A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State.

ABSTRACT: The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in

SUBMITTER: Graffelman J 

PROVIDER: S-EPMC6491861 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature