Polygenic risk score in postmortem diagnosed sporadic early-onset Alzheimer's disease

Early- and Late-Onset Alzheimer's Disease: Two Sides of the Same Coin?

Early-onset Alzheimer's disease (EOAD), defined as Alzheimer's disease onset before 65 years of age, has been significantly less studied than the "classic" late-onset form (LOAD), although EOAD often presents with a more aggressive disease course, caused by variants in the APP, PSEN1, and PSEN2 genes. EOAD has significant differences from LOAD, including encompassing diverse phenotypic manifestations, increased genetic predisposition, and variations in neuropathological burden and distribution. Phenotypically, EOAD can be manifested with non-amnestic variants, sparing the hippocampi with increased tau burden. The aim of this article is to review the different genetic bases, risk factors, pathological mechanisms, and diagnostic approaches between EOAD and LOAD and to suggest steps to further our understanding. The comprehension of the monogenic form of the disease can provide valuable insights that may serve as a roadmap for understanding the common form of the disease.

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

Evaluating the genetic interaction effects of gut microbiome and diet on the risk of neuroticism in the UK Biobank cohort

OBJECTIVES

In this study designed to investigate the effect of diet and gut microbiome on neuropsychiatric disorders, we explored the mechanisms of the interaction between diet and gut microbiome on the risk of neuroticism.

METHODS

First, using the individual genotype data from the UK Biobank cohort (N = 306 165), we calculated the polygenic risk score (PRS) based on 814 dietary habits single nucleotide polymorphisms (SNPs), 21 diet compositions SNPs and 1001 gut microbiome SNPs, respectively. Gut microbiome and diet-associated SNPs were collected from three genome-wide association studies (GWAS), including the gut microbiome (N = 3890), diet compositions (over 235 000 subjects) and dietary habits (N = 449 210). The neuroticism score was calculated by 12 questions from the Eysenck Personality Inventory Neuroticism scale. Then, regression analysis was performed to evaluate the interaction effects between diet and the gut microbiome on the risk of neuroticism.

RESULTS

Our studies demonstrated multiple candidate interactions between diet and gut microbiome, such as protein vs. Bifidobacterium (β = 4.59 × 10-3; P = 9.45 × 10-3) and fat vs. Clostridia (β = 3.67 × 10-3; P = 3.90 × 10-2). In addition, pieces of fresh fruit per day vs. Ruminococcus (β = -5.79 × 10-3, P = 1.10 × 10-3) and pieces of dried fruit per day vs. Clostridiales (β = -5.63 × 10-3, P = 1.49 × 10-3) were found to be negatively associated with neuroticism in fruit types. We also identified several positive interactions, such as tablespoons of raw vegetables per day vs. Veillonella (β = 5.92 × 10-3, P = 9.21 × 10-4) and cooked vegetables per day vs. Acidaminococcaceae (β = 5.69 × 10-3, P = 1.24 × 10-3).

CONCLUSIONS

Our results provide novel clues for understanding the roles of diet and gut microbiome in the development of neuroticism.

The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults

Introduction

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children.

Methods

Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks.

Results

In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036).

Discussion

We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.

Influence of genetic and cardiometabolic risk factors in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder. Cardiometabolic and genetic risk factors play an important role in the trajectory of AD. Cardiometabolic risk factors including diabetes, mid-life obesity, mid-life hypertension and elevated cholesterol have been linked with cognitive decline in AD subjects. These potential risk factors associated with cerebral metabolic changes which fuel AD pathogenesis have been suggested to be the reason for the disappointing clinical trial results. In appreciation of the risks involved, using search engines such as PubMed, Scopus, MEDLINE and Google Scholar, a relevant literature search on cardiometabolic and genetic risk factors in AD was conducted. We discuss the role of genetic as well as established cardiovascular risk factors in the neuropathology of AD. Moreover, we show new evidence of genetic interaction between several genes potentially involved in different pathways related to both neurodegenerative process and cardiovascular damage.

