Genetic Risk Variants Research Articles

Enhancing the utility of polygenic scores in Alzheimer’s disease through systematic curation and annotation

IntroductionPolygenic Scores (PGSs) assess cumulative genetic risk variants that contribute to the association with complex diseases like Alzheimer’s Disease (AD). The PGS Catalog is a valuable repository of PGSs of various complex diseases, but it lacks standardized annotations and harmonization, making the information difficult to integrate for a specific disease.MethodsIn this study, we curated 44 PGS datasets for AD from the PGS Catalog, categorized them into five methodological groups, and annotated 813,257 variants to nearby genes. We aligned the scores based on the “GWAS significant variants” (GWAS-SV) method with the GWAS Catalog and flagged redundant files and those with a “limited scope” due to insufficient external GWAS support. Using rank aggregation (RA), we prioritized consistently important variants and provided an R package, “PgsRankRnnotatR,” to automate this process.ResultsOf the six RA methods evaluated, “Dowdall” method was the most robust. Our refined dataset, enhanced by multiple RA options, is a valuable resource for AD researchers selecting PGSs or exploring AD-related genetic variants.DiscussionOur approach offers a framework for curating, harmonizing, and prioritizing PGS datasets, improving their usability for AD research. By integrating multiple RA methods and automating the process, we provide a flexible tool that enhances PGS selection and genetic variant exploration. This framework can be extended to other complex diseases or traits, facilitating broader applications in genetic risk assessment.

Contributions of Genetic Variation in Astrocytes to Cell and Molecular Mechanisms of Risk and Resilience to Late-Onset Alzheimer's Disease.

Reactive astrocytes are associated with Alzheimer's disease (AD), and several AD genetic risk variants are associated with genes highly expressed in astrocytes. However, the contribution of genetic risk within astrocytes to cellular processes relevant to the pathogenesis of AD remains ill-defined. Here, we present a resource for studying AD genetic risk in astrocytes using a large collection of induced pluripotent stem cell (iPSC) lines from deeply phenotyped individuals with a range of neuropathological and cognitive outcomes. IPSC lines from 44 individuals were differentiated into astrocytes followed by unbiased molecular profiling using RNA sequencing and tandem mass tag-mass spectrometry. We demonstrate the utility of this resource in examining gene- and pathway-level associations with clinical and neuropathological traits, as well as in analyzing genetic risk and resilience factors through parallel analyses of iPSC-astrocytes and brain tissue from the same individuals. Our analyses reveal that genes and pathways altered in iPSC-derived astrocytes from individuals with AD are concordantly dysregulated in AD brain tissue. This includes increased levels of prefoldin proteins, extracellular matrix factors, COPI-mediated trafficking components and reduced levels of proteins involved in cellular respiration and fatty acid oxidation. Additionally, iPSC-derived astrocytes from individuals resilient to high AD neuropathology show elevated basal levels of interferon response proteins and increased secretion of interferon gamma. Correspondingly, higher polygenic risk scores for AD are associated with lower levels of interferon response proteins in astrocytes. This study establishes an experimental system that integrates genetic information with a matched iPSC lines and brain tissue data from a large cohort of individuals to identify genetic contributions to molecular pathways affecting AD risk and resilience.

Single-cell decoding of human islet cell type-specific alterations in type 2 diabetes reveals converging genetic- and state-driven β-cell gene expression defects.

Pancreatic islets maintain glucose homeostasis through coordinated action of their constituent endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single cell transcriptome profiling of 245,878 human islet cells from a 48-donor cohort spanning non-diabetic (ND), pre-diabetic (PD), and T2D states, identifying 14 distinct cell types detected in every donor from each glycemic state. Cohort analysis reveals ∼25-30% loss of functional beta cell mass in T2D vs. ND or PD donors resulting from (1) reduced total beta cell numbers/proportions and (2) reciprocal loss of 'high function' and gain of senescent β-cell subpopulations. We identify in T2D β-cells 511 differentially expressed genes (DEGs), including new (66.5%) and validated genes (e.g., FXYD2, SLC2A2, SYT1 ), and significant neuronal transmission and vitamin A metabolism pathway alterations. Importantly, we demonstrate newly identified DEG roles in human β-cell viability and/or insulin secretion and link 47 DEGs to diabetes-relevant phenotypes in knockout mice, implicating them as potential causal islet dysfunction genes. Additionally, we nominate as candidate T2D causal genes and therapeutic targets 27 DEGs for which T2D genetic risk variants (GWAS SNPs) and pathophysiology (T2D vs. ND) exert concordant expression effects. We provide this freely accessible atlas for data exploration, analysis, and hypothesis testing. Together, this study provides new genomic resources for and insights into T2D pathophysiology and human islet dysfunction.

