Genetic architecture of common non-Alzheimer's disease dementias

Abstract

Frontotemporal dementia (FTD), dementia with Lewy bodies (DLB) and vascular dementia (VaD) are the most common forms of dementia after Alzheimer's disease (AD). The heterogeneity of these disorders and/or the clinical overlap with other diseases hinder the study of their genetic components. Even though Mendelian dementias are rare, the study of these forms of disease can have a significant impact in the lives of patients and families and have successfully brought to the fore many of the genes currently known to be involved in FTD and VaD, starting to give us a glimpse of the molecular mechanisms underlying these phenotypes. More recently, genome-wide association studies have also pointed to disease risk-associated loci. This has been particularly important for DLB where familial forms of disease are very rarely described. In this review we systematically describe the Mendelian and risk genes involved in these non-AD dementias in an effort to contribute to a better understanding of their genetic architecture, find differences and commonalities between different dementia phenotypes, and uncover areas that would benefit from more intense research endeavors.

Fig. 1.

Genetic architecture of FTD. Circos plot of Mendelian and risk genes with confirmed roles in FTD. The links between genes and the colored outer layer correspond to biological processes annotated from Gene Ontology terms as obtained from GOSlim (https://go.princeton.edu/cgi-bin/GOTermMapper) which prioritizes annotations based on a previously curated subset of GO terms. The terms shown here annotate genes in all three dementias to better highlight the functional differences between these diseases. Here we can see genes in FTD are well dispersed throughout the genome and have roles in a wide range of functions, with many of these genes having annotations in a variety of biological processes.

Fig. 2.

Survival analyses of age at death by means of Kaplan–Meier curves for APOE in an international cohort of 679 DLB cases. On the left, the effect on age at death of the presence/absence of the APOE ε4 allele is shown. The analysis showed a significant difference in the ages at death between carriers and non-carriers of the APOE ε4 risk allele. On the right panel the effect on age at death of the different APOE haplotypes was found to be significantly different groups.

Fig. 3.

Genetic architecture of DLB. Circos plot of genes with established roles in DLB. As in the FTD circos plot, links and colored outer layer represent GOSlim biological processes which are shared by all three dementias. While the number of genes with conclusive roles in DLB are much fewer than other dementias, these genes are involved in a wide variety of biological functions.

Fig. 4.

Genetic architecture of VaD. Circos plot of established VaD genes. Links and colored outer layer were obtained from biological process Gene Ontology annotations shared by all three dementias as described in Fig. 1. Given the burden of proof for genetic risk associations with the different genes studied in VaD we only considered APOE to be represented here. All other genes are Mendelian causes of hereditary vasculopathies which typically cause arteriopathy and microvascular disease leading to vascular cognitive impairment and dementia.

Fig. 5.

Posterior decoding of genetic effect direction and strength of evidence for the rs429358 (APOE ε4) and rs7412 (APOE ε2) variants in APOE. In this figure the International Classification of Diseases, tenth revision (ICD-10) is depicted as a radial tree where the first orbit represents the 22 ICD-10 chapters, followed by an orbit representing blocks of categories, and then by 2 consecutive orbits representing the ICD-10 categories including the observed annotation codes. This TreeWAS (https://treewas.org) refers to a Bayesian approach for mapping genetic risk across disease classification codes within a hierarchical ontology (Cortes et al., 2017). Each genetic variant in the TreeWAS Database was analysed to infer its association with a given diagnostic term. These terms are structured hierarchically, where a given term (ICD-10 clinical code) is nested within a broader disease/disorder category. Here, this tree structure is displayed with the “top node” or “root” as a circle in the middle and nested terms subtend from the root or their parent terms outwards. Each node is colored according to its dominant effect; protective (blue), risk (red), or no effect (gray) posterior probability, computed as Δp = p(protective) − p(risk). The analyses represented here relate to hospitalization episode statistics provided by the UK Biobank as a source of phenotypic data and is derived from linkage with the hospital episode statistics registry (UK Biobank data fields 41142 and 41078).