A Pathway-Specific Polygenic Risk Score is Associated with Tau Pathology and Cognitive Decline

Background: Tauopathy is a primary neuropathological hallmark of Alzheimer’s disease with a strong relationship to cognitive impairment. In the brain, tau aggregation is associated with the regulation of tau kinases and the binding ability of tau to microtubules. Objective: To explore the potential for using specific polygenic risk scores (PRSs), combining the genetic influences involved in tau-protein kinases and the tau-protein binding pathway, as predictors of tau pathology and cognitive decline in non-demented individuals. Methods: We computed a pathway-specific PRS using summary statistics from previous large-scale genome-wide association studies of dementia. We examined whether PRS is related to tau uptake in positron emission tomography (PET), tau levels, and the rate of tau level changes in cerebrospinal fluid (CSF). We further assessed whether PRS is associated with memory impairment mediated by CSF tau levels. Results: A higher PRS was related to elevated CSF tau levels and tau-PET uptake at baseline, as well as greater rates of change in CSF tau levels. Moreover, PRS was associated with memory impairment, mediated by increased CSF tau levels. The association between PRS and tau pathology was significant when APOE was excluded, even among females. However, the effect of PRS on cognitive decline appeared to be driven by the inclusion of APOE. Conclusion: The influence of genetic risk in a specific tau-related biological pathway may make an individual more susceptible to tau pathology, resulting in cognitive dysfunction in an early preclinical phase of the disease.

Analysis of Genotype-Phenotype Correlations in Patients With Degenerative Dementia Through the Whole Exome Sequencing

Background: Sporadic dementias generally occur in older age and are highly polygenic, which indicates some patients transmitted in a poly-genes hereditary fashion. Objective: Our study aimed to analyze the correlations of genetic features with clinical symptoms in patients with degenerative dementia. Methods: We recruited a group of 84 dementia patients and conducted the whole exome sequencing (WES). The data were analyzed focusing on 153 dementia-related causing and susceptible genes. Results: According to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines, we identified four reported pathogenic variants, namely, PSEN1 c.A344G, APP c.G2149A, MAPT c.G1165A, and MAPT c.G742A, one reported likely pathogenic variant, namely, PSEN2 c.G100A, one novel pathogenic variants, SQSTM1 c.C671A, and three novel likely pathogenic variants, namely, ABCA7 c.C4690T, ATP13A2 c.3135delC, and NOS3 c.2897-2A > G. 21 variants with uncertain significance in PSEN2, C9orf72, NOTCH3, ABCA7, ERBB4, GRN, MPO, SETX, SORL1, NEFH, ADCM10, and SORL1, etc., were also detected in patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD). Conclusion: The new variants in dementia-related genes indicated heterogeneity in pathogenesis and phenotype of degenerative dementia. WES could serve as an efficient diagnostic tool for detecting intractable dementia.

Prediction of clinical diagnosis of Alzheimer's disease, vascular, mixed, and all-cause dementia by a polygenic risk score and APOE status in a community-based cohort prospectively followed over 17 years

The strongest genetic risk factor for Alzheimer's disease (AD) is the ε4 allele of Apolipoprotein E (APOE) and recent genome-wide association meta-analyses have confirmed additional associated genetic loci with smaller effects. The aim of this study was to investigate the ability of an AD polygenic risk score (PRS) and APOE status to predict clinical diagnosis of AD, vascular (VD), mixed (MD), and all-cause dementia in a community-based cohort prospectively followed over 17 years and secondarily across age, sex, and education strata. A PRS encompassing genetic variants reaching genome-wide significant associations to AD (excluding APOE) from the most recent genome-wide association meta-analysis data was calculated and APOE status was determined in 5203 participants. During follow-up, 103, 111, 58, and 359 participants were diagnosed with AD, VD, MD, and all-cause dementia, respectively. Prediction ability of AD, VD, MD, and all-cause dementia by the PRS and APOE was assessed by multiple logistic regression and receiver operating characteristic curve analyses. The PRS per standard deviation increase in score and APOE4 positivity (≥1 ε4 allele) were significantly associated with greater odds of AD (OR, 95% CI: PRS: 1.70, 1.45-1.99; APOE4: 3.34, 2.24-4.99) and AD prediction accuracy was significantly improved when adding the PRS to a base model of age, sex, and education (ASE) (c-statistics: ASE, 0.772; ASE + PRS, 0.810). The PRS enriched the ability of APOE to discern AD with stronger associations than to VD, MD, or all-cause dementia in a prospective community-based cohort.

Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer's Disease

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.