DOP134 Uncovering genetic archetypes to explain patient heterogeneity in IBD

Abstract Background Genome-wide association studies (GWAS) have identified 300 loci linked to inflammatory bowel disease (IBD) risk, underscoring the genetic complexity of the disease. But these findings do not explain the significant clinical heterogeneity observed across IBD patients. Without clear biomarkers to define subtypes, the clinical impact of these genetic variants remains limited. We hypothesized that distinct genetic archetypes underlie the variability in symptoms observed among patients. We developed a battery of polygenic risk scores to capture these genetic profiles and explore their potential for more precise patient stratification. Methods We applied a three-step strategy to develop partitioned polygenic risk scores (PRS) to characterize distinct genetic profiles. First, we used a phenome-wide association approach to classify genetic risk variants into non-overlapping clusters based on their association with traits related with IBD. Second, we used functional genomics datasets to identify partitioned PRSs based on similarity in gene enhancer landscapes, gene expression changes across IBD-relevant tissues, co-expression networks in patients, and publicly available gene ontologies and biological pathways. Finally, we used a genetic algorithm to integrate the abovementioned molecularly-based PRSs and assign patients into independent clusters. This algorithm utilized artificial intelligence principles based on genetic programming, focusing on optimizing the discovery of groups of patients with most genetic differences among them. Results Our analyses identified distinct genetic archetypes in IBD patients, revealing clusters associated with specific biological pathways. The phenome-wide approach classified genome-wide significant variants into seven non-overlapping clusters, some linked to inflammatory (e.g. multiple sclerosis) and behavioral traits (e.g. sedentary behaviour) that are relevant to the comorbidity patterns observed in each patient. Through functionally-informed partitioning of genetic risk, we proposed 18,470 novel pathway-specific risk scores adapted for IBD. Notably, 25% of them significantly differentiated patients from controls. Finally, the genetic algorithm integrating these pathway-specific PRSs uncovered patient clusters with unique genetic profiles, highlighting the diverse biological underpinnings of IBD heterogeneity. Conclusion By combining GWAS results, functional genomics, and AI algorithms, we uncovered distinct genetic archetypes in IBD patients that reflect diverse biological pathways, including inflammatory and behavioral traits. This strategy improves our understanding of the genetic underpinnings of IBD heterogeneity and may serve to lay the groundwork for more precise patient stratification.

DOP129 Integrating genetic risk and omics insights to characterize the molecular alterations in inflammatory bowel disease

