Genes, pathways and risk prediction in Alzheimer's disease

The failure of recent clinical trials in Alzheimer's disease has highlighted the need for the development of a more complete understanding of the pathogenesis of the disorder and also a belief that therapies may only work if given very early in the disease process before overt symptoms occur. The rare, early onset forms of the disease are all caused by mutations which make amyloid deposition a more likely event. Here we discuss the recent data showing that, in contrast, much of the risk of late onset disease is encoded by loci involved in lipid metabolism and/or encoded by microglia. We discuss these finding and suggest that amyloid induced membrane damage may be a key factor in disease and also review the evidence that genome wide genetic analysis can substantially help in the prediction of those individuals at high risk of disease in the general population

New cases of dementia are rising in elderly populations in Wales, UK

Dementia is one of the most common diseases in elderly populations, and older populations are one of the fastest growing groups globally. Consequently, the number of people developing and living with dementia is likely to grow. Using longitudinal medical records from Wales, UK between 1999 and 2018, diagnoses of overall dementia and common subtypes were combined with demographic data to assess numbers of new and existing cases per year. Data extraction resulted in 161,186 diagnoses from 116,645 individuals. Mean age at diagnosis of dementia increased over this period, resulting in fewer younger people with the disease. New cases of dementia have risen, as has the number of people living with dementia. Individuals with dementia are also living longer, even accounting for their older age. This may present a challenge for healthcare systems as the number of elderly people living with dementia is expected to continue to grow

Longevity GWAS Using the Drosophila Genetic Reference Panel

We used 197 Drosophila melanogaster Genetic Reference Panel (DGRP) lines to perform a genome-wide association analysis for virgin female lifespan, using ~2M common single nucleotide polymorphisms (SNPs). We found considerable genetic variation in lifespan in the DGRP, with a broad-sense heritability of 0.413. There was little power to detect signals at a genome-wide level in single-SNP and gene-based analyses. Polygenic score analysis revealed that a small proportion of the variation in lifespan (~4.7%) was explicable in terms of additive effects of common SNPs (≥2% minor allele frequency). However, several of the top associated genes are involved in the processes previously shown to impact ageing (eg, carbohydrate-related metabolism, regulation of cell death, proteolysis). Other top-ranked genes are of unknown function and provide promising candidates for experimental examination. Genes in the target of rapamycin pathway (TOR; Chrb, slif, mipp2, dredd, RpS9, dm) contributed to the significant enrichment of this pathway among the top-ranked 100 genes (p = 4.79×10(-06)). Gene Ontology analysis suggested that genes involved in carbohydrate metabolism are important for lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. This analysis suggests that our understanding of the genetic basis of natural variation in lifespan from induced mutations is incomplete

A novel computational approach for predicting complex phenotypes in Drosophila (starvation-sensitive and sterile) by deriving their gene expression signatures from public data

Many research teams perform numerous genetic, transcriptomic, proteomic and other types of omic experiments to understand molecular, cellular and physiological mechanisms of disease and health. Often (but not always), the results of these experiments are deposited in publicly available repository databases. These data records often include phenotypic characteristics following genetic and environmental perturbations, with the aim of discovering underlying molecular mechanisms leading to the phenotypic responses. A constrained set of phenotypic characteristics is usually recorded and these are mostly hypothesis driven of possible to record within financial or practical constraints. We present a novel proof-of-principal computational approach for combining publicly available gene-expression data from control/mutant animal experiments that exhibit a particular phenotype, and we use this approach to predict unobserved phenotypic characteristics in new experiments (data derived from EBI’s ArrayExpress and ExpressionAtlas respectively). We utilised available microarray gene-expression data for two phenotypes (starvation-sensitive and sterile) in Drosophila. The data were combined using a linear-mixed effects model with the inclusion of consecutive principal components to account for variability between experiments in conjunction with Gene Ontology enrichment analysis. We present how available data can be ranked in accordance to a phenotypic likelihood of exhibiting these two phenotypes using random forest. The results from our study show that it is possible to integrate seemingly different gene-expression microarray data and predict a potential phenotypic manifestation with a relatively high degree of confidence (>80% AUC). This provides thus far unexplored opportunities for inferring unknown and unbiased phenotypic characteristics from already performed experiments, in order to identify studies for future analyses. Molecular mechanisms associated with gene and environment perturbations are intrinsically linked and give rise to a variety of phenotypic manifestations. Therefore, unravelling the phenotypic spectrum can help to gain insights into disease mechanisms associated with gene and environmental perturbations. Our approach uses public data that are set to increase in volume, thus providing value for money

Polygenic Risk Score Analysis of Alzheimer’s Disease in Cases without APOE4 or APOE2 Alleles

The We and others have previously shown that polygenic risk score analysis (PRS) has considerable predictive utility for identifying those at high risk of developing Alzheimer’s disease (AD) with an area under the curve (AUC) of >0.8. However, by far the greatest determinant of this risk is the apolipoprotein E locus with the E4 allele alone giving an AUC of ∼0.68 and the inclusion of the protective E2 allele increasing this to ∼0.69 in a clinical cohort. An important question is to determine how good PRS is at predicting risk in those who do not carry the E4 allele (E3 homozygotes, E3E2 and E2E2) and in those who carry neither the E4 or E2 allele (i.e. E3 homozygotes). Previous studies have shown that PRS remains a significant predictor of AD risk in clinical cohorts after controlling for APOE ε4 carrier status. In this study we assess the accuracy of PRS prediction in a cohort of pathologically confirmed AD cases and controls. The exclusion of APOE4 carriers has surprisingly little effect on the PRS prediction accuracy (AUC ∼0.83 [95% CI: 0.80-0.86]), and the accuracy remained higher than that in clinical cohorts with APOE included as a predictor. From a practical perspective this suggests that PRS analysis will have predictive utility even in E4 negative individuals and may be useful in clinical trial design