Genetic variants in glutamate-, Aβ-, and tau-related pathways determine polygenic risk for Alzheimer's disease

Synapse loss is an early event in late-onset Alzheimer's disease (LOAD). In this study, we have assessed the capacity of a polygenic risk score (PRS) restricted to synapse-encoding loci to predict LOAD. We used summary statistics from the International Genetics of Alzheimer's Project genome-wide association meta-analysis of 74,046 patients for model construction and tested the "synaptic PRS" in 2 independent data sets of controls and pathologically confirmed LOAD. The mean synaptic PRS was 2.3-fold higher in LOAD than that in controls (p < 0.0001) with a predictive accuracy of 72% in the target data set (n = 439) and 73% in the validation data set (n = 136), a 5%-6% improvement compared with the APOE locus (p < 0.00001). The model comprises 8 variants from 4 previously identified (BIN1, PTK2B, PICALM, APOE) and 2 novel (DLG2, MINK1) LOAD loci involved in glutamate signaling (p = 0.01) or APP catabolism or tau binding (p = 0.005). As the simplest PRS model with good predictive accuracy to predict LOAD, we conclude that synapse-encoding genes are enriched for LOAD risk-modifying loci. The synaptic PRS could be used to identify individuals at risk of LOAD before symptom onset.

The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer's Disease

BACKGROUND

Over 20 single-nucleotide polymorphisms (SNPs) are associated with increased risk of Alzheimer's disease (AD). We categorized these loci into immunity, lipid metabolism, and endocytosis pathways, and associated the polygenic risk scores (PRS) calculated, with AD biomarkers in mild cognitive impairment (MCI) subjects.

OBJECTIVE

The aim of this study was to identify associations between pathway-specific PRS and AD biomarkers in patients with MCI and healthy controls.

METHODS

AD biomarkers ([18F]Florbetapir-PET SUVR, FDG-PET SUVR, hippocampal volume, CSF tau and amyloid-β levels) and neurocognitive tests scores were obtained in 258 healthy controls and 451 MCI subjects from the ADNI dataset at baseline and at 24-month follow up. Pathway-related (immunity, lipid metabolism, and endocytosis) and total polygenic risk scores were calculated from 20 SNPs. Multiple linear regression analysis was used to test predictive value of the polygenic risk scores over longitudinal biomarker and cognitive changes.

RESULTS

Higher immune risk score was associated with worse cognitive measures and reduced glucose metabolism. Higher lipid risk score was associated with increased amyloid deposition and cortical hypometabolism. Total, immune, and lipid scores were associated with significant changes in cognitive measures, amyloid deposition, and brain metabolism.

CONCLUSION

Polygenic risk scores highlights the influence of specific genes on amyloid-dependent and independent pathways; and these pathways could be differentially influenced by lipid and immune scores respectively.

Genetic Risk Profiling in Parkinson's Disease and Utilizing Genetics to Gain Insight into Disease-Related Biological Pathways

Parkinson's disease (PD) is a complex disorder underpinned by both environmental and genetic factors. The latter only began to be understood around two decades ago, but since then great inroads have rapidly been made into deconvoluting the genetic component of PD. In particular, recent large-scale projects such as genome-wide association (GWA) studies have provided insight into the genetic risk factors associated with genetically ''complex'' PD (PD that cannot readily be attributed to single deleterious mutations). Here, we discuss the plethora of genetic information provided by PD GWA studies and how this may be utilized to generate polygenic risk scores (PRS), which may be used in the prediction of risk and trajectory of PD. We also comment on how pathway-specific genetic profiling can be used to gain insight into PD-related biological pathways, and how this may be further utilized to nominate causal PD genes and potentially druggable therapeutic targets. Finally, we outline the current limits of our understanding of PD genetics and the potential contribution of variation currently uncaptured in genetic studies, focusing here on uncatalogued structural variants.

Abnormal Homocysteine Metabolism: An Insight of Alzheimer's Disease from DNA Methylation

Alzheimer's disease (AD) is a chronic neurodegenerative disease in the central nervous system that has complex pathogenesis in the elderly. The current review focuses on the epigenetic mechanisms of AD, according to the latest findings. One of the best-characterized chromatin modifications in epigenetic mechanisms is DNA methylation. Highly replicable data shows that AD occurrence is often accompanied by methylation level changes of the AD-related gene. Homocysteine (Hcy) is not only an intermediate product of one-carbon metabolism but also an important independent risk factor of AD; it can affect the cognitive function of the brain by changing the one-carbon metabolism and interfering with the DNA methylation process, resulting in cerebrovascular disease. In general, Hcy may be an environmental factor that affects AD via the DNA methylation pathway with a series of changes in AD-related substance. This review will concentrate on the relation between DNA methylation and Hcy and try to figure out their rule in the pathophysiology of AD.