Abstract Background High-throughput omic technologies have enabled detailed molecular profiling of IBD patients. Traditionally, studies focus on a single molecular layer (e.g., genomics, transcriptomics, proteomics) to understand the complex interactions driving IBD. We can distinguish two strategies: 1)Genetic studies, such as genome-wide association studies (GWAS), which have identified >300 SNPs linked to increased IBD risk. 2)Omic profiling of patient tissues, which identifies hundreds of genes and molecular pathways altered in disease. We performed integrative analysis to explore consistency across omic findings and to obtain a synthetic picture of the sequence of molecular events that lead to IBD. Methods We used three approaches to clarify the interplay between genes identified through GWAS and those identified through other omics. First, we used Mendelian Randomization to infer causality and determine the directionality of interactions between the genes identified through all strategies. We incorporated 220 additional traits using summary statistics from the GWAS Atlas to validate our findings. Second, we assessed the impact of environmental exposures, such as smoking and diet. Finally, we used polygenic risk scores (PRS) to explore if variability in genetic risk shapes the differences in omic findings observed across patients. Results We made four relevant findings regarding the molecular changes in IBD. First, we noted low overlap between genes involved in genetic risk and those altered in patient samples. For instance, only 7% of genes identified by GWAS are differentially expressed at the transcriptomic/proteomic levels in IBD, and only 8% of genes identified by omics are significant in GWAS results. This trend was consistent across omic technologies and disease stages. Second, we infer that genes involved in genetic risk often modulate the expression levels of genes altered in patients. This trend is not bidirectional, implying that GWAS genes are drivers of the downstream transcriptional changes that characterize patients. Third, alterations in patients do not strategy according to individual PRS, suggesting that genetic effects lead to alterations pervasively shared across patients. Finally, environmental risk exposures, such as smoking or diet, do not impact GWAS genes (p=0.91) but have ample effects on omics-identified genes (p=1.8 x10-8). Conclusion Genetic risk variants identified by GWAS act as upstream regulators of the main patient alterations, while environmental risk factors exert independent effects on these alterations. The model suggests that omic research does not uncover causal alterations, but reveals a convergent endpoint that characterizes IBD patients, regardless of their genetic predisposition and external exposures.

Genetic and neurobiological aspects of psychosis in major neurocognitive disorder.

Psychiatric symptoms are frequent in neurocognitive disorders and dementias. Psychotic symptoms, mainly hallucinations and delusions, may appear in up to 50% of cases, influencing morbidity and mortality. Genetic, neurobiological, and environmental factors are involved in their onset. We conducted a narrative review of primary articles developed in humans that analyzed the genetic and neurobiological basis of psychosis in dementias. Evidence suggests that there are genetic risk variants for presenting psychosis in dementia. How genetic variants are related to schizophrenia, dementia, and other neurodegenerative disorders is under discussion. Candidate gene studies have found and genetic variants are associated with psychosis in dementia while genome-wide association studies have shown variants located in y . Epigenetic studies are scarce but have detected differences in the methylome of people with dementia and psychosis. On the other hand, alterations of the cholinergic, serotonergic, dopaminergic, and gabaergic neurotransmitter systems and the excitatory-inhibitory balance have been described in dementia. From a functional and anatomical point of view, there are alterations in several regions, mainly in the frontal area and other sensory processing and integration areas. Finally, we describe the influence of cognitive alterations in the genesis and maintenance of delusions and discuss the phenomenological overlap with confabulations. Multiple genetic, neurobiological, structural, and cognitive factors influence the occurrence of delusions and hallucinations in persons with dementia. Further research is needed to understand the pathophysiology of psychosis in dementias. This approach would support the understanding of psychosis as a transdiagnostic entity.

Impact of TCF7L2 rs7903146 on clinical presentation and risk of complications in patients with type 2 diabetes.

TCF7L2 rs7903146 is the most impactful single genetic risk variant for type 2 diabetes. However, its role on disease progression, complications and mortality among people with type 2 diabetes at diagnosis remains unclear. We assessed the per allele impact of the rs7903146 T-allele on clinical characteristics and complication risk in 9231 individuals with type 2 diabetes at diagnosis and over a 10-year follow-up period. Log-binomial and robust Poisson regression analyses were used to estimate prevalence ratios for clinical characteristics and macro- and microvascular complications at diabetes onset, while Cox regression was applied to estimate the risk of complications post-diagnosis. Analyses were adjusted for sex, calendar year at birth, age at enrollment and diabetes duration. The per T-allele impact was associated with 0.6 kg/m2 (95% CI: 0.4, 0.8) lower BMI, 1.4 cm (95% CI: 1.0, 1.8) smaller waist circumference, 5.6% (95% CI: 4.2, 7.0) lower insulin secretion and 5.0% (95% CI: 3.3, 6.7) higher insulin sensitivity. Over 10 years, the per T-allele impact was associated with lower risks for major adverse cardiovascular events (0.87 [95% CI 0.79, 0.95]), myocardial infarction (0.82 [95% CI: 0.72, 0.93]) and heart failure (0.85 [95% CI 0.73, 1.00]), with no significant impact on microvascular complications. The TCF7L2 variant is associated with less obesity, lower insulin secretion and higher insulin action at diabetes onset, and decreased risk of cardiovascular events following type 2 diabetes onset.

Differential Organ Ageing Is Associated With Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a progressive disorder and the leading cause of central vision loss. Age is the most important risk factor, followed by genetics and smoking. However, ageing is a complex process, and biological age can deviate from chronological age between individuals and within different organ systems. Initially, we used machine learning to predict the biological age of the immune, cardiovascular, pulmonary, renal, musculoskeletal, metabolic and hepatic systems by analysing various physiological and physical markers in the UK Biobank cohort. Then, we investigated the association of each organ's biological age with incident AMD derived from electronic health record data as well as with different AMD genetic risk scores. We observed that most organ systems in participants who developed AMD after recruitment showed accelerated ageing compared with controls, with the immune system being the most affected, especially in younger males. Surprisingly, we found that AMD patients showed slower ageing of their hepatic system compared to controls, particularly in female patients. The overall AMD genetic risk score was associated with faster organ ageing across all tissues except cardiovascular and pulmonary, while genetic risk scores stratified by pathways differently influenced each organ system. In conclusion, we found differential organ ageing associated with AMD. Significantly, genetic risk variants of AMD are associated with differential ageing of various organ systems.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow‐Up Studies

ABSTRACTBiological datasets often consist of thousands or millions of variables, e.g. genetic variants or biomarkers, and when sample sizes are large it is common to find many associated with an outcome of interest, for example, disease risk in a GWAS, at high levels of statistical significance, but with very small effects. The False Discovery Rate (FDR) is used to identify effects of interest based on ranking variables according to their statistical significance. Here, we develop a complementary measure to the FDR, the priorityFDR, that ranks variables by a combination of effect size and significance, allowing further prioritisation among a set of variables that pass a significance or FDR threshold. Applying to the largest GWAS of type 1 diabetes to date (15,573 cases and 158,408 controls), we identified 26 independent genetic associations, including two newly‐reported loci, with qualitatively lower priorityFDRs than the remaining 175 signals. We detected putatively causal type 1 diabetes risk genes using Mendelian Randomisation, and found that these were located disproportionately close to low priorityFDR signals (p = 0.005), as were genes in the IL‐2 pathway (p = 0.003). Selecting variables on both effect size and significance can lead to improved prioritisation for mechanistic follow‐up studies from genetic and other large biological datasets.

Epigenetics in Neurodegenerative Diseases.

Healthy brain functioning requires a continuous fine-tuning of gene expression, involving changes in the epigenetic landscape and 3D chromatin organization. Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD) are three multifactorial neurodegenerative diseases (NDDs) that are partially explained by genetics (gene mutations and genetic risk factors) and influenced by non-genetic factors (i.e., aging, lifestyle, and environmental conditions). Examining comprehensive studies of global and locus-specific (epi)genomic and transcriptomic alterations in human and mouse brain samples at the cell-type resolution has uncovered important phenomena associated with AD. First, DNA methylation and histone marks at promoters contribute to transcriptional dysregulation of genes that are directly implicated in AD pathogenesis (i.e., APP), neuroplasticity and cognition (i.e., PSD95), and microglial activation (i.e., TREM2). Second, the presence of AD genetic risk variants in cell-type-specific distal enhancers (i.e., BIN1 in microglia) alters transcription, presumably by disrupting associated enhancer-promoter interactions and chromatin looping. Third, epigenomic erosion is associated with widespread transcriptional disruption and cell identity loss. And fourth, aging, high cholesterol, air pollution, and pesticides have emerged as potential drivers of AD by inducing locus-specific and global epigenetic modifications that impact key AD-related pathways. Epigenetic studies in ALS/FTD also provide evidence that genetic and non-genetic factors alter gene expression profiles in neurons and astrocytes through aberrant epigenetic mechanisms. We additionally overview the recent development of potential new therapeutic strategies involving (epi)genetic editing and the use of small chromatin-modifying molecules (epidrugs).

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.

Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.

Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain.

