MS4A Cluster in Alzheimer’s Disease

Ma, Jing; Yu, Jin‐Tai; Tan, Lan

doi:10.1007/s12035-014-8800-z

Cited by 47 publications

(42 citation statements)

References 60 publications

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“…Variants within membrane-spanning 4-domains subfamily A (MS4A) gene cluster are some other recently discovered AD risk factors [34]. Our analysis also demonstrated their associations with imaging QTs: (1) MS4A4E-rs670139 predicts cerebral white matter volume at BL, M06 and M12; and (2) MS4A6A-rs667897 predicts GM densities of posterior cingulate and pallidum at almost all time points.…”

Section: Resultssupporting

confidence: 59%

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

Wang

Yan

Yao

et al. 2017

Lecture Notes in Computer Science

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With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

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Section: Resultssupporting

confidence: 59%

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

Wang

Yan

Yao

et al. 2017

Lecture Notes in Computer Science

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“…Second, and more importantly, the top SNP for the CSF TREM2 (rs4939338, P = 5.45×10 -07 ), is also one of the top SNPs for the GWAS for AD risk (P-value for AD risk= 1.01×10 -14 ), indicating that the same signal is detected by the two phenotypes (sTREM2 levels in CSF and AD risk). The MS4A gene family encodes cell membrane proteins, the functions of which are still poorly understood but with a possible role in immune cells [32]. At present, it is unknown whether and how MSA4 genes interact with TREM2.…”

Section: Discussionmentioning

confidence: 99%

Cerebrospinal fluid soluble TREM2 is higher in Alzheimer disease and associated with mutation status

et al. 2016

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Low frequency coding variants in TREM2 are associated with increased Alzheimer disease (AD) risk, while loss of functions mutations in the gene lead to an autosomal recessive early-onset dementia, named Nasu-Hakola disease (NHD). TREM2 can be detected as a soluble protein in cerebrospinal fluid (CSF) and plasma, and its CSF levels are elevated in inflammatory CNS diseases. We measured solubleTREM2 (sTREM2) in the CSF of a large AD case-control dataset (n=180) and 40 TREM2 risk variant carriers to determine whether CSF sTREM2 levels are associated with AD status or mutation status. We also performed genetic studies to identify genetic variants associated with CSF sTREM2 levels. CSF, but not plasma, sTREM2 was highly correlated with CSF total tau and phosphorylated-tau levels (r=0.35, p<1×10-4; r=0.40, p<1×10-4 respectively), but not with CSF Aβ42. AD cases presented higher CSF sTREM2 levels than controls (P=0.01). Carriers of NHD-associated TREM2 variants presented significantly lower CSF sTREM2 levels, supporting the hypothesis that these mutations lead to reduced protein production/function (R136Q, D87N, Q33X or T66M; p=1×10-3). In contrast, CSF sTREM2 levels were significantly higher in R47H carriers compared to non-carriers (P=6×10-3), suggesting that this variant does not impact protein expression and increases AD risk through a different pathogenic mechanism than NHD variants. In GWAS analyses for CSF sTREM2 levels the most significant signal was located on the MS4A gene locus (P=5.45×10-07) corresponding to one of the SNPs reported to be associated with AD risk in this locus. Furthermore, SNPs involved in pathways related to virus cellular entry and vesicular trafficking were overrepresented, suggesting that CSF sTREM2 levels could be an informative phenotype for AD.

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“…However, the function of the Ms4a family members in cell-and tissue-specific contexts is largely unresolved. Interestingly, Ms4a family members are associated with Alzheimer's risk (Hollingworth et al, 2011;Ma et al, 2015;Naj et al, 2011), but their role in the disease is not understood. Cluster 6 microglia were also enriched for chemokine (C-C motif) receptor 1 (Ccr1), which regulates immune cell migration and functional states (Chou et al, 2010;Furuichi et al, 2008;Mahad et al, 2004).…”

Section: A Novel Population Of Embryonic Ms4a-expressing Microglia Shmentioning

confidence: 99%

“…Lineage tracing will be essential to understanding their origin and whether they give rise to microglia that persist in the brain. Also, whether Cluster 6 microglia also have specialized functions in brain development remains to be seen, but their enrichment for several members of the Ms4a gene family, which are risk genes for Alzheimer's disease (Ma et al, 2015), could confer unique functional properties and necessitates further exploration.…”

Section: Identification Of Distinct Subpopulations Of Microglia That mentioning

confidence: 99%

Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain

Hammond

Dufort

Dissing-Olesen

et al. 2018

Preprint

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Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. In this study, we analyzed the RNA expression patterns of more than 76,000 individual microglia during development, old age and after brain injury. Analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states - including chemokine-enriched inflammatory microglia - persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human MS lesions. These unique microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

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MS4A Cluster in Alzheimer’s Disease

Cited by 47 publications

References 60 publications

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

Cerebrospinal fluid soluble TREM2 is higher in Alzheimer disease and associated with mutation status

Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain

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