Polygenic resilience scores capture protective genetic effects for Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Hou J
PROVIDER: S-EPMC9314356 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective:Genome-wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age-specific genetic risk for AD. The aim of this study was to quantify the age-specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. Methods:Quantification of individual differences in age-specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age-specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P-value thresholds for disease association. Results:Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ? 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10-12) was observed for PRS based upon genome-wide significant SNPs (P ? 5 × 10-8). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation:Both PRS and PHS can be used to predict an age-specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ? 10-3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10-3, the age-specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).
| S-EPMC6414493 | biostudies-literature
Project description:IntroductionIdentification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The "resilience" framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci.ObjectivesIn this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD.MethodsThe discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics.ResultsIt was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved.ConclusionOur findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.
| S-EPMC10834825 | biostudies-literature
Project description:Alzheimer's disease (AD) heritability is enriched in glial genes, but how and when cell-type-specific genetic risk contributes to AD remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets. In an autopsy dataset spanning all stages of AD (n=1,457), astrocytic (Ast) ADPRS was associated with both diffuse and neuritic Aβ plaques, while microglial (Mic) ADPRS was associated with neuritic Aβ plaques, microglial activation, tau, and cognitive decline. Causal modeling analyses further clarified these relationships. In an independent neuroimaging dataset of cognitively unimpaired elderly (n=2,921), Ast-ADPRS were associated with Aβ, and Mic-ADPRS was associated with Aβ and tau, showing a consistent pattern with the autopsy dataset. Oligodendrocytic and excitatory neuronal ADPRSs were associated with tau, but only in the autopsy dataset including symptomatic AD cases. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.
| S-EPMC10274993 | biostudies-literature
Project description:BACKGROUND:Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are clustered in areas of biology, notably immunity and inflammation, cholesterol metabolism, endocytosis, and ubiquitination. Polygenic scores (PRS), which weight the sum of an individual's risk alleles, have been used to draw inferences about the pathological processes underpinning AD. OBJECTIVE:This paper aims to systematically review how AD PRS are being used to study a range of outcomes and phenotypes related to neurodegeneration. METHODS:We searched the literature from July 2008-July 2018 following PRISMA guidelines. RESULTS:57 studies met criteria. The AD PRS can distinguish AD cases from controls. The ability of AD PRS to predict conversion from mild cognitive impairment (MCI) to AD was less clear. There was strong evidence of association between AD PRS and cognitive impairment. AD PRS were correlated with a number of biological phenotypes associated with AD pathology, such as neuroimaging changes and amyloid and tau measures. Pathway-specific polygenic scores were also associated with AD-related biologically relevant phenotypes. CONCLUSION:PRS can predict AD effectively and are associated with cognitive impairment. There is also evidence of association between AD PRS and other phenotypes relevant to neurodegeneration. The associations between pathway specific polygenic scores and phenotypic changes may allow us to define the biology of the disease in individuals and indicate who may benefit from specific treatments. Longitudinal cohort studies are required to test the ability of PGS to delineate pathway-specific disease activity.
| S-EPMC7242840 | biostudies-literature
Project description:Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.
| S-EPMC8302739 | biostudies-literature
Project description:Genetic 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.
| S-EPMC8184987 | biostudies-literature
Project description:The success of genome-wide association studies (GWAS) completed in the last 15 years has reinforced a key fact: polygenic architecture makes a substantial contribution to variation of susceptibility to complex disease, including Alzheimer's disease. One straight-forward way to capture this architecture and predict which individuals in a population are most at risk is to calculate a polygenic risk score (PRS). This score aggregates the risk conferred across multiple genetic variants, ultimately representing an individual's predicted genetic susceptibility for a disease. PRS have received increasing attention after having been successfully used in complex traits. This has brought with it renewed attention on new methods which improve the accuracy of risk prediction. While these applications are initially informative, their utility is far from equitable: the majority of PRS models use samples heavily if not entirely of individuals of European descent. This basic approach opens concerns of health equity if applied inaccurately to other population groups, or health disparity if we fail to use them at all. In this review we will examine the methods of calculating PRS and some of their previous uses in disease prediction. We also advocate for, with supporting scientific evidence, inclusion of data from diverse populations in these existing and future studies of population risk via PRS.
| S-EPMC9484091 | biostudies-literature
Project description:BackgroundAn increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer's disease (LOAD) pathology. Due to the increased permeability of the blood-brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it.MethodsWe used a genome-wide association study (GWAS) of 90 different plasma proteins (n = 17,747) to create polygenic scores (PGSs) in an independent discovery (cases = 1,852 and controls = 1,990) and replication (cases = 799 and controls = 778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases = 21,982, controls = 41,944) as an outcome to explore possible causal relationships between the two.ResultsWe identified four plasma protein level PGSs that were significantly associated (FDR-adjusted p < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR/95%CI = 0.945/0.906-0.984, p = 0.005).ConclusionOur results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.
| S-EPMC11300309 | biostudies-literature
Project description:The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person's SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer's Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.
| S-EPMC8305482 | biostudies-literature
Project description:ObjectiveThe aim of the current study is to understand why some individuals avoid developing Parkinson disease (PD) despite being at relatively high genetic risk, using the largest datasets of individual-level genetic data available.MethodsWe calculated polygenic risk score to identify controls and matched PD cases with the highest burden of genetic risk for PD in the discovery cohort (International Parkinson's Disease Genomics Consortium, 7,204 PD cases and 9,412 controls) and validation cohorts (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease, 8,968 cases and 7,598 controls; UK Biobank, 2,639 PD cases and 14,301 controls; Accelerating Medicines Partnership-Parkinson's Disease Initiative, 2,248 cases and 2,817 controls). A genome-wide association study meta-analysis was performed on these individuals to understand genetic variation associated with resistance to disease. We further constructed a polygenic resilience score, and performed multimarker analysis of genomic annotation (MAGMA) gene-based analyses and functional enrichment analyses.ResultsA higher polygenic resilience score was associated with a lower risk for PD (β = -0.054, standard error [SE] = 0.022, p = 0.013). Although no single locus reached genome-wide significance, MAGMA gene-based analyses nominated TBCA as a putative gene. Furthermore, we estimated the narrow-sense heritability associated with resilience to PD (h2 = 0.081, SE = 0.035, p = 0.0003). Subsequent functional enrichment analysis highlighted histone methylation as a potential pathway harboring resilience alleles that could mitigate the effects of PD risk loci.InterpretationThe present study represents a novel and comprehensive assessment of heritable genetic variation contributing to PD resistance. We show that a genetic resilience score can modify the penetrance of PD genetic risk factors and therefore protect individuals carrying a high-risk genetic burden from developing PD. ANN NEUROL 2022;92:270-278.
| S-EPMC9329258 | biostudies-literature