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Multidimensional Scaling for relatedness research

Research Leader:
Jan Graffelman
Universitat Politècnica de Catalunya UPC

Collaborators: Iván Galván, Rafael de Cid, PMPPC-IGTP

Multidimensional scaling is a well-known multivariate technique, that is often used in genetics for uncovering population substructure. In this project we investigate multidimensional scaling of marker data for relatedness research. Relatedness is usually investigated by estimating and plotting identity-by-state and identity-by-descent allele sharing statistics. We show that outlying individuals in a map obtained by multidimensional scaling of genetic variables do not necessarily stem from a different human population, but can be the consequence of relatedness. The usefulness of multidimensional scaling in relatedness investigation is illustrated with data from the GCAT cohort. We propose a method for classifying pairs of individuals into relationship categories that combines genetic bootstrapping, multidimensional scaling and discriminant analysis.


Contact person: Jan Graffelman

Web link: https://www.eio.upc.edu/