Evidence map›Paper›PMID 30590437›Full record

ArticleBioinformatics (Oxford, England)2019

KinVis: a visualization tool to detect cryptic relatedness in genetic datasets.

Ehsan Ullah, Michaël Aupetit, Arun Das, Abhishek Patil, Noora Al Muftah, Reda Rawi, Mohamad Saad, Halima Bensmail

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

Article in Bioinformatics (Oxford, England), 2019. 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
0.4field-weighted citation impact, top 29% 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, 7 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

8 authors at 3 institutions in 2 countries.

Ehsan UllahQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Michaël AupetitQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Arun DasQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Abhishek PatilQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Noora Al MuftahDepartment of Computational Biology and Quantitative Genetics, Harvard School of Public Health, Boston, MA, USA.
Reda RawiQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Mohamad SaadQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Halima BensmailQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Hamad bin Khalifa University · QAHarvard University · USNational Institute of Allergy and Infectious Diseases · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationIt is important to characterize individual relatedness in terms of familial relationships and underlying population structure in genome-wide association studies for correct downstream analysis. The characterization of individual relatedness becomes vital if the cohort is to be used as reference panel in other studies for association tests and for identifying ethnic diversities. In this paper, we propose a kinship visualization tool to detect cryptic relatedness between subjects. We utilize multi-dimensional scaling, bar charts, heat maps and node-link visualizations to enable analysis of relatedness information. AVAILABILITY AND IMPLEMENTATION: Available online as well as can be downloaded at http://shiny-vis.qcri.org/public/kinvis/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genome-Wide Association StudySoftware

Identifiers

PMID30590437
PMCPMC6931347
OpenAlexW2905963422

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.