Genetic risk variants for common diseases are predominantly located in non-coding regulatory regions and modulate gene expression. Although bulk tissue studies have elucidated shared mechanisms of regulatory and disease-associated genetics, the cellular specificity of these mechanisms remains largely unexplored. This study presents a comprehensive single-nucleus multi-ancestry atlas of genetic regulation of gene expression in the human prefrontal cortex, comprising 5.6 million nuclei from 1,384 donors of diverse ancestries. Through multi-resolution analyses spanning eight major cell classes and 27 subclasses, we identify genetic regulation for 14,258 genes, with 857 showing cell type-specific regulatory effects at the class level and 981 at the subclass level. Colocalization of genetic variants associated with gene regulation and disease traits uncovers novel cell type-specific genes implicated in Alzheimer's disease, schizophrenia, and other disorders, which were not detectable in bulk tissue analyses. Analysis of dynamic genetic regulation at the single nucleus level identifies 2,073 genes with regulatory effects that vary across developmental trajectories, inferred from a broad age range of donors. We also uncover 1,655 genes with trans-regulatory effects, revealing distal regulation of gene expression. This high-resolution atlas provides unprecedented insight into the cell type-specific regulatory architecture of the human brain, and offers novel mechanistic targets for understanding the genetic basis of neuropsychiatric and neurodegenerative diseases.

Genetic subtypes of Alzheimer’s disease are related to differential biomarker and cognitive trajectories

AbstractBackgroundAlzheimer’s disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. We previously identified a genetic heterogeneity across two levels. The first level, which was disease‐relevant but not disease‐specific, comprised three clusters (termed “constellations”) driven by different correlation patterns in a region of extended LD surrounding the MAPT locus. The second level reflected disease‐specific genetic subtypes of AD. These structures were previously found to replicate in an independent dataset. Here, we investigate whether genetic subtypes demonstrate different patterns of biomarker and cognitive trajectories.MethodIn a discovery dataset from the UK Biobank (AD cases = 2,739, controls = 5,478), we applied principal component analysis (PCA) to AD‐associated variants, allowing us to identify three constellations. Subsequently, a biclustering algorithm identified two distinct disease‐specific genetic signatures among subsets of cases not found in controls. We classified individuals with mild cognitive impairment (MCI; n = 399) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) into constellations and determined whether they were a member of either bicluster (i.e., genetic subtype) or not. Longitudinal change in cognitive performance (Preclinical Alzheimer’s Cognitive Composite; PACC), amyloid (florbetapir PET) and p‐tau (CSF) was compared between the two bicluster groups and with non‐bicluster cases.ResultThe disease‐relevant constellations and disease‐specific bicluster structure was apparent in the ADNI MCI sample. Individuals with genetic signatures resembling bicluster 2 exhibited greater decline on the PACC compared to the non‐bicluster (p = 0.022) and bicluster 1 groups (p = 0.004). Bicluster 1 demonstrated a somewhat greater increase in florbetapir over time compared to the non‐bicluster group, but this difference was not significant (p = 0.129). Bicluster 2 demonstrated a significantly greater increase of CSF p‐tau over time compared to the non‐bicluster group (p = 0.041)ConclusionThese results provide additional evidence for the existence of AD genetic subtypes in an independent dataset. The genetic subtypes exhibit differential pathological accumulation and cognitive decline over time, suggesting that this genetic variation has meaningful consequences for downstream processes and disease manifestation.

The Alzheimer’s Disease Sequencing Project – Discovery, Discovery Extension and Follow Up Study (ADSP‐FUS): APOE genotype status and demographic characteristics across datasets