Proton pump inhibitors and dementia risk: Evidence from a cohort study using linked routinely collected national health data in Wales, UK

Objectives: Proton pump inhibitors (PPIs) are commonly prescribed for prevention and treatment of gastrointestinal conditions or for gastroprotection from other drugs. Research suggests they are linked to increased dementia risk. We use linked national health data to examine the association between PPI use and the development of incident dementia. Methods and findings: A population-based study using electronic health-data from the Secure Anonymised Information Linkage (SAIL) Databank, Wales (UK) from 1999 to 2015. Of data available on 3,765,744 individuals, a cohort who had ever been prescribed a PPI was developed (n=183,968) for people aged 55 years and over and compared to non-PPI exposed individuals (131,110). Those with prior dementia, mild-cognitive-impairment or delirium codes were excluded. Confounding factors included comorbidities and/or drugs associated with them. Comorbidities might include head injury and some examples of medications include antidepressants, antiplatelets and anticoagulants. These commonly prescribed drugs were investigated as it was not feasible to explore all drugs in this study. The main outcome was a diagnosis of incident dementia. Cox proportional hazard regression modelling was used to calculate the Hazard ratio (HR) of developing dementia in PPI-exposed compared to unexposed individuals while controlling for potential confounders. The mean age of the PPI exposed individuals was 69.9 years and 39.8% male while the mean age of the unexposed individuals was 72.1 years and 41.1% male. The rate of PPI usage was 58.4% (183,968) and incident dementia rate was 11.8% (37,148/315,078). PPI use was associated with decreased dementia risk (HR: 0.67, 95% CI: 0.65 to 0.67, p<0.01). Conclusions: This study, using large-scale, multi-centre health-data was unable to confirm an association between PPI use and increased dementia risk. Previously reported links may be associated with confounders of people using PPI’s, such as increased risk of cardiovascular disease and/or depression and their associated medications which may be responsible for any increased risk of developing dementia

Association of genetic liability for psychiatric disorders with accelerometer-assessed physical activity in the UK Biobank.

Levels of activity are often affected in psychiatric disorders and can be core symptoms of illness. Advances in technology now allow the accurate assessment of activity levels but it remains unclear whether alterations in activity arise from shared risk factors for developing psychiatric disorders, such as genetics, or are better explained as consequences of the disorders and their associated factors. We aimed to examine objectively-measured physical activity in individuals with psychiatric disorders, and assess the role of genetic liability for psychiatric disorders on physical activity. Accelerometer data were available on 95,529 UK Biobank participants, including measures of overall mean activity and minutes per day of moderate activity, walking, sedentary activity, and sleep. Linear regressions measured associations between psychiatric diagnosis and activity levels, and polygenic risk scores (PRS) for psychiatric disorders and activity levels. Genetic correlations were calculated between psychiatric disorders and different types of activity. Having a diagnosis of schizophrenia, bipolar disorder, depression, or autism spectrum disorders (ASD) was associated with reduced overall activity compared to unaffected controls. In individuals without a psychiatric disorder, reduced overall activity levels were associated with PRS for schizophrenia, depression, and ASD. ADHD PRS was associated with increased overall activity. Genetic correlations were consistent with PRS findings. Variation in physical activity is an important feature across psychiatric disorders. Whilst levels of activity are associated with genetic liability to psychiatric disorders to a very limited extent, the substantial differences in activity levels in those with psychiatric disorders most likely arise as a consequences of disorder-related factors

Alzheimer’s disease-associated complement gene variants influence plasma complement protein levels

Background: Alzheimer’s disease (AD) has been associated with immune dysregulation in biomarker and genome-wide association studies (GWAS). GWAS hits include the genes encoding complement regulators clusterin (CLU) and complement receptor 1 (CR1), recognised as key players in AD pathology, and complement proteins have been proposed as biomarkers. Main body: To address whether changes in plasma complement protein levels in AD relate to AD-associated complement gene variants we first measured relevant plasma complement proteins (clusterin, C1q, C1s, CR1, factor H) in a large cohort comprising early onset AD (EOAD; n = 912), late onset AD (LOAD; n = 492) and control (n = 504) donors. Clusterin and C1q were significantly increased (p < 0.001) and sCR1 and factor H reduced (p < 0.01) in AD plasma versus controls. ROC analyses were performed to assess utility of the measured complement biomarkers, alone or in combination with amyloid beta, in predicting AD. C1q was the most predictive single complement biomarker (AUC 0.655 LOAD, 0.601 EOAD); combining C1q with other complement or neurodegeneration makers through stepAIC-informed models improved predictive values slightly. Effects of GWS SNPs (rs6656401, rs6691117 in CR1; rs11136000, rs9331888 in CLU; rs3919533 in C1S) on protein concentrations were assessed by comparing protein levels in carriers of the minor vs major allele. To identify new associations between SNPs and changes in plasma protein levels, we performed a GWAS combining genotyping data in the cohort with complement protein levels as endophenotype. SNPs in CR1 (rs6656401), C1S (rs3919533) and CFH (rs6664877) reached significance and influenced plasma levels of the corresponding protein, whereas SNPs in CLU did not influence clusterin levels. Conclusion: Complement dysregulation is evident in AD and may contribute to pathology. AD-associated SNPs in CR1, C1S and CFH impact plasma levels of the encoded proteins, suggesting a mechanism for impact on disease risk