Heterogeneous effects of genetic risk for Alzheimer’s disease on the phenome

AbstractHere we report how four major forms of Alzheimer’s disease (AD) genetic risk—APOE-ε4, APOE-ε2, polygenic risk and familial risk—are associated with 273 traits in ~500,000 individuals in the UK Biobank. The traits cover blood biochemistry and cell traits, metabolic and general health, psychosocial health, and cognitive function. The difference in the profile of traits associated with the different forms of AD risk is striking and may contribute to heterogenous presentation of the disease. However, we also identify traits significantly associated with multiple forms of AD genetic risk, as well as traits showing significant changes across ages in those at high risk of AD, which may point to their potential roles in AD etiology. Finally, we highlight how survivor effects, in particular those relating to shared risks of cardiovascular disease and AD, can generate associations that may mislead interpretation in epidemiological AD studies. The UK Biobank provides a unique opportunity to powerfully compare the effects of different forms of AD genetic risk on the phenome in the same cohort.

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Breast Cancer, Alzheimer’s Disease, and APOE4 Allele in the UK Biobank Cohort

Journal of Alzheimer s Disease Reports ◽

10.3233/adr-200266 ◽

2021 ◽

Vol 5 (1) ◽

pp. 49-53

Author(s):

Steven Lehrer ◽

Peter H. Rheinstein

Keyword(s):

Breast Cancer ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Apoe Genotype ◽

Uk Biobank ◽

Breast Cancer Patients ◽

Apoe4 Allele ◽

Significant Difference ◽

The Uk ◽

Cognitive Problems

Background: Cognitive problems are common in breast cancer patients. The apolipoprotein E4 (APOE4) gene, a risk factor for Alzheimer’s disease (AD), may be associated with cancer-related cognitive decline. Objective: To further evaluate the effects of the APOE4 allele, we studied a cohort of patients from the UK Biobank (UKB) who had breast cancer; some also had AD. Methods: Our analysis included all subjects with invasive breast cancer. Single nucleotide polymorphism (SNP) data for rs 429358 and rs 7412 was used to determine APOE genotypes. Cognitive function as numeric memory was assessed with an online test (UKB data field 20240). Results: We analyzed data from 2,876 women with breast cancer. Of the breast cancer subjects, 585 (20%) carried the APOE4 allele. Numeric memory scores were significantly lower in APOE4 carriers and APOE4 homozygotes than non-carriers (p = 0.046). 34 breast cancer subjects (1.1%) had AD. There was no significant difference in survival among genotypes ɛ3/ɛ3, ɛ3/ɛ4, and ɛ4/ɛ4. Conclusion: UKB data suggest that cognitive problems in women with breast cancer are, for the most part, mild, compared with other sequelae of the disease. AD, the worst cognitive problem, is relatively rare (1.1%) and, when it occurs, APOE genotype has little impact on survival.

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Genetic overlap between Alzheimer’s disease and depression mapped onto the brain

10.1101/2021.01.23.21250016 ◽

2021 ◽

Author(s):

Jennifer Monereo Sánchez ◽

Miranda T. Schram ◽

Oleksandr Frei ◽

Kevin O’Connell ◽

Alexey A. Shadrin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Brain Mri ◽

Genetic Correlations ◽

Gaussian Mixture ◽

Brain Morphology ◽

Uk Biobank ◽

Genetic Overlap ◽

The Uk

ABSTRACTBackgroundAlzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.MethodsWe applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.ResultsMiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1×10−4) and depression (B=0.007, p=3.2×10−9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6×10−4), third ventricle volume ventricle (B=-0.025, p=5.0×10−6), and inferior temporal gyrus surface area (B=0.017, p=5.3×10−4).DiscussionOur results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

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Alzheimer’s disease susceptibility gene apolipoprotein e (APOE) and blood biomarkers in UK Biobank (N=395,769)

10.1101/2020.02.12.20021998 ◽

2020 ◽

Author(s):

Amy C. Ferguson ◽

Rachana Tank ◽

Laura M. Lyall ◽

Joey Ward ◽

Carlos Celis-Morales ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Risk Factor ◽

Apolipoprotein E ◽

Genetic Risk ◽

Susceptibility Gene ◽

Density Lipoprotein ◽

European Ancestry ◽

Uk Biobank ◽

Blood Biomarkers

AbstractBackground and objectiveAlzheimer’s disease (AD) is a neurodegenerative condition where the underlying aetiology is still unclear. Investigating the potential influence of apolipoprotein e (APOE), a major genetic risk factor, on common blood biomarkers could provide a greater understanding of the mechanisms of AD and dementia risk. Our objective was to conduct the largest (to date) single-protocol investigation of blood biomarkers in the context of APOE genotype, in UK Biobank.MethodsAfter quality control and exclusions, data on 395,769 participants of White European ancestry were available for analysis. Linear regressions were used to test potential associations between APOE genotypes and biomarkers.ResultsSeveral biomarkers significantly associated with APOE e4 ‘risk’ and e2 ‘protective’ genotypes (vs. neutral e3/e3). Most associations supported previous data: for example, e4 genotype was associated with elevated low-density lipoprotein cholesterol (LDL) (standardized beta [b] = 0.150 standard deviations [SDs] per allele, p<0.001) and e2 with lower LDL (b = −0.456 SDs, p<0.001). There were however instances of associations found in unexpected directions: e.g. e4 and increased insulin-like growth factor (IGF-1) (standardized beta = 0.017, p<0.001) where lower levels have been previously suggested as an AD risk factor.ConclusionsThese findings highlight biomarker differences in non-demented people at genetic risk for dementia. The evidence here in supports previous hypotheses of involvement from cardiometabolic and neuroinflammatory pathways.

