RT Journal Article
SR Electronic
T1 A Sample Covariance-Based Approach For Spatial Binary Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.06.189571
DO 10.1101/2020.07.06.189571
A1 Sahar Zarmehri
A1 Ephraim M. Hanks
A1 Lin Lin
YR 2020
UL http://biorxiv.org/content/early/2020/07/06/2020.07.06.189571.abstract
AB The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced single-nucleotide polymorphisms (SNPs): isolation by distance and isolation by resistance. We model the data using a Generalized Linear Mixed effect Model (GLMM) with spatially referenced random effects and provide a novel approach for estimating parameters in spatial GLMMs. In this approach, we use the links between binary probit models and bivariate normal probabilities to directly compute the model-based covariance function for spatial binary data. Parameter estimation is based on minimizing sum of squared distance between the elements of sample covariance and model-based covariance matrices. We analyze data including Brucella Abortus SNPs from spatially referenced hosts in the Greater Yellowstone Ecosystem (GYE).Competing Interest StatementFunding: This work was partially supported by the United States Geological Survey [grant number G16AC00055].