Genetic and polygenic risk score analysis for Alzheimer's disease in the Chinese population

INTRODUCTION

Dozens of Alzheimer's disease (AD)-associated loci have been identified in European-descent populations, but their effects have not been thoroughly investigated in the Hong Kong Chinese population.

METHODS

TaqMan array genotyping was performed for known AD-associated variants in a Hong Kong Chinese cohort. Regression analysis was conducted to study the associations of variants with AD-associated traits and biomarkers. Lasso regression was applied to establish a polygenic risk score (PRS) model for AD risk prediction.

RESULTS

SORL1 is associated with AD in the Hong Kong Chinese population. Meta-analysis corroborates the AD-protective effect of the SORL1 rs11218343 C allele. The PRS is developed and associated with AD risk, cognitive status, and AD-related endophenotypes. TREM2 H157Y might influence the amyloid beta 42/40 ratio and levels of immune-associated proteins in plasma.

DISCUSSION

SORL1 is associated with AD in the Hong Kong Chinese population. The PRS model can predict AD risk and cognitive status in this population.

Alzheimer Disease Pathology-Associated Polymorphism in a Complex Variable Number of Tandem Repeat Region Within the MUC6 Gene, Near the AP2A2 Gene

We found evidence of late-onset Alzheimer disease (LOAD)-associated genetic polymorphism within an exon of Mucin 6 (MUC6) and immediately downstream from another gene: Adaptor Related Protein Complex 2 Subunit Alpha 2 (AP2A2). PCR analyses on genomic DNA samples confirmed that the size of the MUC6 variable number tandem repeat (VNTR) region was highly polymorphic. In a cohort of autopsied subjects with quantitative digital pathology data (n = 119), the size of the polymorphic region was associated with the severity of pTau pathology in neocortex. In a separate replication cohort of autopsied subjects (n = 173), more pTau pathology was again observed in subjects with longer VNTR regions (p = 0.031). Unlike MUC6, AP2A2 is highly expressed in human brain. AP2A2 expression was lower in a subset analysis of brain samples from persons with longer versus shorter VNTR regions (p = 0.014 normalizing with AP2B1 expression). Double-label immunofluorescence studies showed that AP2A2 protein often colocalized with neurofibrillary tangles in LOAD but was not colocalized with pTau proteinopathy in progressive supranuclear palsy, or with TDP-43 proteinopathy. In summary, polymorphism in a repeat-rich region near AP2A2 was associated with neocortical pTau proteinopathy (because of the unique repeats, prior genome-wide association studies were probably unable to detect this association), and AP2A2 was often colocalized with neurofibrillary tangles in LOAD.

The MUC6/AP2A2 Locus and Its Relevance to Alzheimer's Disease: A Review

We recently reported evidence of Alzheimer's disease (AD)-linked genetic variation within the mucin 6 (MUC6) gene on chromosome 11p, nearby the adaptor-related protein complex 2 subunit alpha 2 (AP2A2) gene. This locus has interesting features related to human genomics and clinical research. MUC6 gene variants have been reported to potentially influence viral-including herpesvirus-immunity and the gut microbiome. Within the MUC6 gene is a unique variable number of tandem repeat (VNTR) region. We discovered an association between MUC6 VNTR repeat expansion and AD pathologic severity, particularly tau proteinopathy. Here, we review the relevant literature. The AD-linked VNTR polymorphism may also influence AP2A2 gene expression. AP2A2 encodes a polypeptide component of the adaptor protein complex, AP-2, which is involved in clathrin-coated vesicle function and was previously implicated in AD pathogenesis. To provide background information, we describe some key knowledge gaps in AD genetics research. The "missing/hidden heritability problem" of AD is highlighted. Extensive portions of the human genome, including the MUC6 VNTR, have not been thoroughly evaluated due to limitations of existing high-throughput sequencing technology. We present and discuss additional data, along with cautionary considerations, relevant to the hypothesis that MUC6 repeat expansion influences AD pathogenesis.

Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling

Late-onset Alzheimer’s Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer’s disease.

Early-onset Alzheimer Disease and Its Variants

PURPOSE OF REVIEW

Early-onset Alzheimer disease (AD) is defined as having an age of onset younger than 65 years. While early-onset AD is often overshadowed by the more common late-onset AD, recognition of the differences between early- and late-onset AD is important for clinicians.

