Hidden heterogeneity in Alzheimer's disease: Insights from genetic association studies and other analyses

Abstract

Despite evident success in clarifying many important features of Alzheimer's disease (AD) the efficient methods of its prevention and treatment are not yet available. The reasons are likely to be the fact that AD is a multifactorial and heterogeneous health disorder with multiple alternative pathways of disease development and progression. The availability of genetic data on individuals participated in longitudinal studies of aging health and longevity, as well as on participants of cross-sectional case-control studies allow for investigating genetic and non-genetic connections with AD and to link the results of these analyses with research findings obtained in clinical, experimental, and molecular biological studies of this health disorder. The objective of this paper is to perform GWAS of AD in several study populations and investigate possible roles of detected genetic factors in developing AD hallmarks and in other health disorders. The data collected in the Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), Health and Retirement Study (HRS) and Late Onset Alzheimer's Disease Family Study (LOADFS) were used in these analyses. The logistic regression and Cox's regression were used as statistical models in GWAS. The results of analyses confirmed strong associations of genetic variants from well-known genes APOE, TOMM40, PVRL2 (NECTIN2), and APOC1 with AD. Possible roles of these genes in pathological mechanisms resulting in development of hallmarks of AD are described. Many genes whose connection with AD was detected in other studies showed nominally significant associations with this health disorder in our study. The evidence on genetic connections between AD and vulnerability to infection, as well as between AD and other health disorders, such as cancer and type 2 diabetes, were investigated. The progress in uncovering hidden heterogeneity in AD would be substantially facilitated if common mechanisms involved in development of AD, its hallmarks, and AD related chronic conditions were investigated in their mutual connection.

Keywords: Amyloid cascade hypothesis; BBB; Cancer; Diabetes; Neurodegeneration; Neuroinflammation; Neuronal death; Pleiotropy; Response to infection; Synapse loss.

Fig. 1

Left panel. The QQ-plots of the results of GWAS of Alzheimer’s disease obtained in the analyses using logistic regression (GLIMMIX in LOADFS) for male and females combined. a). CHS (case: 286; control: 4732); b). FHS (case: 308; control: 3343); c. HRS (case: 656; control: 8768); d). LOADFS (case: 2319; control: 2242). Right panel. Corresponding Manhattan plots for the same analysis as shown on the left panel.

Fig. 2

Left panel. The QQ-plots of the results of GWAS of Alzheimer’s disease obtained in the analyses using Cox regression. a). CHS; b). FHS; c). HRS. Right panel. Corresponding Manhattan plots for the same analysis on the left panel.

Fig. 3

Left panel. The Venn diagrams of intersection of SNPs obtained from GWAS analyses on HRS white individuals using logistic regression: a). between Alzheimer’s/dementia (AD) and cancers (non-skin); b). between Alzheimer’s/dementia (AD) and diabetes; c). between cancers (non-skin) and diabetes. Right panel. Distribution of the overlapped SNPs by the effects corresponding to the left panel.