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Using a genetic risk score to estimate the earliest age of Alzheimer’s disease-related physiologic change in Body Mass Index

10.1101/19013441 ◽

2019 ◽

Author(s):

Willa D. Brenowitz ◽

Scott C. Zimmerman ◽

Teresa J. Filshtein ◽

Kristine Yaffe ◽

Stefan Walter ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Body Mass Index ◽

Weight Loss ◽

Body Mass ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Genetic Ancestry ◽

Uk Biobank

AbstractObjectivesWeight loss is common in the years before an Alzheimer’s disease (AD) diagnosis, likely due to changes in appetite and diet. The age at which this change in body mass index (BMI) emerges is unclear but may point to the earliest manifestations of AD, timing that may be important for identifying windows of intervention or risk reduction. We examined the association between AD genetic risk and cross-sectional BMI across adults in mid-to late-life as an innovative approach to determine the age at which BMI changes and may indicate preclinical AD.DesignObservational studySettingUK BiobankParticipants407,386 UK Biobank non-demented participants aged 39-70 with Caucasian genetic ancestry enrolled 2007-2010.Main Outcome MeasuresBMI (kg/m2) was constructed from height and weight measured during the initial visit. A genetic risk score for AD (AD-GRS) was calculated as a weighted sum of 23 genetic variants previously confirmed to be genome-wide significant predictors of AD (Z-scored). We evaluated whether the association of AD-GRS with BMI differed by age using linear regression with adjustment for sex and genetic ancestry, stratified by age grouping (40-60, 61+). We calculated the earliest age at which high AD-GRS predicted divergence in BMI compared to normal age-related BMI trends with linear and quadratic terms for age and interactions with AD-GRS.ResultsIn 39-49 year olds, AD-GRS was not significantly associated with lower BMI (0.00 kg/m2 per SD in AD-GRS; 95%CI: -0.03,0.03). In 50-59 year olds AD-GRS was associated with lower BMI (-0.03 kg/m2 per 1 SD in AD-GRS; 95%CI:-0.06,-0.01) and this association was stronger in 60-70 year olds (-0.09 kg/m2 per 1 SD in AD-GRS; 95%CI:-0.12,-0.07). Model-based BMI age-curves for people with high versus low AD-GRS scores began to diverge after age 47.InterpretationGenetic factors that increase AD risk begin to predict lower BMI in adults by age 50, with greater effect later in older ages. Weight loss may manifest as an early pathophysiologic change associated with AD.

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Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

10.1101/2022.01.05.22268795 ◽

2022 ◽

Author(s):

Tiago Azevedo ◽

Richard A.I. Bethlehem ◽

David J. Whiteside ◽

Nol Swaddiwudhipong ◽

James B. Rowe ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neurodegenerative Disease ◽

Fluid Intelligence ◽

Structural Mri ◽

Critical Issue ◽

Healthy Population ◽

Uk Biobank ◽

Sporadic Disease ◽

The Uk

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease modifying trials. Evidence from genetic studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80), and demonstrate correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of numeric memory, reaction time, working memory and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

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Characterizing brain age in the Alzheimer's disease connectome project using a deep neural network pre‐trained on the UK Biobank

Alzheimer s & Dementia ◽

10.1002/alz.057535 ◽

2021 ◽

Vol 17 (S5) ◽

Author(s):

Nagesh Adluru ◽

Veena A. Nair ◽

Vivek Prabhakaran ◽

Vishnu Bashyam ◽

Shi‐Jiang Li ◽

...

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Neural Network ◽

Uk Biobank ◽

The Uk ◽

Brain Age

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Phenome‐wide associations with an Alzheimer’s disease genetic risk score in UK Biobank: Identifying early indicators of disease

Alzheimer s & Dementia ◽

10.1002/alz.040114 ◽

2020 ◽

Vol 16 (S10) ◽

Author(s):

Scott C Zimmerman ◽

Sarah F Ackley ◽

Willa D Brenowitz ◽

Rebecca E Graff ◽

M Maria Glymour

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Uk Biobank ◽

Early Indicators

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Exploration of Alzheimer's Disease MRI Biomarkers Using APOE4 Carrier Status in the UK Biobank

10.1101/2021.09.09.21263324 ◽

2021 ◽

Author(s):

Jingnan Du ◽

Zhaowen Liu ◽

Lindsay C Hanford ◽

Kevin M Anderson ◽

Jianfeng Feng ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Large Scale ◽

Vascular Dysfunction ◽

Hippocampal Volume ◽

Carrier Status ◽

Amyloid Angiopathy ◽

Uk Biobank ◽

The Uk

Large-scale datasets enable novel strategies to refine and discover relations among biomarkers of disease. Here 30,863 individuals ages 44-82 from the UK Biobank were analyzed to explore MRI biomarkers associated with Alzheimer's disease (AD) genetic risk as contrast to general effects of aging. Individuals homozygotic for the E4 variant of apolipoprotein E (APOE4) overlapped non-carriers in their 50s but demonstrated neurodegenerative effects on the hippocampal system beginning in the seventh decade (reduced hippocampal volume, entorhinal thickness, and hippocampal cingulum integrity). Phenome-wide exploration further nominated the posterior thalamic radiation (PTR) as having a strong effect, as well as multiple diffusion MRI (dMRI) and white matter measures consistent with vascular dysfunction. Effects on the hippocampal system and white matter could be dissociated in the homozygotic APOE4 carriers supporting separation between AD and cerebral amyloid angiopathy (CAA) patterns. These results suggest new ways to combine and interrogate measures of neurodegeneration.

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