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CD33 modulates TREM2: convergence of Alzheimer loci.

Nature neuroscience | 2015

We used a protein quantitative trait analysis in monocytes from 226 individuals to evaluate cross-talk between Alzheimer loci. The NME8 locus influenced PTK2B and the CD33 risk allele led to greater TREM2 expression. There was also a decreased TREM1/TREM2 ratio with a TREM1 risk allele, decreased TREM2 expression with CD33 suppression and elevated cortical TREM2 mRNA expression with amyloid pathology.

Pubmed ID: 26414614 RIS Download

Research resources used in this publication

None found

Antibodies used in this publication

None found

Associated grants

  • Agency: NIA NIH HHS, United States
    Id: U01 AG046152
  • Agency: NIA NIH HHS, United States
    Id: R01 AG17917
  • Agency: NIA NIH HHS, United States
    Id: RF1 AG015819
  • Agency: NIA NIH HHS, United States
    Id: K25 AG041906
  • Agency: NIA NIH HHS, United States
    Id: P01 AG036694
  • Agency: NIA NIH HHS, United States
    Id: R01 AG043617
  • Agency: NIA NIH HHS, United States
    Id: R01 AG017917
  • Agency: NIA NIH HHS, United States
    Id: P50 AG005134
  • Agency: NIA NIH HHS, United States
    Id: R01 AG048015
  • Agency: NIA NIH HHS, United States
    Id: P30 AG10161
  • Agency: NIA NIH HHS, United States
    Id: U01 AG46152
  • Agency: NIA NIH HHS, United States
    Id: P30 AG010161
  • Agency: NIA NIH HHS, United States
    Id: R01 AG15819
  • Agency: NIA NIH HHS, United States
    Id: R01 AG036836
  • Agency: NIA NIH HHS, United States
    Id: R01 AG015819

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This is a list of tools and resources that we have found mentioned in this publication.


BioLegend (tool)

RRID:SCR_001134

Commercial antibody supplier and developer for biomedical research. These products are compatible with use in flow cytometry and mass cytometry, immunoprecipitation and chip, western blotting, immunofluorescence microscopy, and quantitative multiplexing.

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FlowJo (tool)

RRID:SCR_008520

Software for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.

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PLINK (tool)

RRID:SCR_001757

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

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BEAGLE (tool)

RRID:SCR_001789

Software package for analysis of large-scale genetic data sets with hundreds of thousands of markers genotyped on thousands of samples. BEAGLE can * phase genotype data (i.e. infer haplotypes) for unrelated individuals, parent-offspring pairs, and parent-offspring trios. * infer sporadic missing genotype data. * impute ungenotyped markers that have been genotyped in a reference panel. * perform single marker and haplotypic association analysis. * detect genetic regions that are homozygous-by-descent in an individual or identical-by-descent in pairs of individuals. Beagle can also be used in conjunction with PRESTO, a program for fast and flexible permutation testing. PRESTO can compute empirical distributions of order statistics, analyze stratified data, and determine significance levels for one-stage and two-stage genetic association studies. BEAGLE is written in Java and runs on any computing platform with a Java version 1.6 interpreter (e.g. Windows, Unix, Linux, Solaris, Mac).

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International HapMap Project (tool)

RRID:SCR_002846

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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Miltenyi Biotec (tool)

RRID:SCR_008984

An Organization portal, Antibody supplier, Service resource,

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MACH (tool)

RRID:SCR_009621

QTL analysis based on imputed dosages/posterior_probabilities.

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