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Activity Number:
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406
- Spatio-Temporal Methods in Ecology and Epidemiology
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #324285
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Title:
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Asymptotic Approaches for Spatial Genetic Data
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Author(s):
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Sahar Zarmehri* and Ephraim M Hanks
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Companies:
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The Pennsylvania State University and The Pennsylvania State University
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Keywords:
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Spatial statistics ;
Ecology ;
Landscape genetics
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Abstract:
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In this paper, we develop asymptotic approaches to estimate correlation in spatial genetic data. We model single nucleotide polymorphisms (SNPs) using binary probit regression, with spatially correlated random effects as latent variables. However, the size of SNPs data (often many thousands of SNPs are collected) makes this latent variable approach computationally difficult. We propose two asymptotic approaches to estimating parameters governing spatial autocorrelation in SNP data based on central limit theorems for the sample mean and the sample covariance of the observed SNPs data. We then compare each method with the result from a full Bayesian analysis using MCMC. We apply these methods to SNP data from Brucella abortus in Yellowstone National Park elk.
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Authors who are presenting talks have a * after their name.
