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

Jansen, Iris E.; Savage, Jeanne E.; Watanabe, Kyoko; Bryois, Julien; Williams, Dylan M; Steinberg, Stacy; Sealock, Julia; Karlsson, Ida; Hägg, Sara; Athanasiu, Lavinia; Proitsi, Petroula; Witoelar, Aree; Stringer, Sven; Aarsland, Dag; Almdahl, Ina Selseth; Andersen, Fred; Bergh, Sverre; Bettella, Francesco; Björnsson, Sigurbjörn; Brækhus, Anne; Bråthen, Geir; Leeuw, Christiaan de; Desikan, Rahul S.; Djurovic, Srdjan; Dumitrescu, Logan; Fladby, Tormod; Homan, Timothy; Jónsson, Pálmi V.; Kiddle, Steven; Rongve, K Arvid; Saltvedt, Ingvild; Sando, Sigrid Botne; Selbæk, Geir; Skenne, Nathan; Snædal, Jón; Stordal, Eystein; Ulstein, Ingun; Wang, Yunpeng; White, Linda R.; Hjerling-Leffler, Jens; Sullivan, Patrick F.; Flier, Wiesje M. van der; Dobson, Richard; Davis, Lea K.; Stefánsson, Hreinn; Stefánsson, Kāri; Pedersen, Nancy L.; Ripke, Stephan; Andreassen, Ole A.; Posthuma, Daniëlle

doi:10.1101/258533

Cited by 50 publications

(56 citation statements)

References 55 publications

(1 reference statement)

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“…Of the four new SNPs that strongly influenced Alzheimer’s risk, we found that MBLAC, DDB2 and MINK1 were associated with proxy AD status in the UKB sample. Importantly, five of the six IGAP/ADGC2 SNPs replicated in UKB consistent with prior work highlighting the usefulness of the by-proxy phenotype approach for AD [52]. Although a proxy phenotype is not equivalent to a clinical diagnosis of dementia, our findings illustrate that a subset of cardiovascular genes influences disease risk even in people with a genetic predisposition for developing AD.…”

Section: Discussionsupporting

confidence: 86%

“…[52]). Individuals with one or two parents with AD were defined as proxy cases, while putting more weight on the proxy cases with two parents.…”

Section: Methodsmentioning

confidence: 99%

“…Using large GWAS and validated tools to estimate pleiotropy, we sought to identify SNPs jointly associated with AD and one or more CV-associated RF, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). We additionally assessed whether the AD/CV genes showed independent replication within a large ‘AD-by-proxy’ phenotype sample that relies upon parental AD status to identify proxy cases and proxy controls [52]. Finally, we examined whether the AD/CV pleiotropic genes are differentially expressed within AD brains.…”

Section: Introductionmentioning

confidence: 99%

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Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

et al. 2018

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Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), 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 (TG, 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 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10−9), MINK1 (chromosome 17, meta-p = 1.98 × 10−7) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10−7 and closest gene = MYBPC3, meta-p = 5.62 × 10−8). In a large ‘AD-by-proxy’ cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

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Section: Discussionsupporting

confidence: 86%

“…[52]). Individuals with one or two parents with AD were defined as proxy cases, while putting more weight on the proxy cases with two parents.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

et al. 2018

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“…For real phenotypes, we calculated SNP minor allele frequency (MAF) and LD between SNPs using the 1000 Genomes phase 3 data set for 503 subjects/samples of European ancestry [28,29,30]. In order to carry out realistic simulations (i.e., with realistic heterozygosity and LD structures for SNPs), we used HAPGEN2 [31,32,33] [38]; (7) late onset Alzheimer's disease (LOAD; N cases = 17,008, N controls = 37,154) [39] (in the Supplementary Material we present results for a more recent GWAS with N cases = 71,880 and N controls = 383,378 [40]); (8) amyotrophic lateral sclerosis (ALS) (N cases = 12,577, N controls = 23,475) [41]; (9) number of years of formal education (N = 293,723) [42]; (10) intelligence (N = 262,529) [43,44]; (11) body mass index (N = 233,554) [45]; (12) height (N = 251,747) [46]; (13) putamen volume (normalized by intracranial volume, N = 11,598) [47]; (14) low-(N = 89,873) and (15) high-density lipoprotein (N = 94,295) [48]; and (16) total cholesterol (N = 94,579) [48]. Most participants were of European ancestry.…”

