263T Poster - Population Genetics
Thursday June 09, 9:15 PM - 10:00 PM

Authors:
Lesly Lopez; Jordan Collignon; Suzanne Sindi; Emily Jane McTavish

Affiliation: University of California, Merced

Keywords:
Population history

Principal component analysis (PCA) is a popular dimension-reduction technique to summarize patterns of population structure. PCA is regularly applied to genetic data to investigate genetic variation. The expectation of projections on the principal components can estimated through pairwise coalescent times. We examine how isolation by distance affects coalescent times between samples and, consequently, the estimates of these expected pairwise coalescent times from the projections of the principal components. To simulate genetic variation caused by geographic distance we modified the original Wright Fisher model to violate the assumption of a panmictic population. We use a distance-weighted Wright Fisher model with a modified probability for the location of the parent of each haploid individual.