RECENT FINDINGS

Early-onset AD comprises about 5% to 6% of cases of AD and includes a substantial percentage of phenotypic variants that differ from the usual amnestic presentation of typical AD. Characteristics of early-onset AD in comparison to late-onset AD include a larger genetic predisposition (familial mutations and summed polygenic risk), more aggressive course, more frequent delay in diagnosis, higher prevalence of traumatic brain injury, less memory impairment and greater involvement of other cognitive domains on presentation, and greater psychosocial difficulties. Neuroimaging features of early-onset AD in comparison to late-onset AD include greater frequency of hippocampal sparing and posterior neocortical atrophy, increased tau burden, and greater connectomic changes affecting frontoparietal networks rather than the default mode network.

SUMMARY

Early-onset AD differs substantially from late-onset AD, with different phenotypic presentations, greater genetic predisposition, and differences in neuropathologic burden and topography. Early-onset AD more often presents with nonamnestic phenotypic variants that spare the hippocampi and with greater tau burden in posterior neocortices. The early-onset AD phenotypic variants involve different neural networks than typical AD. The management of early-onset AD is similar to that of late-onset AD but with special emphasis on targeting specific cognitive areas and more age-appropriate psychosocial support and education.

From Polygenic Scores to Precision Medicine in Alzheimer's Disease: A Systematic Review

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.

Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment

Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer's disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer's Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups (p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.

The Coming of Age of the Angiotensin Hypothesis in Alzheimer's Disease: Progress Toward Disease Prevention and Treatment?

There is wide recognition of a complex association between midlife hypertension and cardiovascular disease and later development of Alzheimer’s disease (AD) and cognitive impairment. While significant progress has been made in reducing rates of mortality and morbidity due to cardiovascular disease over the last thirty years, progress towards effective treatments for AD has been slower. Despite the known association between hypertension and dementia, research into each disease has largely been undertaken in parallel and independently. Yet over the last decade and a half, the emergence of converging findings from pre-clinical and clinical research has shown how the renin angiotensin system (RAS), which is very important in blood pressure regulation and cardiovascular disease, warrants careful consideration in the pathogenesis of AD. Numerous components of the RAS have now been found to be altered in AD such that the multifunctional and potent vasoconstrictor angiotensin II, and similarly acting angiotensin III, are greatly altered at the expense of other RAS signaling peptides considered to contribute to neuronal and cognitive function. Collectively these changes may contribute to many of the neuropathological hallmarks of AD, as well as observed progressive deficiencies in cognitive function, while also linking elements of a number of the proposed hypotheses for the cause of AD. This review discusses the emergence of the RAS and its likely importance in AD, not only because of the multiple facets of its involvement, but also perhaps fortuitously because of the ready availability of numerous RAS-acting drugs, that could be repurposed as interventions in AD.

Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.

Relative risk for Alzheimer disease based on complete family history

OBJECTIVE

The inherited component for Alzheimer disease (AD) risk has focused on close relatives; consideration of the full family history may improve accuracy and utility of risk estimates.

METHODS

A population resource including a genealogy of Utah pioneers from the 1800s linked to Utah death certificates was used to estimate relative risk for AD based on specific family history constellations, including from first- to third-degree relatives.

RESULTS

Any affected first-degree relatives (FDR) significantly increased risk of AD (≥1 FDRs: relative risk [RR] 1.73, 95% confidence interval [CI] [1.59-1.87]; ≥2 FDRs: RR 3.98 [3.26-4.82]; ≥3 FDRs: RR 2.48 [1.07-4.89]; ≥4 FDRs: RR 14.77 [5.42-32.15]). Affected second-degree relatives (SDR) increased risk even in the presence of affected FDRs (FDR = 1 with SDR = 2: RR 21.29 [5.80-54.52]). AD only in third-degree relatives (TDR) also increased risk (FDR = 0, SDR = 0, TDR ≥3: RR 1.43 [1.21-1.68]). Mixed evidence was observed for differences in risk based on maternal compared to paternal inheritance; higher risks for men than women with equivalent family history, and higher risk for individuals with at least one affected FDR regardless of the relative's age at death, were observed.

CONCLUSIONS

This population-based estimation of RRs for AD based on family history ascertained from extended genealogy data indicates that inherited genetic factors have a broad influence, extending beyond immediate relatives. Providers should consider the full constellation of family history when counseling patients and families about their risk of AD.

Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis

OBJECTIVE

The relationships between early growth parameters and bone mineral density (BMD) remain elusive now. In this study, we performed a large scale polygenic risk score (PRS) analysis to evaluate the potential impact of early growth parameters on the variations of BMD.

METHODS

We used 2286 Caucasian subjects as cohort 1 and 3404 Framingham Heart Study (FHS) subjects as cohort 2 in this study. BMD at ulna & radius, hip and spine were measured using dual energy X-ray absorptiometry. BMD values were adjusted for age, sex, height and weight as covariates. Genome-wide single-nucleotide polymorphism (SNP) genotyping of the 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The GWAS datasets of early growth parameters were driven from the Early Growth Genetics Consortium, including birth weight (BW), birth head circumference (BHC), childhood body mass index (CBMI), pubertal height growth related indexes and tanner stage. Polygenic Risk Score (PRSice) and linkage disequilibrium (LD) score regression analysis were conducted to assess the genetic correlation between early growth parameters and BMD.

RESULTS

We detected significant genetic correlations in cohort 1, such as total spine BMD vs. CBMI (p value = 1.51 × 10^-4, rg = 0.4525), right ulna and radius BMD vs. CBMI (p value = 1.51 × 10^-4, rg = 0.4399) and total body BMD vs. tanner stage (p value = 7.00 × 10^-4, rg = -0.0721). For cohort 2, significant correlations were observed for total spine BMD vs. height change standard deviation score (SDS) between 8 years and adult (denoted as PGF + PGM) (p value = 3.97 × 10^-4, rg = -0.1425), femoral neck BMD vs. the timing of peak height velocity by looking at the height change SDS between age 14 years and adult (denoted as PTF + PTM) (p value = 7.04 × 10^-4, rg = -0.2185), and total spine BMD vs. PTF + PTM (p value = 6.86 × 10^-4, rg = -0.2180).

CONCLUSION

Our study results suggest that some early growth parameters could affect the variations of BMD.

The genetic risk of Alzheimer's disease beyond APOE ε4: systematic review of Alzheimer's genetic risk scores

The ε4 allele of Apolipoprotein E (APOE) is the strongest known genetic risk factor of Alzheimer's disease (AD) but does not account for the entirety of genetic risk. Genetic risk scores (GRSs) incorporating additional genetic variants have been developed to determine the genetic risk for AD, yet there is no systematic review assessing the contribution of GRSs for AD beyond the effect of APOE ε4. The purpose of this systematic PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses)-based review was to summarize original research studies that have developed and validated a GRS for AD utilizing associated single nucleotide polymorphisms (SNPs). The PubMed and Web of Science databases were searched on April 6, 2018 and screening was completed on 2018 citations by two independent reviewers. Eighteen studies published between 2010 and 2018 were included in the review. All GRSs expressed significant associations or discrimination capability of AD when compared to clinically normal controls; however, GRS prediction of MCI to AD conversion was mixed. APOE ε4 status was more predictive of AD than the GRSs, although the GRSs did add to AD prediction accuracy beyond APOE ε4. GRSs might contribute to identifying genetic risk of AD beyond APOE. However, additional studies are warranted to assess the performance of GRSs in independent longitudinal cohorts.

The Whole Exome Sequencing Clarifies the Genotype- Phenotype Correlations in Patients with Early-Onset Dementia

Our study aimed to identify the underlying causes in patients with early onset dementia by clinical and genetic exploration. We recruited a group of 38 patients with early-onset dementia. Firstly, hexanucleotide repeat expansions in C9ORF72 gene were screened in all subjects to exclude the possibility of copy number variation. Then, the whole exome sequencing (WES) was conducted, and the data were analyzed focusing on 89 dementia-related causing and susceptible genes. The effects of identified variants were classified according to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. There were no pathogenic expansions in C9ORF72 detected. According to the ACMG standards and guidelines, we identified five known pathogenic mutations, PSEN1 P284L, PSEN1c.857-1G>A, PSEN1 I143T, PSEN1 G209E and MAPT G389R, and one novel pathogenic mutation APP K687N. All these mutations caused dementia with the mean onset age of 38.3 (range from 27 to 51) and rapid progression. Eleven variants with uncertain significance were also detected and needed further verification. The clinical phenotypes of dementia are heterogeneous, with both onset ages and clinical features being influenced by mutation position as well as the causative gene. WES can serve as efficient diagnostic tools for different heterogeneous dementia.