A global view of the genetic basis of Alzheimer disease

Abstract

The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.

Fig. 1 |. Genetic loci associated with Alzheimer disease in genome-wide association studies of non-Hispanic white individuals.

Manhattan plot from a study by Bellenguez and colleagues showing the identified genetic loci associated with Alzheimer disease in non-Hispanic white individuals. P values are two-sided raw P values derived from fixed-effect meta-analysis. The threshold for genome-wide significance (P = 5 × 10⁻⁸) is indicated by the red dashed line, and the suggestive threshold for genome-wide significance (P = 1 < 10⁻⁵) is indicated by the black dashed line. Loci are named for the closest gene to the sentinel variant for each locus. Loci newly identified by Bellenguez et al. are shown in red, whereas loci previously reported are shown in light and dark blue. Adapted from ref. , Springer Nature Limited.

Fig. 2 |. Timeline and routes of human migration inferred from genomic data.

Dashed lines represent routes of migration that remain controversial. CA, Central Anatolia; FC, Fertile Crescent; IP, Iberian Peninsula; kyr, thousand years; PCS, Pontic-Caspian steppe. Adapted from ref. , Springer Nature Limited.

Fig. 3 |. Admixture mapping of Alzheimer disease in Caribbean Hispanic individuals.

Manhattan plots of analyses conducted in admixMap. Model 1 (upper panel) is adjusted for age, sex, genotype batch, principal components for population stratification, and kinship. Model 2 (lower panel) is in addition adjusted for APOE genotype. The red highlighted parts represent the identified ancestral blocks significant after multiple testing correction. Adapted from ref. , Springer Nature Limited.

Fig. 4 |. Prevalence of dementia in Africa.

Heat map showing the wide range of dementia prevalence in African countries determined over the past 25 years. Dementia prevalence studies have also been conducted in Senegal and Kenya, but the data are not yet published. Adapted with permission from ref. , Wiley.

Fig. 5 |. Pathology in clinically diagnosed Alzheimer disease.

The proportions of other types of pathological changes in individuals with clinically diagnosed Alzheimer disease (AD). The data are from a publication by DeTure and Dickson, in which they describe the pathological evaluation of 626 individuals from the Mayo Clinic Brain Bank. ‘Not AD’ indicates a completely distinct form of dementia.

Fig. 6 |. An integrated multiomics approach.

Integrated multiomics approaches can be used to understand how genetic variation leads to disease, to establish which molecular pathways are altered, and to identify new therapeutic targets and diagnostic approaches. Expression quantitative trait loci (eQTLs) would be generated from each omics layer (gene expression from the transcriptome, protein quantitative traits from the proteome, methylation quantitative traits from the metabolome). These multiomics layers would then be integrated into a systems analysis that includes information on the genome and epigenome, with the goal of furthering understanding of disease mechanisms and developing novel treatments and diagnostics.

Most promising blood-based biomarkers for AD by disease stage.

Adapted from ref. , CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).