Multi-ancestry meta-analysis and fine-mapping in Alzheimer’s Disease

Abstract

Genome-wide association studies (GWAS) of Alzheimer's disease are predominantly carried out in European ancestry individuals despite the known variation in genetic architecture and disease prevalence across global populations. We leveraged published and de novo GWAS from European, East Asian, African American, and Caribbean Hispanic populations to perform the largest multi-ancestry GWAS meta-analysis of Alzheimer's disease to date. This method allowed us to identify two independent novel disease-associated loci on chromosome 3. We also leveraged diverse haplotype structures to fine-map nine loci and globally assessed the heterogeneity of known risk factors across populations. Additionally we compared the generalizability of multi-ancestry- and single-ancestry-derived polygenic risk scores in a three-way admixed Colombian population. Our findings highlight the importance of multi-ancestry representation in uncovering and understanding putative factors that contribute to Alzheimer's disease risk.

An AluYb8 retrotransposon characterises a risk haplotype of TMEM106B associated in neurodegeneration

“…Lastly, a comparison of complete haplotype sequences between European and African genomes revealed four major haplotype groups in the TMEM106B locus, two of which are dominantly defined by African individuals, explaining why linkage disequilibrium (LD) of the risk alleles is reduced in TMEM106B in genomes with African ancestry. Therefore, next to the proposed mechanism of the TMEM106B risk haplotype, this study also highlights how the lack of complete haplotype characterisations in other ancestral populations leads to a limited understanding of neurodegenerative risk loci in non-Europeans 77,78 . Ongoing efforts to generate long read sequencing data from diverse population-backgrounds 28 will enable rigorous evaluation of the evolutionary development of disease-associated haplotypes, and ultimately for population specific disease risk-assessments.…”

Artificial intelligence for dementia genetics and omics

“…There is currently a marked lack of diversity within dementia genetics studies, with GWAS discovery being largely confined to the genetics of AD in non‐Hispanic White adults of European ancestry. Although some small GWAS have been conducted in non‐European samples, 20,21–23 have measured non‐AD dementias, 6,9,10 and incorporated dementia‐related intermediate quantitative phenotypes or endophenotypes (such as amyloid‐beta and cerebral small vessel disease), 24–26 these studies are largely underpowered. Certain ancestries remain understudied, for example, South Asians despite representing around a quarter of the total global population.…”

“…Lake and colleagues leverage genetically quantified admixture and random effects models in a population with complex substructures using both random-effects derived risk scores and a risk heuristic that leverages the rates of genetic admixture to build a better predictive model. 22…”

“…Recent work in multi‐ancestry PRS is a good first step in the right direction, 42 but with larger sample sizes of participant level data, a ML approach could perform well. Lake and colleagues leverage genetically quantified admixture and random effects models in a population with complex substructures using both random‐effects derived risk scores and a risk heuristic that leverages the rates of genetic admixture to build a better predictive model 22 …”

“…Although some small GWAS have been conducted in non-European samples, 20,[21][22][23] have measured non-AD dementias, 6,9,10 and incorporated dementia-related intermediate quantitative phenotypes or endophenotypes (such as amyloid-beta and cerebral small vessel disease), [24][25][26] The study of both coding and non-coding rare/structural variants associated with dementia risk needs to be further pursued through short-and long-read sequencing technologies, which are thought to be important contributors to missing heritability in dementia. 27 Under the hood, long-read sequencing is powered by DL, using GPUpowered alignment algorithms to better characterize the genome.…”