Genetic analysis of moose populations from Minnesota and Yellowstone National Park

University of Minnesota M.S. thesis. December 2015. Major: Integrated Biosciences. Advisors: Jared Strasburg, Ronald Moen. 1 computer file (PDF); v, 73 pages. By assessing the amount and geographic distribution of genetic variation in moose we can better understand how microevolutionary processes an...

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Bibliographic Details
Main Author: Tjepkes, Tessa
Format: Thesis
Language:English
Published: 2015
Subjects:
DNA
Online Access:http://hdl.handle.net/11299/177051
Description
Summary:University of Minnesota M.S. thesis. December 2015. Major: Integrated Biosciences. Advisors: Jared Strasburg, Ronald Moen. 1 computer file (PDF); v, 73 pages. By assessing the amount and geographic distribution of genetic variation in moose we can better understand how microevolutionary processes and landscape features have influenced that variation. How the distribution of moose changes in the future will be partially dictated by the amount and content of genetic variation moose populations possess. Therefore, it will be useful to acquire more moose population genetic data and to study declining populations. My thesis had two primary objectives: (1) to compare the efficacy of DNA extraction from different biological samples and (2) to genotype a subset of Minnesota moose at a locus known to be associated with chronic wasting disease in other cervid populations. DNA for genetic analyses was extracted from blood, tissue, and pellets. Extracted DNA from all source types was sufficient for genotyping using 15 microsatellites and Sanger sequencing. However, DNA extracted from pellets was of both lower quality and quantity than DNA extracted from blood and tissue. Minnesota moose contain polymorphisms that have been correlated with increased susceptibility to chronic wasting disease in cervids in other areas. These results provide valuable comparisons of efficiency and effectiveness of DNA extraction protocols for tissue, blood, and fecal pellets as well as baseline population genetic data that can be used to detect future genetic changes in these populations.