Untangling Genetic Risk for Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is a genetically heterogeneous neurodegenerative disorder caused by fully penetrant single gene mutations in a minority of cases, while the majority of cases are sporadic or show modest familial clustering. These cases are of late onset and likely result from the interaction of many genes and the environment. More than 30 loci have been implicated in AD by a combination of linkage, genome-wide association, and whole genome/exome sequencing. We have learned from these studies that perturbations in endolysosomal, lipid metabolism, and immune response pathways substantially contribute to sporadic AD pathogenesis. We review here current knowledge about functions of AD susceptibility genes, highlighting cells of the myeloid lineage as drivers of at least part of the genetic component in late-onset AD. Although targeted resequencing utilized for the identification of causal variants has discovered coding mutations in some AD-associated genes, a lot of risk variants lie in noncoding regions. Here we discuss the use of functional genomics approaches that integrate transcriptomic, epigenetic, and endophenotype traits with systems biology to annotate genetic variants, and to facilitate discovery of AD risk genes. Further validation in cell culture and mouse models will be necessary to establish causality for these genes. This knowledge will allow mechanism-based design of novel therapeutic interventions in AD and promises coherent implementation of treatment in a personalized manner.

Keywords: Alzheimer’s disease; Endolysosomal pathway; Functional genomics; Genome-wide association studies; Immune response; Lipid metabolism.

Figure 1. Schematic representation of a multidimensional approach for fine-mapping risk variants in Alzheimer’s disease-associated loci

(A) An illustration of a locus-specific association signal from a genome-wide association study (GWAS) of Alzheimer’s disease (AD), e.g. Manhattan association plot (top panel). Each dot represents a single nucleotide polymorphism (SNP), with the X-axis showing the chromosomal position and Y-axis showing the association P values on the -log₁₀ scale. SNPs are colored (in red) by pairwise linkage disequilibrium (LD) pattern with the most strongly associated SNP. The regional association signal from a quantitative trait loci (QTL) study in specific cell populations (e.g., peripheral monocytes or macrophages) (middle panel), that may show expression, splicing and methylation QTL. The functional annotation of the genome with histone marks and the genomic position with the genes (bottom panel), e.g. H3K4me3 marks promoter regions, H3K27ac marks enhancer regions. (B) Classification of SNPs in coding and non-coding regions by mechanism of action that may affect expression, splicing or protein function due to mutations, insertions and deletions. The coding variants change protein sequence. The non-coding variants may influence protein levels by modulating transcription factor binding at the intronic/distal enhancer or promoter regions, changing histone methylation and acetylation, splicing, miRNA, long noncoding RNA binding and stability, or structural variation. TF: transcription factor. UTR: untranslated region. (C) Gene co-expression network and pathway analysis using large-scale transcriptomic or proteomic datasets. (D) Endophenotypes relevant to AD: amyloid-β (Aβ) plaque load measured with positron emission tomography (PET) tracer, Aβ42 and tau/p-tau₁₈₁ levels in cerebrospinal fluid (CSF), neuropathological changes. (E) Functional validation of genetic hits using genome editing tools (e.g. CRISPR/Cas9) in cell culture and mouse models. (F) Integrating AD GWAS with functional genomics approaches can help prioritize candidate genes, biological pathways and cell types, which in turn can help generate novel hypothesis for experimental validation. Genes listed are discussed in the review.