Polygenic risk scores in Alzheimer’s disease: current applications and future directions

Genome-wide association studies have identified nearly 40 genome-wide significant single nucleotide polymorphisms (SNPs) which are associated with Alzheimer's Disease (AD). Due to the polygenicity of AD, polygenic risk scores (PRS) have shown high potential for AD risk prediction. PRSs have been shown to successfully discriminate between AD cases and controls achieving a prediction accuracy of up to 84% based on area under the receiver operating curve. The prediction accuracy in AD is higher compared with other complex genetic disorders. PRS can be restricted to SNPs which reside in biologically relevant gene-sets; the predictive value of these gene-sets in the general population is not as high as genome-wide PRS, but they may play an important role to identify mechanisms of disease development and inform biological experiments. Multiple methods are available to derive PRSs, such as selecting SNPs based on statistical evidence of association with the disease or using prior evidence for SNP selection. All methods have advantages, but PRS produced using different methodologies are often not comparable, and results should be interpreted with care. Similarly, this is true when PRS is based on different background populations. With the exponential growth in development of digital electronic devices it is easy to calculate an individual's disease risk using public databases. A major limitation for the utility of PRSs is that the risk score is sample and method dependent. Therefore, replicability and interpretability of PRS is an important issue. PRS can be used to determine the probability of developing disease which incorporates information about disease risk in the general population or in a specific AD risk group. It is essential to consult with genetic counselors to ensure genetic risk is communicated appropriately

Translating genetic risk of Alzheimer’s disease into mechanistic insight and drug targets

To provide better prevention and treatment, we need to understand the environmental and genetic risks of Alzheimer’s disease (AD). However, the definition of AD has been confounded with dementia in many studies. Thus, overinterpretation of genetic findings with regard to mechanisms and drug targets may explain, in part, controversies in the field. Here, we analyze the different forms of genetic risk of AD and how these can be used to model disease. We stress the importance of studying gene variants in the right cell types and in the right pathological context. The lack of mechanistic understanding of genetic variation has become the major bottleneck in the search for new drug targets for AD

Genes, pathways and risk prediction in Alzheimer's disease

Hemophilia is an X-linked inherited bleeding disorder, resulting from defects in the F8 (hemophilia A) or F9 (hemophilia B) genes. Persons with hemophilia have bleeding episodes into the soft tissues and joints, which are treated with self-infusion of factor VIII or IX concentrates. Hemophilia provides an attractive target for gene therapy studies, due to the monogenic nature of these disorders and easily measurable endpoints (factor levels and bleed rates). All successful, pre-clinical and clinical studies to date have utilized recombinant adeno-associated viral (AAV) vectors for factor VIII or IX hepatocyte transduction. Recent clinical data have presented normalization of factor levels in some patients with improvements in bleed rate and quality of life. The main toxicity seen within these studies has been early transient elevation in liver enzymes, with variable effect on transgene expression. Although long-term data are awaited, durable expression has been seen within the hemophilia dog model with no late-toxicity or oncogenesis. There are a number of phase III studies currently recruiting; however, there may be some limitations in translating these data to clinical practice, due to inclusion/exclusion criteria. AAV-based gene therapy is one of a number of novel approaches for treatment of hemophilia with other gene therapy (in vivo and ex vivo) and non-replacement therapies progressing through clinical trials. Availability of these high-cost novel therapeutics will require evolution of both clinical and financial healthcare services to allow equitable personalization of care for persons with hemophilia

Genome-wide association studies for Alzheimer’s disease: bigger is not always better

As the size of genome-wide association studies increase, the number of associated trait loci identified inevitably increase. One welcomes this if it allows the better delineation of the pathways to disease and increases the accuracy of genetic prediction of disease risk through polygenic risk score analysis. However, there are several problems in the continuing increase in the genome-wide analysis of ‘Alzheimer’s disease’. In this review, we have systematically assessed the history of Alzheimer’s disease genome-wide association studies, including their sample sizes, age and selection/assessment criteria of cases and controls and heritability explained by these disease genome-wide association studies. We observe that nearly all earlier disease genome-wide association studies are now part of all current disease genome-wide association studies. In addition, the latest disease genome-wide association studies include (i) only a small fraction (∼10%) of clinically screened controls, substituting for them population-based samples which are systematically younger than cases, and (ii) around 50% of Alzheimer’s disease cases are in fact ‘proxy dementia cases’. As a consequence, the more genes the field finds, the less the heritability they explain. We highlight potential caveats this situation creates and discuss some of the consequences occurring when translating the newest Alzheimer’s disease genome-wide association study results into basic research and/or clinical practice

Are Alzheimer's and coronary artery diseases genetically related to longevity?

IntroductionIn the last decade researchers have attempted to investigate the shared genetic architecture of longevity and age-related diseases and assess whether the increased longevity in certain people is due to protective alleles in the risk genes for a particular condition or whether there are specific “longevity” genes increasing the lifespan independently of age-related conditions' risk genes. The aim of this study was to investigate the shared genetic component between longevity and two age-related conditions.MethodsWe performed a cross-trait meta-analysis of publicly available genome-wide data for Alzheimer's disease, coronary artery disease and longevity using a subset-based approach provided by the R package ASSET.ResultsDespite the lack of strong genetic correlation between longevity and the two diseases, we identified 38 genome-wide significant lead SNPs across 22 independent genomic loci. Of them 6 were found to be potentially shared among the three traits mapping to genes including DAB2IP, DNM2, FCHO1, CLPTM1, and SNRPD2. We also identified 19 novel genome-wide associations for the individual traits in this study. Functional annotations and biological pathway enrichment analyses suggested that pleiotropic variants are involved in clathrin-mediated endocytosis and plasma lipoprotein and neurotransmitter clearance processes.DiscussionIn summary, we have been able to advance in the knowledge of the genetic overlap existing among longevity and the two most common age-related disorders

Approximate projectors in singular spectrum analysis

Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given

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

Background Alzheimer’s disease, among other neurodegenerative disorders, spans decades in individuals’ life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer’s disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE-ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals. Methods We estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups. Results The AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+, the probability of AD grows from 0.03 to 0.18 (without APOE) and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08–0.6 and 0.3–0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3. Conclusions Our approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach has potential for use in a clinical setting and can easily be updated for novel rare variants and for other populations or confounding factors when appropriate genome-wide association data become available

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