [42]. TBKBP1 also modulates TNF-alpha signaling by binding to TBK1 (TANK binding kinase 1) [43]; rare pathogenic variants in TBK1 cause FTD-ALS [44–46]. Importantly, elevated CSF levels of TNF-alpha are a core feature of FTD [6,47]. Building on these findings, in our bioinformatics “network”-based analysis, we found that both LRRK2 and TBKBP1 interact with genes within the HLA region (Fig 4). Further, physical interactions between MAPT and the HLA network are compatible with research suggesting that under different conditions reactive microglia can either drive or mitigate tau pathology [48,49]. MAPT mutations, which disrupt the normal binding of tau protein to tubulin, account for a large proportion of familial FTD cases [50]. Together, these findings suggest that LRRK2, TBKBP1, and MAPT may, at least in part, influence FTD pathogenesis via HLA-related mechanisms.

This study has limitations. Particularly, in the original datasets that form the basis of our analysis, diagnosis of FTD was established clinically. Given the clinical overlap among neurodegenerative diseases, we cannot exclude the potential influence of clinical misdiagnosis. As such, our findings would benefit from confirmation in large pathologically confirmed cohorts. In addition, given the complex interconnectedness of the HLA region (see Fig 4), we also were not able to define the specific gene or genes on Chr 6 responsible for our pleiotropic signal. However, given the number of HLA loci associated with increased FTD risk, it may be the case that no single HLA variant will be clinically informative; rather, an additive combination of these microglia-associated genetic variants may better inform FTD risk. This possibility is supported by our observation that the expression levels of FTD-immune shared genetic variants differ on average between FTD brains and controls, but with considerable overlap between the two groups, again suggesting that no single variant is likely to be the determinant of FTD risk (Fig 5). Further, we acknowledge the lack of transcriptomic and epigenetic data that would help to identify possible causal associations and mechanisms driving our pleotropic signal.

In conclusion, across a large cohort (total n = 192,886 cases and controls), we used pleiotropy between FTD-related disorders and immune-mediated disease to identify several genes within the HLA region that are expressed within microglia and differentially expressed in the brains of patients with FTD. Building on prior work [6,7], our results support a disease model in which immune dysfunction is central to the pathophysiology of a subset of FTD cases. These findings have important implications for future work in FTD focused on monitoring microglial activation as a marker of disease progression and investigating anti-inflammatory treatments as modifiers of disease outcome.

Supporting Information

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