Probability of Alzheimer's disease based on common and rare genetic variants

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases

Background

Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease. We hypothesized that unknown counterfactors might be involved in reversing the PRS prediction, which might provide new insights into the pathogenesis, prevention, and early intervention of diseases.

Methods

We built a novel computational framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery (disc) and the replication (rep) datasets include 2722 and 2854 individuals, respectively. First, we calculated the optimized PRS model based on the three recent AD GWAS summary statistics for each cohort. Then, we stratified the individuals by their PRS and clinical diagnosis into six biologically meaningful PRS strata, such as AD cases with low/high risk and cognitively normal (CN) with low/high risk. Lastly, we imputed individual genetically-regulated expression (GReX) and identified differential GReX and GRPas between risk strata using gene-set enrichment and variational analyses in two models, with and without APOE effects. An orthogonality test was further conducted to verify those GRPas are independent of PRS risk. To verify the generalizability of other polygenic diseases, we further applied a default model of GRPa-PRS for schizophrenia (SCZ).

Results

For each stratum, we conducted the same procedures in both the disc and rep datasets for comparison. In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocyte response to oxidative stress. Additionally, we discovered resilience-related GRPs that are orthogonal to AD PRS, such as the calcium signaling pathway and divalent inorganic cation homeostasis. In SCZ, pathways related to mitochondrial function and muscle development were highlighted. Finally, our GRPa-PRS method identified more consistent differential pathways compared to another variant-based pathway PRS method.

Conclusions

We developed a framework, GRPa-PRS, to systematically explore the differential GReX and GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those strata unveiled new insights into the pathways associated with disease risk and resilience. Our framework is extendable to other polygenic complex diseases.

Bioinformatics to analyze the differentially expressed genes in different degrees of Alzheimer's disease and their roles in progress of the disease

Employing bioinformatics approaches, this investigation pinpointed pivotal differentially expressed genes (DEGs) across the spectrum of Alzheimer's disease (AD), from incipient to severe stages, using the GSE28146 dataset from the GEO repository. Analytical methods included DEG identification via the limma package in R, coupled with GO and KEGG pathway analyses through clusterProfiler, to discern biological processes and pathway involvements. Key findings spotlighted the roles of proteasome subunits PSMB4, PSMB8, PSMC4, and PSMD6 in the early stage, ribosomal proteins RPS3 and RPL11 during moderate AD, and mitochondrial components COX5B, COX6B2, and COX7A2 in severe AD, underscoring their importance in the disease's pathogenesis. Conclusively, these results not only delineate the dynamic genetic shifts accompanying AD progression but also propose critical biomarkers for potential therapeutic targeting, offering a consolidated basis for future AD research and treatment development. This offered a novel idea for analyzing the pathogenesis and development of AD and investigation of targeted drugs.

APOE ε4's impact on response to amyloid therapies in early symptomatic Alzheimer's disease: Analyses from multiple clinical trials

INTRODUCTION

Apolipoprotein E (APOE) ε4 may interact with response to amyloid-targeting therapies.

METHODS

Aggregate data from trials enrolling participants with amyloid-positive, early symptomatic Alzheimer's disease (AD) were analyzed for disease progression.

RESULTS

Pooled analysis of potentially efficacious antibodies lecanemab, aducanumab, solanezumab, and donanemab shows slightly better efficacy in APOE ε4 carriers than in non-carriers. Carrier and non-carrier mean (95% confidence interval) differences from placebo using Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were -0.30 (-0.478, -0.106) and -0.20 (-0.435, 0.042) and AD Assessment Scale-Cognitive subscale (ADAS-Cog) values were -1.01 (-1.577, -0.456) and -0.80 (-1.627, 0.018), respectively. Decline in the APOE ε4 non-carrier placebo group was equal to or greater than that in carriers across multiple scales. Probability of study success increases as the representation of the carrier population increases.

