Pleiotropy of Alzheimer’s Disease and Educational Attainment: Insights from the Summary Statistics

Abstract Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer’s disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic predisposition to these phenotypes may shed light on EDU-related protection against AD. We examined pleiotropic predisposition to AD and EDU using Fisher’s method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5×10-8

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Lipid associated polygenic enrichment in Alzheimer’s disease

10.1101/383844 ◽

2018 ◽

Author(s):

Iris J. Broce ◽

Chin Hong Tan ◽

Chun Chieh Fan ◽

Aree Witoelar ◽

Natalie Wen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genetic Variants ◽

Plasma Lipids ◽

Association Studies ◽

Density Lipoprotein ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genetic Pleiotropy ◽

Common Genetic Variants

ABSTRACTCardiovascular (CV) and lifestyle associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ∊4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV associated genes also increase risk for AD (genetic pleiotropy). Using large genome-wide association studies (GWASs) (total n > 500,000 cases and controls) and validated tools to quantify genetic pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 57 SNPs on 19 different chromosomes that were jointly associated with AD and CV outcomes including APOA4, ABCA1, ABCG5, LIPG, and MTCH2/SPI1. We found that common genetic variants influencing AD are associated with multiple CV RFs, at times with a different directionality of effect. Expression of these AD/CV pleiotropic genes was enriched for lipid metabolism processes, over-represented within astrocytes and vascular structures, highly co-expressed, and differentially altered within AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid associated RFs. Rather than a single causal link between genetic loci, RF and the outcome, we found that common genetic variants influencing AD are associated with multiple CV RFs. Our collective findings suggest that a network of genes involved in lipid biology also influence Alzheimer’s risk.

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Identifying Genetic Interactions Associated with Late-Onset Alzheimer’s Disease

10.7287/peerj.preprints.123 ◽

2013 ◽

Author(s):

Charalampos S Floudas ◽

Nara Um ◽

M. Ilyas Kamboh ◽

Michael M Barmada ◽

Shyam Visweswaran

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Association Studies ◽

Univariate Analysis ◽

Genetic Interactions ◽

Supporting Evidence ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.

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Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models

Genetics ◽

10.1534/genetics.119.302940 ◽

2020 ◽

Vol 215 (1) ◽

pp. 41-58 ◽

Cited By ~ 4

Author(s):

Liang He ◽

Alexander M. Kulminski

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Statistical Power ◽

Large Scale ◽

Cox Model ◽

Association Studies ◽

Age At Onset ◽

Mixed Effects ◽

Mixed Effects Models ◽

Genome Wide Association Studies

Age-at-onset is one of the critical traits in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset traits can provide more insights into genetic effects on disease progression and transitions between stages. Moreover, proportional hazards (or Cox) regression models can achieve higher statistical power in a cohort study than a case-control trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for sample dependence, application of Cox mixed-effects models (CMEMs) to large-scale GWAS is so far hindered by intractable computational cost. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset traits using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including, but not limited to, block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG generalizes existing algorithms to support positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is orders of magnitude computationally more efficient than coxme and coxph with frailty for GWAS. We found that using sparse approximation of relatedness matrices yielded highly comparable results in controlling false-positive rate and retaining statistical power for an ethnically homogeneous family-based sample. By applying COXMEG to a study of Alzheimer’s disease (AD) with a Late-Onset Alzheimer’s Disease Family Study from the National Institute on Aging sample comprising 3456 non-Hispanic whites and 287 African Americans, we identified the APOE ε4 variant with strong statistical power (P = 1e−101), far more significant than that reported in a previous study using a transformed variable and a marginal Cox model. Furthermore, we identified novel SNP rs36051450 (P = 2e−9) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. These results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset traits.

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Herpesvirus Infections and Alzheimer’s Disease: A Mendelian Randomization Study

10.21203/rs.3.rs-323013/v1 ◽

2021 ◽

Author(s):

Shu-Yi Huang ◽

Yu-Xiang Yang ◽

Kevin Kuo ◽

Hong-Qi Li ◽

Xue-Ning Shen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Family History ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Primary Analysis ◽

Varicella Zoster ◽

History Of

Abstract BackgroundObservational studies have suggested that herpesvirus infection increased the risk of Alzheimer’s disease (AD), but it is unclear whether the association is causal. The aim of the present study is to evaluate the causal relationship between four herpesvirus infections and AD. MethodsWe performed a two-sample Mendelian randomization analysis to investigate association of four active herpesvirus infections with AD using summary statistics from genome-wide association studies. The four herpesvirus infections (i.e., chickenpox, shingles, cold sores, mononucleosis) are caused by varicella-zoster virus, herpes simplex virus type 1, and Epstein-Barr virus (EBV), respectively. A large summary statistics data from International Genomics of Alzheimer’s Project was used in primary analysis, including 21,982 AD cases and 41,944 controls. Validation was further performed using family history of AD data from UK Biobank (27,696 cases of maternal AD, 14,338 cases of paternal AD and 272,244 controls).ResultsWe found evidence of a suggestive association between mononucleosis (caused by EBV) and risk of AD (odds ratio [OR] = 1.634, 95% confidence interval [CI] = 1.092-2.446, P = 0.017) after Bonferroni correction. It has been verified in validation analysis that mononucleosis is also associated with family history of AD (OR [95% CI] = 1.392 [1.061, 1.826], P=0.017). Genetically predicted shingles were associated with AD risk (OR [95% CI] = 0.867 [0.784, 0.958], P = 0.005). While genetically predicted chickenpox was suggestively associated with increased family history of AD (OR [95% CI] = 1.147 [1.007, 1.307], P = 0.039).ConclusionsOur findings provided evidence supporting a positive relationship between mononucleosis and AD, indicating a causal link between EBV infection and AD. Further elucidations of this association and underlying mechanisms are likely to identify feasible interventions to promote AD prevention.

