UCLA Bioinformatics Program

Speaker: John Novembre, Dept of Ecology and Evolutionary Biology & Bioinformatics IDP, UCLA

When: Mon, May 19, 2008

Where: CNSI Auditorium

Abstract:
The size of recent single nucleotide polymorphism (SNP) data sets and the need to account for population structure in genome-wide association studies has led to a renewed use of principal components analysis (PCA) in the field of population genetics. Despite its widespread use in many fields, how PCA behaves for spatially structured data is not widely known. In this talk I will show how PCA behaves with spatial data with an emphasis on the impact this behavior has for how PCA has been applied and interpreted in population genetics. I will also demonstrate this behavior in an analysis of a novel 500K SNP dataset from European human populations. Finally, the results suggest a method for predicting the spatial location of a DNA sample of unknown origin within Europe, and I will show, that one can do surprisingly well at this task, despite overall low levels of differentiation among Europeans.

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