Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system.

BACKGROUND: Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. METHODS: We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. RESULTS: MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. CONCLUSIONS: Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

The genetic overlap between Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease.

Neurodegenerative diseases are a group of disorders characterized by neuronal cell death causing a variety of physical and mental problems. While these disorders can be characterized by their phenotypic presentation within the nervous system, their aetiologies differ to varying degrees. The majority of previous genetic evidence for overlap between neurodegenerative diseases has been pairwise. In this study, we aimed to identify overlap between the 4 investigated neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease) at the variant, gene, genomic locus, gene-set, cell, or tissue level, with specific interest in overlap between 3 or more diseases. Using local genetic correlation, we found 2 loci (TMEM175 and HLA) that were shared across 3 disorders. We also highlighted genes, genomic loci, gene sets, cell types, and tissue types which may be important to 2 or more disorders by analyzing the association of variants with a common factor estimated from the 4 disorders. Our study successfully highlighted genetic loci and tissues associated with 2 or more neurodegenerative diseases.

Rare variant aggregation in 148,508 exomes identifies genes associated with proxy dementia.

Proxy phenotypes allow for the utilization of genetic data from large population cohorts to analyze late-onset diseases by using parental diagnoses as a proxy for genetic disease risk. Proxy phenotypes based on parental diagnosis status have been used in previous studies to identify common variants associated with Alzheimer's disease. As of yet, proxy phenotypes have not been used to identify genes associated with Alzheimer's disease through rare variants. Here we show that a proxy Alzheimer's disease/dementia phenotype can capture known Alzheimer's disease risk genes through rare variant aggregation. We generated a proxy Alzheimer's disease/dementia phenotype for 148,508 unrelated individuals of European ancestry in the UK biobank in order to perform exome-wide rare variant aggregation analyses to identify genes associated with proxy Alzheimer's disease/dementia. We identified four genes significantly associated with the proxy phenotype, three of which were significantly associated with proxy Alzheimer's disease/dementia in an independent replication cohort consisting of 197,506 unrelated individuals of European ancestry in the UK biobank. All three of the replicated genes have been previously associated with clinically diagnosed Alzheimer's disease (SORL1, TREM2, and TOMM40/APOE). We show that proxy Alzheimer's disease/dementia can be used to identify genes associated with Alzheimer's disease through rare variant aggregation.

Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers.

Amyloid-beta 42 (Aß42) and phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) reflect core features of the pathogenesis of Alzheimer's disease (AD) more directly than clinical diagnosis. Initiated by the European Alzheimer & Dementia Biobank (EADB), the largest collaborative effort on genetics underlying CSF biomarkers was established, including 31 cohorts with a total of 13,116 individuals (discovery n = 8074; replication n = 5042 individuals). Besides the APOE locus, novel associations with two other well-established AD risk loci were observed; CR1 was shown a locus for Aß42 and BIN1 for pTau. GMNC and C16orf95 were further identified as loci for pTau, of which the latter is novel. Clustering methods exploring the influence of all known AD risk loci on the CSF protein levels, revealed 4 biological categories suggesting multiple Aß42 and pTau related biological pathways involved in the etiology of AD. In functional follow-up analyses, GMNC and C16orf95 both associated with lateral ventricular volume, implying an overlap in genetic etiology for tau levels and brain ventricular volume.

Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health.

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.