Genetic analysis suggests high misassignment rates in clinical Alzheimer's cases and controls

Genetic case-control association studies are often based on clinically ascertained cases and population or convenience controls. It is known that some of the controls will contain cases, as they are usually not screened for the disease of interest. However, even clinically assessed cases and controls can be misassigned. For Alzheimer's disease (AD), it is important to know the accuracy of the clinical assignment. The predictive accuracy of AD risk by polygenic risk score analysis has been reported in both clinical and pathologically confirmed cohorts. The genetic risk prediction can provide additional insights to inform classification of subjects to case and control sets at a preclinical stage. In this study, we take a mathematical approach and aim to assess the importance of a genetic component for the assignment of subjects to AD-positive and -negative groups, and provide an estimate of misassignment rates (MARs) in AD case/control cohorts accounting for genetic prediction modeling results. The derived formulae provide a tool to estimate MARs in any sample. This approach can also provide an estimate of the maximal and minimal MARs and therefore could be useful for statistical power estimation at the study design stage. We illustrate this approach in 2 independent clinical cohorts and estimate misdiagnosis rate up to 36% in controls unscreened for the APOE genotype, and up to 29% when E3 homozygous subjects are used as controls in clinical studies

Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Genetic correlation ([Formula: see text]) between traits can offer valuable insight into underlying shared biological mechanisms. Neurodegenerative diseases overlap neuropathologically and often manifest comorbid neuropsychiatric symptoms. However, global [Formula: see text] analyses show minimal [Formula: see text] among neurodegenerative and neuropsychiatric diseases. Importantly, local [Formula: see text] s can exist in the absence of global relationships. To investigate this possibility, we applied LAVA, a tool for local [Formula: see text] analysis, to genome-wide association studies of 3 neurodegenerative diseases (Alzheimer's disease, Lewy body dementia and Parkinson's disease) and 3 neuropsychiatric disorders (bipolar disorder, major depressive disorder and schizophrenia). We identified several local [Formula: see text] s missed in global analyses, including between (i) all 3 neurodegenerative diseases and schizophrenia and (ii) Alzheimer's and Parkinson's disease. For those local [Formula: see text] s identified in genomic regions containing disease-implicated genes, such as SNCA, CLU and APOE, incorporation of expression quantitative trait loci identified genes that may drive genetic overlaps between diseases. Collectively, we demonstrate that complex genetic relationships exist among neurodegenerative and neuropsychiatric diseases, highlighting putative pleiotropic genomic regions and genes. These findings imply sharing of pathogenic processes and the potential existence of common therapeutic targets

Measuring heritable contributions to Alzheimer's disease: polygenic risk score analysis with twins

The heritability of Alzheimer’s disease estimated from twin studies is greater than the heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer’s disease polygenic risk scores and heritability from twin models, to provide insight into the role of measured genetic variants and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer’s disease was conducted between 1986 and 2016 and is the first study to incorporate polygenic risk scores into biometrical twin models of Alzheimer’s disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs and 688 incomplete pairs) with clinically based diagnoses and registry follow-up (Mage = 85.28, SD = 7.02; 44% male; 431 cases and 1155 controls). We report contributions of polygenic risk scores at P < 1 × 10−5, considering a full polygenic risk score (PRS), PRS without the APOE region (PRS.no.APOE) and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer’s disease risk. The full PRS and PRS.no.APOE contributed 10.1 and 2.4% to Alzheimer’s disease risk, respectively. When APOE ɛ4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer’s disease risk, where APOE ɛ4 explained 9.3% and PRS.no.APOE dropped from 2.4 to 2.1%. The total genetic contribution to Alzheimer’s disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region accounts for much of the measurable genetic contribution to Alzheimer’s disease, with a smaller contribution from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood

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%)

Genome-wide Analysis of Motor Progression in Parkinson Disease

BACKGROUND AND OBJECTIVES: The genetic basis of Parkinson disease (PD) motor progression is largely unknown. Previous studies of the genetics of PD progression have included small cohorts and shown a limited overlap with genetic PD risk factors from case-control studies. Here, we have studied genomic variation associated with PD motor severity and early-stage progression in large longitudinal cohorts to help to define the biology of PD progression and potential new drug targets. METHODS: We performed a GWAS meta-analysis of early PD motor severity and progression up to 3 years from study entry. We used linear mixed-effect models with additive effects, corrected for age at diagnosis, sex, and the first 5 genetic principal components to assess variability in axial, limb, and total Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III scores. RESULTS: We included 3,572 unrelated European ancestry patients with PD from 5 observational cohorts and 1 drug trial. The average AAO was 62.6 years (SD = 9.83), and 63% of participants were male. We found an average increase in the total MDS-UPDRS III score of 2.3 points/year. We identified an association between PD axial motor progression and variation at the GJA5 locus at 1q12 (β = -0.25, SE = 0.04, p = 3.4e-10). Exploration of the regulation of gene expression in the region (cis-expression quantitative trait loci [eQTL] analysis) showed that the lead variant was associated with expression of ACP6, a lysophosphatidic acid phosphatase that regulates mitochondrial lipid biosynthesis (cis-eQTL p-values in blood and brain RNA expression data sets: <10-14 in eQTLGen and 10-7 in PsychEncode). DISCUSSION: Our study highlights the potential role of mitochondrial lipid homeostasis in the progression of PD, which may be important in establishing new drug targets that might modify disease progression

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

The genetic basis of levodopa-induced-dyskinesia (LiD) is poorly understood, and there have been few well-powered genome-wide studies. We performed a genome-wide survival meta-analyses to study the effect of genetic variation on the development of LiD in five separate longitudinal cohorts, and meta-analysed the results. We included 2784 PD patients, of whom 14.6% developed LiD. We found female sex (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10-5) increased the probability of developing LiD. We identified three genetic loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10-8) located at the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06, SE = 0.19, P = 2.81 × 10-9) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10-9) in the XYLT1 locus. Based on a functional annotation analysis on chromosome 1, we determined that changes in DNAJB4 gene expression, close to LRP8, are an additional potential cause of increased susceptibility to LiD. Baseline anxiety status was significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10-5). Finally, we performed a candidate variant analysis of previously reported loci, and found that genetic variability in ANKK1 (rs1800497, HR = 1.27, SE = 0.09, P = 8.89 × 10-3) and BDNF (rs6265, HR = 1.21, SE = 0.10, P = 4.95 × 10-2) loci were significantly associated with time to LiD in our large meta-analysis

What does heritability of Alzheimer's disease represent?

INTRODUCTION: Both late-onset Alzheimer's disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. METHODS: We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. RESULTS: SNP-based heritability of late onset Alzheimer's disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. CONCLUSION: The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes

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)