AbstractBackgroundThe ADSP is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for Alzheimer Disease (AD). Initial phases (Discovery and Discovery Extension) were predominantly non‐Hispanic Whites of European Ancestry (NHW‐EA). The ADSP expanded the population diversity in the Follow Up Study (ADSP‐FUS), and the current phase, ADSP‐FUS 2.0: The Diverse Population Initiative, focusing on whole genome sequencing (WGS) of non‐European populations including Hispanic/Latino (HL), non‐Hispanic Black with African Ancestry (NHB‐AA) and Asian populations. Support for these efforts include newly funded initiatives such as The DAWN Project, focused on recruitment of African, African‐American and Hispanic American populations, and the Asian Cohort for Alzheimer’s Disease (ACAD).MethodsADSP cohorts consist of studies of AD, dementia, and age‐related conditions. Clinical classifications are assigned based on standard criteria and derived from clinical measures and history, as well as additional neuropathologic data. In addition to production of WGS, APOE genotyping is available for all ADSP samples.ResultsThe ADSP currently consists of 40 cohorts comprised of ∼36,300 individuals, with plans to sequence >110,000 individuals from diverse race/ethnicity. Genotyping, sequencing, and clinical adjudication has been performed on 36,361 participants (cases N = 12,133, median age = 72; cognitively‐unimpaired(CU) individuals N = 17,116, median age = 74; ADRD N = 7,112, median age = 71). Mean ages for cases and controls vary across cohorts, 57.0+5.6 to 86.5+4.2 cases and 63.3+7.8 to 90.0+0 controls. 61% participants female, distributed as follows: cases(60.3%), CU(63.7%), and ADRD(55.8%). APOE genotype proportions differ considerably across reported race/ethnicity, for example highest for APOE ε4/ε4 carriers observed in Non‐Hispanic whites participants (7.4%) and the lowest in Asians (1.7%)ConclusionThe results provide an overview of clinical features in ADSP cohorts. The growth of the ADSP‐FUS 2.0 is central to the ADSP and expanding the size and diversity of this genomic resource available via NIAGADS. WGS data will be integrated with ADSP programs focused on phenotype harmonization, association analyses, functional genomics, and machine learning. In concert with these programs, the ADSP‐FUS 2.0 will accelerate the identification and understanding of potential genetic risk and protective variants for AD across all populations with the target of developing new treatments that are globally effective.

Bin1 regulates synaptic endocytic trafficking compromised by Alzheimer’s genetic risk variants

AbstractBackgroundAlzheimer’s disease (AD), an untreatable synaptic disorder, is the most frequent cause of dementia. It is still unclear which mechanisms drive the early synapse dysfunction in the most common late‐onset AD (LOAD). The second most important LOAD risk gene identified, BIN1, is an endocytic regulator. We implicated Bin1 in BACE1 recycling with Bin1 loss of function, increasing axonal beta‐amyloid42. Since endocytic trafficking is essential for synapse function, we hypothesize that Bin1 may regulate synaptic endocytic trafficking, which may be disrupted by Bin1’s loss of function or beta‐amyloid42. We aim to dissect Bin1 causal mechanisms of synaptic dysfunction to assess how the risk for LOAD increases.MethodSynaptically mature mouse primary cortical neurons and brain hippocampus were used to investigate Bin1 synaptic endocytic localization with immunofluorescence and synaptosomes. Using knockdown, overexpression, and rescue approaches the impact on synaptic endosome size and synaptic density was determined using super‐resolution fluorescence microscopy and quantitative subcellular image analysis.ResultBin1 was enriched in synaptic vesicle brain fractions, supporting its presynaptic function. Unexpectedly, Bin1 is enriched proximal synapses, identified as inhibitory GABAergic presynaptic terminals in primary neurons and the brain. Notably, Bin1 down‐regulation specifically reduced inhibitory synapse density and increased neuronal excitability. Bin1 loss increased inhibitory synaptic vesicle release and endocytosis, exhausting presynaptic endosomes. Interestingly, the impact on inhibitory synapses is independent of beta‐amyloid production. Moreover, Bin1 LOAD mutations interfere with Bin1 function and are likely to cause loss of function.ConclusionBin1 is an inhibitory presynaptic protein. The LOAD mutations may cause Bin1 loss of function, affecting inhibitory synapses. Losing inhibitory transmission may turn excitatory neurons hyperactive, accelerating the loss of excitatory synapses. In the future, we aim to dissect the underlying mechanisms by which Bin1 mutations interfere with Bin1’s role in synaptic vesicle mechanisms at inhibitory presynaptic terminals.