Section: Data Preparationmentioning

confidence: 99%

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Holland¹,

Frei

Desikan³

et al. 2017

Preprint

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Of signal interest in the genetics of traits are estimating the proportion, π 1 , of causally associated single nucleotide polymorphisms (SNPs), and their effect size variance, σ 2 β , which are components of the mean heritabilities captured by the causal SNP. Here we present the first model, using detailed linkage disequilibrium structure, to estimate these quantities from genome-wide association studies (GWAS) summary statistics, assuming a Gaussian distribution of SNP effect sizes, β. We apply the model to three diverse phenotypes -schizophrenia, putamen volume, and educational attainment -and validate it with extensive simulations. We find that schizophrenia is highly polygenic, with ≃ 5 × 10 4 causal SNPs distributed with small effect size variance, σ 2 β = 3.5 × 10 −5 (in units where the phenotype variance is normalized to 1), requiring a GWAS study with more than 1/2-million samples in each arm for full discovery. In contrast, putamen volume involves only ≃ 3 × 10 2 causal SNPs, but with σ 2 β = 1.2 × 10 −3 , indicating a much larger proportion of the causal SNPs that are strongly associated. Educational attainment has similar polygenicity to schizophrenia, but with effects that are substantially weaker, σ 2 β = 5 × 10 −6 , leading to much lower heritability. Thus the model is able to describe the broad genetic architecture of phenotypes where both polygenicity and effect size variance range over several orders of magnitude, shows why only small proportions of heritability have been explained for discovered SNPs, and provides a roadmap for future GWAS discoveries.

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“…For example, damage to and loss of glial cells, secondary to infection, trauma, or radiation, results in glial cell replacement via cell division, accelerating the basal rate of cell senescence. Many independent variables can affect (almost always increase) the rate of cell senescence including genes, chemotherapy, toxins, trauma, hypertension, stroke, hyperglycemia, microbiome, stress, hormones, infection, senolytic therapy, and so on (Figure ). These variables may be subdivided into subcategories, with supportive data, such as the host of possible infectious etiologies due to bacterial, chlamydial, fungal, viral, or prion‐related causes.…”

Section: Part 2: Summary Of the Modelmentioning

confidence: 99%

A unified model of dementias and age‐related neurodegeneration

Fossel

2020

Alzheimer's & Dementia

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Those working with Alzheimer's and other dementias have been frustrated by the implacability of these diseases. Regardless of limited symptomatic treatment, 1 there are no proven disease-modifying interventions. Despite huge and growing costs of care, 2 a pipeline of candidate drugs, 3 >400 registered trials, 4 tens of thousands of patients, 5 billions of dollars in both US federal 6 and pharmaceutical company investment, 7,8 more than a century of clinical expertise, and thousands of professional careers, dozens of pharmaceutical and biotechnology firms have foundered and failed 9 in attempts to prevent, slow, or alter the course of the dementias.This article presents a novel model to explain the relationships between age-related neurodegenerative disorders (eg, dementias) and the underlying molecular mechanisms of the aging process. The hypothesis is prompted by the fact that accepted conceptual models have failed to yield effective interventions for Alzheimer's or other dementias. 10 This article is a specific response to the Alzheimer's & Dementia editorial of November 2015, 11 which called for a systemic re-evaluation of our current models and their ability to answer fundamental questions regarding complex brain disorders and their relationship to clinical dementia, as well as the failure to yield effective clinical interventions.The article is divided into three parts. The first part explores current models of age-related neurogenerative diseases. The second part proposes a specific model and details both its working and its implications.The third part applies the model to answering the 10 key questions proposed by the Alzheimer's & Dementia editorial. The intent is to provide a conceptual model that accords with known data and proposes a novel point of clinical intervention. The model is intended to provoke discussion and provide a point of departure, rather than to offer a complete and final model for age-related neurodegenerative disease. The value of any such model rests on the outcome of clinical trials, but the model offers potentially innovative pathways to such trials. Current dementia modelsAlthough there is no general explanation for the failure to identify an effective intervention, there is growing consensus that a major factor is the lack of a comprehensive model for the dementias. 12 Over the past century, 13 several models have attained varying degrees of acceptance.Such models generally assume (or imply) a predominant single cause of Alzheimer's disease (AD; Figure 1).Such models suggest that, although several factors may contribute to the pathology, most Alzheimer's pathology results from a single predominant cause, such as amyloid plaque. In Figure 1, A (the predominant cause) is the leading causal factor, whereas B, C, D, and E represent contributing (but less significant or even incidental) factors.Until recently, the foremost among such explanations has been amyloid (A ) theory, [14][15][16][17] in various iterations. Other candidates have been tau tangles, 18-20 mitochondrial dysfun...

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

Cited by 50 publications

References 55 publications

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

A unified model of dementias and age‐related neurodegeneration

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