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Functional biological paths altered in Alzheimer’s disease: from genes to bile acids

10.1101/2020.01.31.929554 ◽

2020 ◽

Author(s):

Priyanka Gorijala ◽

Kwangsik Nho ◽

Shannon L. Risacher ◽

Rima Kaddurah-Daouk ◽

Andrew J. Saykin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Bile Acids ◽

Biological Networks ◽

Large Scale ◽

Association Studies ◽

Underlying Mechanism ◽

Genome Wide Association Studies ◽

Functional Protein ◽

Age Related

AbstractLarge-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimer’s disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

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A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BMC Medicine ◽

10.1186/s12916-021-02186-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Haojie Lu ◽

Jiahao Qiao ◽

Zhonghe Shao ◽

Ting Wang ◽

Shuiping Huang ◽

...

Keyword(s):

Psychiatric Disorders ◽

Genetic Correlation ◽

Large Scale ◽

Association Studies ◽

Single Gene ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Nucleotide Polymorphisms ◽

Entire Genome ◽

Gene Level

Abstract Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

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Physical activity and risk of Alzheimer’s disease: a two-sample Mendelian randomization study

10.1101/819821 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sebastian E Baumeister ◽

André Karch ◽

Martin Bahls ◽

Alexander Teumer ◽

Michael F Leitzmann ◽

...

Keyword(s):

Physical Activity ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Observational Studies ◽

Protective Factor ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Inverse Variance

ABSTRACTIntroductionEvidence from observational studies for the effect of physical activity on the risk of Alzheimer’s disease (AD) is inconclusive. We performed Mendelian randomization analysis to examine whether physical activity is a protective factor for AD.MethodsSummary data of genome-wide association studies on physical activity and AD were identified using PubMed and the GWAS catalog. The study population included 21,982 AD cases and 41,944 cognitively normal controls. Eight single nucleotide polymorphisms (SNP) known at P < 5×10−8 to be associated with accelerometer-assessed physical activity served as instrumental variables.ResultsGenetically predicted accelerometer-assessed physical activity had no effect on the risk of AD (inverse variance weighted odds ratio [OR] per standard deviation (SD) increment: 1.03, 95% confidence interval: 0.97-1.10, P=0.332).DiscussionThe present study does not support a relationship between physical activity and risk of AD, and suggests that previous observational studies might have been biased.

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Alzheimer’s disease risk SNPs show no strong effect on miRNA expression in human lymphoblastoid cell lines

10.1101/367318 ◽

2018 ◽

Author(s):

Inken Wohlers ◽

Colin Schulz ◽

Fabian Kilpert ◽

Lars Bertram

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cell Lines ◽

Mirna Expression ◽

Large Scale ◽

Disease Risk ◽

Association Studies ◽

Lymphoblastoid Cell Lines ◽

Eqtl Analysis ◽

Genome Wide Association Studies

AbstractThe role of microRNAs (miRNAs) in the pathogenesis of Alzheimer’s disease (AD) is currently extensively investigated. In this study, we assessed the potential impact of AD genetic risk variants on miRNA expression by performing large-scale bioinformatic data integration. Our analysis was based on genetic variants from three AD genome-wide association studies (GWAS). Association with miRNA expression was tested by expression quantitative trait loci (eQTL) analysis using next-generation miRNA sequencing data generated in lymphoblastoid cell lines (LCL). While, overall, we did not identify a strong effect of AD GWAS variants on miRNA expression in this cell type we highlight two notable outliers, i.e. miR-29c-5p and miR-6840-5p. MiR-29c-5p was recently reported to be involved in the regulation of BACE1 and SORL1 expression. In conclusion, despite two exceptions our large-scale assessment provides only limited support for the hypothesis that AD GWAS variants act as miRNA eQTLs.

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Genetics of rheumatoid arthritis

10.1093/med/9780199642489.003.0040 ◽

2013 ◽

Author(s):

Steve Eyre ◽

Jane Worthington

Keyword(s):

Rheumatoid Arthritis ◽

Large Scale ◽

Association Studies ◽

Epidemiological Studies ◽

Convincing Evidence ◽

Susceptibility Locus ◽

Small Scale ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Hla Association

A range of epidemiological studies have clearly established that susceptibility to rheumatoid arthritis (RA) is determined by both genetic and environmental factors. Studies over the last five decades have used a variety of approaches to identify the genetic variants associated with disease. HLA DRB1 was the first RA susceptibility locus to be discovered and has the largest effect size. We describe current understanding of the complexities of HLA association for RA. Linkage and small-scale association studies prior to 2007 provided convincing evidence for only one more RA susceptibility locus, PTPN22. Major breakthroughs in high-throughput genotyping and systematic discovery and mapping of hundreds of thousands of single nucleotide polymorphisms (SNPs) led to large-scale genome-wide association studies used for the first time for RA in 2007. This approach has had a dramatic impact on our knowledge of the susceptibility loci for RA, such that over 60 risk variants have now been robustly identified. We present an overview of these studies and the loci that have been identified. We consider how this knowledge is contributing to a greater understanding of the aetiology and pathology of the disease and in turn how this can influence management of patients presenting with an inflammatory arthritis. We consider some of the unanswered questions and the approaches that will need to be taken to address them.

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