Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease

Machine learning in Alzheimer’s disease genetics

: Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations

A polygenic score (PGS) for Alzheimer’s disease (AD) was derived recently from data on genome-wide significant loci in European ancestry populations. We applied this PGS to populations in 17 European countries and observed a consistent association with the AD risk, age at onset and cerebrospinal fluid levels of AD biomarkers, independently of apolipoprotein E locus (APOE). This PGS was also associated with the AD risk in many other populations of diverse ancestries. A cross-ancestry polygenic risk score improved the association with the AD risk in most of the multiancestry populations tested when the APOE region was included. Finally, we found that the PGS/polygenic risk score captured AD-specific information because the association weakened as the diagnosis was broadened. In conclusion, a simple PGS captures the AD-specific genetic information that is common to populations of different ancestries, although studies of more diverse populations are still needed to better characterize the genetics of AD

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues

Excessive self-grooming, gene dysregulation and imbalance between the striosome and matrix compartments in the striatum of Shank3 mutant mice

International audienceAutism is characterized by atypical social communication and stereotyped behaviors. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are detected in 1–2% of patients with autism and intellectual disability, but the mechanisms underpinning the symptoms remain largely unknown. Here, we characterized the behavior of Shank3 Δ11/Δ11 mice from 3 to 12 months of age. We observed decreased locomotor activity, increased stereotyped self-grooming and modification of socio-sexual interaction compared to wild-type littermates. We then used RNAseq on four brain regions of the same animals to identify differentially expressed genes (DEGs). DEGs were identified mainly in the striatum and were associated with synaptic transmission (e.g., Grm2, Dlgap1 ), G-protein-signaling pathways (e.g., Gnal , Prkcg1 , and Camk2g ), as well as excitation/inhibition balance (e.g., Gad2 ). Downregulated and upregulated genes were enriched in the gene clusters of medium-sized spiny neurons expressing the dopamine 1 (D1-MSN) and the dopamine 2 receptor (D2-MSN), respectively. Several DEGs ( Cnr1 , Gnal , Gad2 , and Drd4 ) were reported as striosome markers. By studying the distribution of the glutamate decarboxylase GAD65, encoded by Gad2 , we showed that the striosome compartment of Shank3 Δ11/Δ11 mice was enlarged and displayed much higher expression of GAD65 compared to wild-type mice. Altogether, these results indicate altered gene expression in the striatum of Shank3 -deficient mice and strongly suggest, for the first time, that the excessive self-grooming of these mice is related to an imbalance in the striatal striosome and matrix compartments

Excessive self-grooming of Shank3 mutant mice is associated with gene dysregulation and imbalance between the striosome and matrix compartments in the striatum

Abstract Autism spectrum disorders (ASD) are characterised by atypical social communication and stereotyped behaviours. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are detected in 1-2% of patients with ASD and intellectual disability (ID), but the mechanisms underpinning the symptoms remain largely unknown. Here, we characterized the behaviour of Shank3 mutant mice deleted for exon 11 ( Shank3 Δ11/Δ11 ) from three to twelve months of age. We observed decreased locomotor activity, increased stereotyped self-grooming and atypical socio-sexual interaction compared to wild-type littermates. We then used RNAseq on four brain regions of the same animals to identify differentially expressed genes (DEG). DEGs were identified mainly in the striatum and were associated with synaptic transmission (e.g. Grm2 , Dlgap1 ), G-protein-signalling pathways (e.g. Gnal , Prkcg1 , and Camk2g ), as well as excitation/inhibition balance (e.g. Gad2 ). Downregulated and upregulated genes were respectively enriched in the gene clusters of medium-sized spiny neurons expressing the dopamine 1 (D1-MSN) and the dopamine 2 receptor (D2-MSN). Moreover, expression of DEGs reported as striosome markers within the striatum ( Cnr1 , Gnal1 , Gad2 , and Drd4 ) were positively correlated with excessive self-grooming. Finally, we showed that the striosome compartment of Shank3 Δ11/Δ11 mice was enlarged and displayed higher expression of GAD65 compared to wild-type mice. Altogether, these results shed light on a possible role of the striosomes/matrix imbalance in excessive self-grooming in Shank3 Δ11/Δ11 mice. Such striatal alterations could be present in a subgroup of patients with ASD and ID and could open the way to new therapeutic approaches

Excessive self-grooming, gene dysregulation and imbalance between the striosome and matrix compartments in the striatum of<i>Shank3</i>mutant mice

AbstractAutism is characterised by atypical social communication and stereotyped behaviours. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are detected in 1-2% of patients with autism and intellectual disability (ID), but the mechanisms underpinning the symptoms remain largely unknown. Here, we characterised the behaviour ofShank3Δ11/Δ11mice from three to twelve months of age. We observed decreased locomotor activity, increased stereotyped self-grooming and modification of socio-sexual interaction compared to wild-type littermates. We then used RNAseq on four brain regions of the same animals to identify differentially expressed genes (DEG). DEGs were identified mainly in the striatum and were associated with synaptic transmission (e.g.Grm2, Dlgap1), G-protein-signalling pathways (e.g.Gnal, Prkcg1, and Camk2g), as well as excitation/inhibition balance (e.g.Gad2). Downregulated and upregulated genes were enriched in the gene clusters of medium-sized spiny neurons expressing the dopamine 1 (D1-MSN) and the dopamine 2 receptor (D2-MSN), respectively. Several DEGs (Cnr1, Gnal1, Gad2, and Drd4) were reported as striosome markers. By studying the distribution of the glutamate decarboxylase GAD65, encoded byGad2, we showed that the striosome compartment ofShank3Δ11/Δ11mice was enlarged and displayed much higher expression of GAD65 compared to wild-type mice. Altogether, these results indicate altered gene expression in the striatum of SHANK3-deficient mice and strongly suggest, for the first time, that the impairment in behaviour of these mice are related to an imbalance striosomes/matrix.</jats:p

Méthodes de partitionnements pour détecter des structures fines de population et applications au projet POPGEN

International audienceIntroduction: To identify genetic risk factors for multifactorial disease, it is essential to compare the genomes of patients with those of genetically similar healthy individuals. It is therefore crucial to understand the genetic structure of the overall population. One important way of gaining such understanding is by applying clustering methods whose aim is to identify groups of individuals based on their genomes.Methods: In this context, we present a comparative analysis of various clustering approaches, with a focus on hierarchical methods such as fineSTRUCTURE, model-based clustering approaches such as Mclust, and aggregation-based clustering techniques. We also investigate the impact of different similarity measures obtained through haplotype-sharing methods on clustering outcomes.Results: We enhance previous comparative studies by evaluating clustering methods in the context of fine-scale population structure by simulating data that aligns with the observed population structure in French populations. This approach enables us to gauge the robustness and accuracy of various methods using simulated datasets. Additionally, we apply these methods to real data from POPGEN, a project encompassing the entire metropolitan territory of France and aggregating precise genetic and geographical information from over 9,772 volunteers. We investigate how the genetic clusters observed in POPGEN correspond to the fine-scale geography within different regions of France.Conclusion: Our study serves to demonstrate the performance of different clustering approaches on both simulated and real datasets, offering insights to help choose the most suitable clustering methods for identifying fine-scale population structure.Funding: This work is funded by the French Ministry of Research for the POPGEN project in the framework of the French initiative for genomic medicine (Plan France Médecine Génomique 2025; PFMG 2025; https://pfmg2025.aviesan.fr). The CONSTANCES cohort benefits from grant ANR-11INBS-0002 from the French National Research Agency