Lyndsay A. Staley

Genome-Wide Association Study of CSF Levels of 59 Alzheimer's Disease Candidate Proteins: Significant Associations with Proteins Involved in Amyloid Processing and Inflammation

Cerebrospinal fluid (CSF) 42 amino acid species of amyloid beta (Aβ42) and tau levels are strongly correlated with the presence of Alzheimers disease (AD) neuropathology including amyloid plaques and neurodegeneration and have been successfully used as endophenotypes for genetic studies of AD. Additional CSF analytes may also serve as useful endophenotypes that capture other aspects of AD pathophysiology. Here we have conducted a genome-wide association study of CSF levels of 59 AD-related analytes. All analytes were measured using the Rules Based Medicine Human DiscoveryMAP Panel, which includes analytes relevant to several disease-related processes. Data from two independently collected and measured datasets, the Knight Alzheimers Disease Research Center (ADRC) and Alzheimers Disease Neuroimaging Initiative (ADNI), were analyzed separately, and combined results were obtained using meta-analysis. We identified genetic associations with CSF levels of 5 proteins (Angiotensin-converting enzyme (ACE), Chemokine (C-C motif) ligand 2 (CCL2), Chemokine (C-C motif) ligand 4 (CCL4), Interleukin 6 receptor (IL6R) and Matrix metalloproteinase-3 (MMP3)) with study-wide significant p-values (p<1.46×10−10) and significant, consistent evidence for association in both the Knight ADRC and the ADNI samples. These proteins are involved in amyloid processing and pro-inflammatory signaling. SNPs associated with ACE, IL6R and MMP3 protein levels are located within the coding regions of the corresponding structural gene. The SNPs associated with CSF levels of CCL4 and CCL2 are located in known chemokine binding proteins. The genetic associations reported here are novel and suggest mechanisms for genetic control of CSF and plasma levels of these disease-related proteins. Significant SNPs in ACE and MMP3 also showed association with AD risk. Our findings suggest that these proteins/pathways may be valuable therapeutic targets for AD. Robust associations in cognitively normal individuals suggest that these SNPs also influence regulation of these proteins more generally and may therefore be relevant to other diseases.

Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk

Ebbert et al. reported gene‐gene interactions between rs11136000‐rs670139 (CLU‐MS4A4E) and rs3865444‐rs670139 (CD33‐MS4A4E). We evaluate these interactions in the largest data set for an epistasis study.

CELL-FREE, SINGLE-STRANDED DNA CONCENTRATION IN CSF AS BIOMARKER TO DIAGNOSE ALZHEIMER’S DISEASE STATUS

Background:Early detection of molecular changes in Alzheimer’s disease is likely to play a key role in the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, decreases in cerebral spinal fluid (CSF) amyloid (A) levels may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Topography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status. Methods:We make use of 57 cognitively normal (CN) and 186 mild cognitively impaired (MCI) individuals from the ADNI dataset, who have measures of CSF A, 149 protein (P) and 140 metabolites (M) levels measured in blood and age and APOE4 status (B) at baseline. A random forest approach in 10 repetitions of 10-fold cross validation is used to get an unbiased estimate of the model performance. An independent 210 MCI individuals without CSF measures are used to validate our model’s performance by examining the difference in rates of conversion to AD between the predicted abnormal/normal CSF A strata. Results: We show that a Random Forest model derived from age, APOE and proteins levels (BP) can accurately predict pre-clinical subjects as having abnormal (low) CSFA levels indicative of AD risk (Fig 1: 0.80 AUC, 0.69 sensitivity, and 0.75 specificity). Only 3 analytes are required to achieve similar high levels of accuracy (BP5, 0.78 AUC). Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF A levels (Fig 2: P< 2.5’10). Conclusions: This is the first study to show that a plasma protein signature, together with age and APOE4 genotype, can predict CSFA status, the earliest risk indicator for AD, with high accuracy, further highlighting the potential for developing a blood-based signature for improved AD screening.

