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

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

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

Author(s) -

Ehsan Ullah,

Michaël Aupetit,

Arun Das,

Abhishek Patil,

Noora Al Muftah,

Reda Rawi,

Mohamad Saad,

Halima Bensmail

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty1028

Subject(s) - visualization , computer science , node (physics) , data visualization , association test , kinship , population , data mining , multidimensional scaling , association (psychology) , bar chart , data science , biology , machine learning , genetics , genotype , statistics , gene , medicine , psychology , law , environmental health , structural engineering , engineering , political science , single nucleotide polymorphism , mathematics , psychotherapist

It 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.

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