Genetic risk for Alzheimer's disease is distinct from genetic risk for amyloid deposition

Objectives Alzheimer's disease (AD) is the most common form of dementia and is responsible for a huge and growing health care burden in the developed and developing world. The Polygenic Risk Score (PRS) approach has shown 75%‐84% prediction accuracy of identifying individuals with AD risk. Methods In this study we tested the prediction accuracy of AD, MCI and amyloid deposition risks with PRS, including and excluding APOE genotypes in a large publicly available data set with extensive phenotypic data: the Alzheimer's Disease Neuroimaging Initiative cohort. Among MCI individuals with amyloid positive status we examined PRS prediction accuracy in those who converted to AD. In addition, we divided polygenic risk score by biological pathways and tested them independently for distinguishing between AD, MCI and amyloid deposition. Results We found that AD and MCI are predicted by both APOE genotype and PRS (AUC=0.82% and 68%, respectively). Amyloid deposition is predicted by APOE only (AUC=79%). Further progression to AD of individuals with MCI and amyloid positive status is predicted by PRS over and above APOE (AUC=67%)

Polygenic risk and hazard scores for Alzheimer's disease prediction.

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)

Genetic determinants of survival in progressive supranuclear palsy: a genome-wide association study.

BACKGROUND: The genetic basis of variation in the progression of primary tauopathies has not been determined. We aimed to identify genetic determinants of survival in progressive supranuclear palsy (PSP). METHODS: In stage one of this two stage genome-wide association study (GWAS), we included individuals with PSP, diagnosed according to pathological and clinical criteria, from two separate cohorts: the 2011 PSP GWAS cohort, from brain banks based at the Mayo Clinic (Jacksonville, FL, USA) and in Munich (Germany), and the University College London PSP cohort, from brain banks and the PROSPECT study, a UK-wide longitudinal study of patients with atypical parkinsonian syndromes. Individuals were included if they had clinical data available on sex, age at motor symptom onset, disease duration (from motor symptom onset to death or to the date of censoring, Dec 1, 2019, if individuals were alive), and PSP phenotype (with reference to the 2017 Movement Disorder Society criteria). Genotype data were used to do a survival GWAS using a Cox proportional hazards model. In stage two, data from additional individuals from the Mayo Clinic brain bank, which were obtained after the 2011 PSP GWAS, were used for a pooled analysis. We assessed the expression quantitative trait loci (eQTL) profile of variants that passed genome-wide significance in our GWAS using the Functional Mapping and Annotation of GWAS platform, and did colocalisation analyses using the eQTLGen and PsychENCODE datasets. FINDINGS: Data were collected and analysed between Aug 1, 2016, and Feb 1, 2020. Data were available for 1001 individuals of white European ancestry with PSP in stage one. We found a genome-wide significant association with survival at chromosome 12 (lead single nucleotide polymorphism rs2242367, p=7·5 × 10-10, hazard ratio 1·42 [95% CI 1·22-1·67]). rs2242367 was associated with survival in the individuals added in stage two (n=238; p=0·049, 1·22 [1·00-1·48]) and in the pooled analysis of both stages (n=1239; p=1·3 × 10-10, 1·37 [1·25-1·51]). An eQTL database screen revealed that rs2242367 is associated with increased expression of LRRK2 and two long intergenic non-coding RNAs (lncRNAs), LINC02555 and AC079630.4, in whole blood. Although we did not detect a colocalisation signal for LRRK2, analysis of the PSP survival signal and eQTLs for LINC02555 in the eQTLGen blood dataset revealed a posterior probability of hypothesis 4 of 0·77, suggesting colocalisation due to a single shared causal variant. INTERPRETATION: Genetic variation at the LRRK2 locus was associated with survival in PSP. The mechanism of this association might be through a lncRNA-regulated effect on LRRK2 expression because LINC02555 has previously been shown to regulate LRRK2 expression. LRRK2 has been associated with sporadic and familial forms of Parkinson's disease, and our finding suggests a genetic overlap with PSP. Further functional studies will be important to assess the potential of LRRK2 modulation as a disease-modifying therapy for PSP and related tauopathies. FUNDING: PSP Association, CBD Solutions, Medical Research Council (UK)

Information Technology to Support Improved Care For Chronic Illness

BackgroundIn populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.ObjectiveTo summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.DesignA systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.ResultsThe expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.ConclusionsSpecific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care

Implementation of consumer providers into mental health intensive case management teams.

In mental health care, consumer providers (CPs) are individuals with serious mental illness (SMI) who draw upon their lived experiences while providing services to others with SMI. Implementation of CPs has proven to be challenging in a variety of settings. The PEER project (Peers Enhancing Recovery) involved rolling out CPs using an implementation science model and evaluating implementation and impact in mental health treatment settings (three intervention, three control). In qualitative interviews, facilitators and challenges to implementation were described by the CPs, their team members, clients, and study researchers. Site preparation, external facilitation, and positive, reinforcing experiences with CPs facilitated implementation. Role definitions and deficiencies in CPs' technical knowledge posed challenges to implementation. Sustainability was not realized due to insufficient resources. However, implementation was positive overall, characterized by diffusion of innovation concepts of high relative advantage, strong trialability, compatibility with prevailing norms, compelling observability, and relatively low complexity. By preparing and working systematically with intervention sites to incorporate new services, implementation was strengthened and challenges were minimized

Information technology to support improved care for chronic illness.

BackgroundIn populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.ObjectiveTo summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.DesignA systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.ResultsThe expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.ConclusionsSpecific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care