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

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.

scholarly journals Genetic Overlap Between Alzheimer’s Disease and Depression Mapped Onto the Brain

2021 ◽  
Vol 15 ◽  
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 ◽  
Third Ventricle ◽  
Brain Mri ◽  
Genetic Correlations ◽  
Gaussian Mixture ◽  
Brain Morphology ◽  
Genetic Overlap ◽  
Ventricle Volume

Background: Alzheimer’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, characterising their genetic overlap may provide aetiological 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.Methods: We 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 (UKB) (mean age 57.21, 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.Results: MiXer estimated 98 causal genetic variants overlapping between the 2 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 UKB. This SNP was also associated with several regions of the corpus callosum volume anterior (B &gt; 0.024, p &lt; 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).Discussion: Our 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.

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

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.

scholarly journals Association between polygenic risk for Alzheimer’s disease, brain structure and cognitive abilities in UK Biobank.

2021 ◽  
Author(s):  
Rachana Tank ◽  
Joey Ward ◽  
Kristin E. Flegal ◽  
Daniel Smith ◽  
Mark E.S. Bailey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fluid Intelligence ◽  
Late Onset ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Healthy Adults ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Structural Brain

Background and purpose: Previous studies testing associations between polygenic risk for late-onset Alzheimer’s disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Method: Summary statistics were used to create PGR scores for n=32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. Results: In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = -0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = -0.102, p = 0.003), smaller left hippocampal total (β = -0.118, p = 0.002) and body (β = -0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. Discussion: This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults, and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hei Man Wu ◽  
Alison M. Goate ◽  
Paul F. O’Reilly
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Risk ◽  
Familial Risk ◽  
Psychosocial Health ◽  
Uk Biobank ◽  
Multiple Forms ◽  
Heterogeneous Effects ◽  
The Difference ◽  
The Uk

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.

scholarly journals Genetic meta-analysis identifies 9 novel loci and functional pathways for Alzheimer’s disease risk

2018 ◽  
Author(s):  
Iris E Jansen ◽  
Jeanne E Savage ◽  
Kyoko Watanabe ◽  
Julien Bryois ◽  
Dylan M Williams ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Late Onset ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Cell Types ◽  
Mendelian Randomisation ◽  
Genome Wide

AbstractLate onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

scholarly journals Association between polygenic risk for Alzheimer’s disease, brain structure and cognitive abilities in UK Biobank

2021 ◽  
Author(s):  
Rachana Tank ◽  
Joey Ward ◽  
Kristin E. Flegal ◽  
Daniel J. Smith ◽  
Mark E. S. Bailey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fluid Intelligence ◽  
Late Onset ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Healthy Adults ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Structural Brain

AbstractPrevious studies testing associations between polygenic risk for late-onset Alzheimer’s disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Summary statistics were used to create PGR scores for n = 32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 genetic principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = −0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = −0.102, p = 0.003), smaller left hippocampal total (β = −0.118, p = 0.002) and body (β = −0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

scholarly journals A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773)

2017 ◽  
Author(s):  
Weihua Meng ◽  
Mark J Adams ◽  
Harry L Hebert ◽  
Ian J Deary ◽  
Andrew M McIntosh ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Psychological Traits ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

AbstractHeadache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. We identified 3,343 SNPs which reached the genome-wide significance level of P < 5 × 10−8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 × 10−47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 × 10−15 in the LINC02210-CRHR1 gene was the top SNP.Positive relationships (P < 0.001) between multiple brain tissues and genetic associations were identified through tissue expression analysis, whereas no vascular related tissues showed significant relationships. We identified several significant positive genetic correlations between headache and other psychological traits including neuroticism, depressive symptoms, insomnia, and major depressive disorder.Our results suggest that brain function is closely related to broadly-defined headache. In addition, we also found that many psychological traits have genetic correlations with headache.

scholarly journals Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

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.

scholarly journals Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

2017 ◽  
Author(s):  
Rona J. Strawbridge ◽  
Joey Ward ◽  
Breda Cullen ◽  
Elizabeth M. Tunbridge ◽  
Sarah Hartz ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Risk Taking ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Uk Biobank ◽  
Physical And Mental Health ◽  
Post Traumatic Stress ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

AbstractRisk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

scholarly journals Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N = 112 151) and 24 GWAS consortia.

2015 ◽  
Author(s):  
Saskia P Hagenaars ◽  
Sarah E Harris ◽  
Gail Davies ◽  
William David Hill ◽  
David CM Liewald ◽  
...  
Keyword(s):  
Physical Health ◽  
Test Scores ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Cognitive Test ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk ◽  
Mental And Physical Health ◽  
Shared Genetic

The causes of the known associations between poorer cognitive function and many adverse neuropsychiatric outcomes, poorer physical health, and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric, and cognitive traits in the participants of UK Biobank, a very large population-based sample (N = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics applied to the method of linkage disequilibrium regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, BMI, intracranial volume, infant head circumference, and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.

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