Aging x Environment x genetic risk for late onset Alzheimer’s disease results in alterations in cognitive function in mice independent of amyloid and tau pathology

AbstractBackgroundAlzheimer’s disease (AD) research has been historically dominated with studies in mouse models expressing familial AD mutations; however, the majority of AD patients have the sporadic, late‐onset form of AD (LOAD). To address this gap, the IU/JAX/PITT MODEL‐AD Consortium has focused on development of mouse models that recapitulate LOAD by combining genetic risk variants with environmental risk factors and aging to enable more precise models to evaluate potential therapeutics. The present studies were undertaken to characterize cognitive and neurophysiological phenotypes in LOAD mice.MethodTwo genetic risk factors, APOE4 and Trem2*R47H, were incorporated into C57BL/6J mice with humanized amyloid‐beta to produce the LOAD2 model (JAX# 030670). Male and female LOAD2 and WT mice were exposed to ad libitum 45% high‐fat diet from 2‐months of age (LOAD2+HFD or WT+HFD, respectively) throughout their lifespan and compared to LOAD2 and WT mice on control diet (+CD). Cognitive training began at 14‐months of age using a touchscreen testing battery, similar to previously described methods (Oomen et al 2013). At the conclusion of touchscreen testing, subjects were implanted with wireless telemetry devices (DSI) for evaluation of electroencephalography (EEG) signatures.ResultAll subjects met the touch‐reward association criteria. During task acquisition LOAD2+CD mice demonstrated impaired acquisition relative to WT+CD, while both LOAD2+HFD and WT+HFD failed to learn the task as indicated by accuracy less than chance (<50%); which was confirmed in a separate cohort. LOAD2+HFD mice demonstrated increased spikewave events as measured by EEG, relative to LOAD2+CD. At 18‐months of age +CD mice that met acquisition criteria were evaluated in a location discrimination task with LOAD2+CD mice demonstrating modest impairments in pattern separation relative to age‐matched WT+CD.ConclusionThese data are the first reports of cognitive deficits and neurophysiological alterations in mice with environmental x genetic risk for LOAD, independent of amyloid and tau pathology. Importantly, the present findings demonstrate the sensitivity of the translational touchscreen testing battery for detecting mild cognitive impairment in LOAD mice with corresponding neurophysiologic alterations, and extend previous characterization data for the LOAD2 model and its utility for the study of the biology of LOAD.

Trans‐ancestry GWAS for Alzheimer’s Disease Reveals a Novel Locus interacting with APOE*4

AbstractBackgroundAPOE*4 is the strongest genetic risk for late‐onset Alzheimer’s disease (AD), but other genetic loci may counter its detrimental effect, providing therapeutic avenues. Expanding beyond non‐Hispanic White subjects, we sought to additionally leverage genetic data from non‐Hispanic and Hispanic subjects of admixed African ancestry to perform trans‐ancestry APOE*4‐stratified GWAS, anticipating that allele frequency differences across populations would boost power for gene discovery.MethodParticipants were ages 60+, of European (EU; ≥75%) or admixed African (AFR; ≥25%) ancestry, and diagnosed as cases or controls. Genetic data were available from SNP arrays imputed to TOPMed or whole genome sequencing (WGS). Genome‐wide association studies (GWAS) were performed per data type, APOE*4 status, and Hispanic/non‐Hispanic status (Figure‐1A), requiring genotyping rate ≥80% and minor allele frequency ≥0.1% in EU and ≥1% in AFR, followed by random effects meta‐analysis (Plink v2.0; GWAMA v2.2.2). An APOE*4‐by‐SNP interaction model was also evaluated. GWAS performed multiple linear regression on an AD‐age score (Le Guen & Belloy et al. 2021; Figure‐1B), adjusting for APOE*4/APOE*2 dosage, the first five genetic principal components (PC‐AiR; GENESIS; R v3.6), and array/sequencing center. Variants were filtered to be covered on at least half of the individuals per stratum and on both EU as well as AFR ancestry datasets.ResultWe found 1 novel genome‐wide significant locus in the APOE*4‐by‐SNP interaction GWAS (Figure‐2), with lead variant rs17042744 located intronic on a long non‐coding RNA (ENSG00000233005) and 558kb away from APOB. This variant showed disparate allele frequencies across the studied populations, yet was consistently protective in APOE*4 carriers and risk‐increasing in APOE*4 non‐carriers (Figure‐3).ConclusionWe show the relevance of expanding beyond non‐Hispanic white samples to identify genetic risk variants that may modulate the effect of APOE*4 on AD risk, identifying a risk locus with substantially elevated frequencies in African compared to European ancestry. The locus harbored APOB, a ligand for the LDL receptor, regulator of plasma cholesterol levels, and known to have mutations in its regulatory region that cause hypobetalipoproteinemia and hypercholesterolemia. Ongoing work is exploring replication and computational functional genomics follow‐up work.

