Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Combination of multiple microsatellite data sets to investigate genetic diversity and admixture of domestic cattle
Animal Genetics, Volume 37, No. 1, Year 2006
Notification
URL copied to clipboard!
Description
Microsatellite markers are commonly used for population genetic analyses of livestock. However, up to now, combinations of microsatellite data sets or comparison of population genetic parameters from different studies and breeds has proven difficult. Often different genotyping methods have been employed, preventing standardization of microsatellite allele calling. In other cases different sets of markers have been genotyped, providing differing estimates of population genetic parameters. Here, we address these issues and illustrate a general two-step regression approach in cattle using three different sets of microsatellite data, to combine population genetics estimates of diversity and admixture. This regression-based method is independent of the loci genotyped but requires common breeds in the data sets. We show that combining microsatellite data sets can provide new insights on the origin and geographical distribution of genetic diversity and admixture in cattle, which will facilitate global management of this livestock species. © 2005 International Society for Animal Genetics.
Authors & Co-Authors
Freeman, Abigail
Ireland, Dublin
Trinity College Dublin
B̀radley, Daniel G.
Ireland, Dublin
Trinity College Dublin
Nagda, Sonal N.
Kenya, Nairobi
International Livestock Research Institute Nairobi
Gibson, J. P.
Kenya, Nairobi
International Livestock Research Institute Nairobi
Australia, Armidale
University of new England Australia
Hanotte, Olivier H.
Kenya, Nairobi
International Livestock Research Institute Nairobi
Statistics
Citations: 88
Authors: 5
Affiliations: 3
Identifiers
Doi:
10.1111/j.1365-2052.2005.01363.x
ISSN:
02689146
e-ISSN:
13652052
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study