DISCUSSION

We hypothesize that APOE ε4 carriers have same or better response than non-carriers to amyloid-targeting therapies and similar or less disease progression with placebo in amyloid-positive trials.

HIGHLIGHTS

Amyloid-targeting therapies had slightly greater efficacy in apolipoprotein E (APOE) ε4 carriers. Clinical decline is the same/slightly faster in amyloid-positive APOE ε4 non-carriers. Prevalence of non-carriers in trial populations could impact outcomes.

Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Gene-environment interactions in Alzheimer disease: the emerging role of epigenetics

With the exception of a few monogenic forms, Alzheimer disease (AD) has a complex aetiology that is likely to involve multiple susceptibility genes and environmental factors. The role of environmental factors is difficult to determine and, until a few years ago, the molecular mechanisms underlying gene-environment (G × E) interactions in AD were largely unknown. Here, we review evidence that has emerged over the past two decades to explain how environmental factors, such as diet, lifestyle, alcohol, smoking and pollutants, might interact with the human genome. In particular, we discuss how various environmental AD risk factors can induce epigenetic modifications of key AD-related genes and pathways and consider how epigenetic mechanisms could contribute to the effects of oxidative stress on AD onset. Studies on early-life exposures are helping to uncover critical time windows of sensitivity to epigenetic influences from environmental factors, thereby laying the foundations for future primary preventative approaches. We conclude that epigenetic modifications need to be considered when assessing G × E interactions in AD.

What contribution can genetics make to predict the risk of Alzheimer's disease?

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although its etiology remains incompletely understood, genetic variants are important contributors. The prediction of AD risk through individual genetic variants is an important topic of research that may have individual and societal consequences when preventive treatments will become available. However, the genetic substratum of AD is heterogeneous. In addition to the extremely rare and fully penetrant pathogenic variants of the PSEN1, PSEN2 or APP genes causing autosomal dominant AD, a large spectrum of risk factors have been identified in complex forms, including the common risk factor APOEɛ4, which is associated with a moderate-to-high risk, common polymorphisms associated with a modest individual risk, and a plethora of rare variants in genes like SORL1, TREM2 or ABCA7 with moderate to high-magnitude effect. Understanding how these genetic factors contribute to AD risk in a given individual, in additional to non-genetic factors, remains a challenge. Over the last 10 years, age-related penetrance curves have progressively incorporated advances in the knowledge of AD genetics, from APOE to common polygenic components and, currently, SORL1 rare variants, which represents an important step towards precision medicine in AD. In this review, we present the complex genetic architecture of AD and we expose the prediction of AD risk according to its underlying genetic component.

Mitochondrial Genetics Reinforces Multiple Layers of Interaction in Alzheimer’s Disease

Simple Summary

Nuclear DNA remains the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer’s Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including both differential gene expression and mitochondrial genome-wide epistasis analysis. Our results highlight important layers of interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.

Abstract

Nuclear DNA has been the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer’s Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including differential gene expression and mitochondrial genome-wide epistasis. We assessed the expression of mitochondrial genes in different brain tissues from two public RNA-Seq databases (GEO and GTEx). Then, we analyzed mtDNA from the ADNI Cohort and investigated epistasis regarding mitochondrial variants and levels of Aβ1−42, TAU, and Phosphorylated TAU (PTAU) from cognitively healthy controls, and both mild cognitive impairment (MCI) and AD cases. We identified multiple differentially expressed mitochondrial genes in the comparisons between cognitively healthy individuals and AD patients. We also found increased protein levels in MCI and AD patients when compared to healthy controls, as well as novel candidate networks of mtDNA epistasis, which included variants in all mitochondrially-encoded oxidative phosphorylation complexes, 12S rRNA and MT-DLOOP. Our results highlight layers of potential interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.

Challenge accepted: uncovering the role of rare genetic variants in Alzheimer's disease

The search for rare variants in Alzheimer's disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.