Association between known Alzheimer’s disease risk genetic variants and hippocampal atrophy along the Alzheimer’s disease continuum in a Korean cohort

AbstractBackgroundLarge‐scale genome‐wide association studies (GWAS) of Alzheimer’s disease (AD) from European ancestry identified many genetic variants associated with clinical diagnosis of AD dementia. However, it remains unclear whether these AD‐related variants are associated with AD biomarkers, particularly hippocampal atrophy, a well‐known neurodegeneration biomarker of AD in a Korean population. In this study, we investigated the association between known AD risk single nucleotide polymorphisms (SNPs) and hippocampal atrophy along the AD continuum in older Korean adults.MethodA total of 487 participants (258 cognitively normal olde adults [CN], 144 mild cognitive impairment [MCI], 85 AD dementia) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) were included for analysis. All participants underwent 11C‐PiB‐PET/MRI. Hippocampal volume adjusted for intracranial volume (HVa) was obtained from 3D T1‐weighted MRI scans using FreeSurfer and used as a neurodegeneration marker of AD. Global beta‐amyloid (Aβ) deposition was calculated from PiB uptake in the global cortical region‐of‐interest using SPM12. From the genetic evidence gathered by the AD Sequencing Project (ADSP), which consists of 76 SNPs associated with AD, we selected 38 SNPs with a minor allele frequency (MAF) greater than 1% from the genotyping data imputed using the TOPMed imputation server in the KBASE cohort.ResultAmong 38 known AD‐related SNPs, three SNPs (rs6966331 in EPDR1, rs2242595 in MYO15A, and rs17125924 in FERMT2) were associated with HVa in an initial exploratory analysis (p<0.05). In a subsequent confirmatory analysis, the associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with HVa remained significant after controlling for age, sex, and APOE4 carrier status, as well as global Aβ deposition (p<0.001 and p = 0.009 for rs6966331 and rs2242595, respectively) (Table 1).ConclusionOur study identified associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with hippocampal volume in Korean older adults, and these associations were independent of cerebral Aβ deposition and APOE4 carrier status. These findings suggest that these AD‐related loci may contribute to the development of AD dementia via Aβ‐independent neurodegeneration.

Targeting PNPLA3 to Treat MASH and MASH Related Fibrosis and Cirrhosis.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is caused by metabolic triggers and genetic predisposition. Among the genetic MASLD risk variants identified today, the common PNPLA3 148M variant exerts the largest effect size of MASLD heritability. The PNPLA3 148M protein is causatively linked to the development of liver steatosis, inflammation and fibrosis in experimental studies and is therefore an appealing target for therapeutic approaches to treat this disease. Several PNPLA3 targeted approaches are currently being evaluated in clinical trials for the treatment of metabolic dysfunction-associated steatohepatitis (MASH), the most severe form of MASLD and promising proof of principle data with reduced liver fat content in homozygous PNPLA3 148M risk allele carriers has been reported from phase 1 trials following hepatic silencing of PNPLA3. Thus, targeting PNPLA3, the strongest genetic determinant of MASH may hold promise as the first precision medicine for the treatment of this disease. A histological endpoint-based phase 2b study has been initiated and several more are expected to be initiated to evaluate treatment effects on histological MASH and liver fibrosis in participants being homozygous for the PNPLA3 148M risk allele variant. The scope of this mini-review is to briefly describe the PNPLA3 148M genetics, function and preclinical experimental evidence with therapeutic approaches targeting PNPLA3 as well as to summarise the PNPLA3 based therapies currently in clinical development.