Evidence map›Paper›PMID 30258280›Full record

ArticleCurrent genomics2018

NeuroArray, A Custom CGH Microarray to Decipher Copy Number Variants in Alzheimer's Disease.

Denis Cuccaro, Maria Guarnaccia, Rosario Iemmolo, Velia D'Agata, Sebastiano Cavallaro

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Denis Cuccaro1Institute of Neurological Sciences, National Research Council, Section of Catania, Catania, Italy; 2Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Maria Guarnaccia1Institute of Neurological Sciences, National Research Council, Section of Catania, Catania, Italy; 2Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Rosario Iemmolo1Institute of Neurological Sciences, National Research Council, Section of Catania, Catania, Italy; 2Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Velia D'Agata1Institute of Neurological Sciences, National Research Council, Section of Catania, Catania, Italy; 2Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Sebastiano Cavallaro1Institute of Neurological Sciences, National Research Council, Section of Catania, Catania, Italy; 2Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Institute of Neurological Sciences · ITNational Research Council · ITUniversity of Catania · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCopy Number Variants (CNVs) represent a prevailing type of structural variation (deletions or duplications) in the human genome. In the last few years, several studies have demonstrated that CNVs represent significant mutations in Alzheimer's Disease (AD) hereditability. Currently, innovative high-throughput platforms and bioinformatics algorithms are spreading to screening CNVs involved in different neurological diseases. In particular, the use of custom arrays, based on libraries of probes that can detect significant genomic regions, have greatly improved the resolution of targeted regions and the identification of chromosomal aberrations.

objectiveIn this work, we report the use of NeuroArray, a custom CGH microarray useful to screening and further investigate the role of the recurring genomic aberrations in patients with confirmed or suspected AD.

methodsThe custom oligonucleotide aCGH design includes 641 genes and 9118 exons, linked to AD. The genomic DNA was isolated from blood samples of AD affected patients. The entire protocol of custom NeuroArray included digestion, labelling and hybridization steps as a standard aCGH assay.

resultsThe NeuroArray analysis revealed the presence of amplifications in several genes associated with AD. In the coding regions of these genes, 14,586 probes were designed with a 348 bp median probe spacing. The majority of targeted AD genes map on chromosomes 1 and 10. A significant aspect of the NeuroArray design is that 95% of the total exon targets is covered by at least one probe, a resolution higher than CGH array platforms commercially available.

conclusionBy identifying with a high sensitivity the chromosomal abnormalities in a large panel of AD-related genes and other neurological diseases, the NeuroArray platform is a valid tool for clinical diagnosis.

Indexed as

aCGHAlzheimer’s diseaseCopy number variantsDiagnosisGenomicsNeuroArray

Identifiers

PMID30258280
PMCPMC6128388
OpenAlexW2785256097

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.