SYNERGY BETWEEN RS1049296 IN TRANSFERRIN AND RS1800562 IN HEMOCHROMATOSIS AS A RISK FACTOR FOR ALZHEIMER’S DISEASE

(ADNI). Now that the ADNI-1/GO/2 phases are complete, we performed a comprehensive updated analysis of major Alzheimer’s disease (AD) phenotypes to confirm previous findings and to identify novel associations. Methods: A total of 1566 non-Hispanic Caucasian participants with GWAS and selected phenotype data from the ADNI cohort were included. Genotype data were imputed using the Haplotype Reference Consortium (HRC) reference panel after standard quality control procedures. Baseline measures of 19 phenotypes were analyzed including MRI atrophy measures (8 ROIs), FDG PET (3 ROIs), [18F]Florbetapir amyloid PET (1 meta-ROI), CSF amyloid-b 1-42 peptide (Ab1-42), total tau (ttau), and phosphorylated tau (p-tau), as well as composite scores for memory (MEM), executive function (EF), and selfand informantconcerns regarding everyday cognition (E-Cog). Table 1 presents the full list of phenotypes and sample sizes. All phenotypes were adjusted for age and sex. MRI and cognitive measures were adjusted for education; MRI data was adjusted for intracranial volume (ICV) and magnetic field strength. Univariate GWAS analysis was performed in PLINK. The genome-wide significance threshold was set at P 6.25x10-9 based on Bonferroni correction with the top 8 principal components of the phenotypes, which explained 85% of the total variance. Results:85 genome wide significant SNPs were identified across 15 of the 19 analyzed phenotypes. In 13 of those

The Opioid Abuse Risk Screener predicts aberrant same-day urine drug tests and 1-year controlled substance database checks: A brief report

The Opioid Abuse Risk Screener was developed to support well-informed decision-making in opioid analgesic prescribing by extending the breadth of psychiatric risk factors evaluated relative to other non–clinician-administered measures. We examined the preliminary predictive validity of the Opioid Abuse Risk Screener relative to the widely used Screener and Opioid Assessment for Patients with Pain–Revised in predicting aberrant urine drug tests and controlled substance database checks. The Opioid Abuse Risk Screener is significantly different from the Screener and Opioid Assessment for Patients with Pain–Revised in predicting aberrant same-day urine drug tests (Z = 2.912, p = 0.0036) and controlled substance database checks within 1 year of assessment (Z = 3.731, p = 0.0002). Promising preliminary analyses using machine learning methods are also discussed.

Genome-wide association study of prolactin levels in blood plasma and cerebrospinal fluid

BackgroundProlactin is a polypeptide hormone secreted by the anterior pituitary gland that plays an essential role in lactation, tissue growth, and suppressing apoptosis to increase cell survival. Prolactin serves as a key player in many life-critical processes, including immune system and reproduction. Prolactin is also found in multiple fluids throughout the body, including plasma and cerebrospinal fluid (CSF).MethodsIn this study, we measured prolactin levels in both plasma and CSF, and performed a genome-wide association study. We then performed meta-analyses using METAL with a significance threshold of p < 5 × 10−8 and removed SNPs where the direction of the effect was different between the two datasets.ResultsWe identified 12 SNPs associated with increased prolactin levels in both biological fluids.ConclusionsOur efforts will help researchers understand how prolactin is regulated in both CSF and plasma, which could be beneficial in research for the immune system and reproduction.

Variants in CCL16 are associated with blood plasma and cerebrospinal fluid CCL16 protein levels

BackgroundCCL16 is a chemokine predominantly expressed in the liver, but is also found in the blood and brain, and is known to play important roles in immune response and angiogenesis. Little is known about the gene’s regulation.MethodsHere, we test for potential causal SNPs that affect CCL16 protein levels in both blood plasma and cerebrospinal fluid in a genome-wide association study across two datasets. We then use METAL to performed meta-analyses with a significance threshold of p < 5x10−8. We removed SNPs where the direction of the effect was different between the two datasets.ResultsWe identify 10 SNPs associated with increased CCL16 protein levels in both biological fluids.ConclusionsOur results will help understand CCL16’s regulation, allowing researchers to better understand the gene’s effects on human health.