Title |
An empirical comparison of short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) for relatedness estimation in Chinese rhesus macaques (Macaca mulatta)
|
---|---|
Published in |
American Journal of Primatology, November 2013
|
DOI | 10.1002/ajp.22235 |
Pubmed ID | |
Authors |
Cody T. Ross, Jessica A. Weise, Sarah Bonnar, David Nolin, Jessica Satkoski Trask, David Glenn Smith, Betsy Ferguson, James Ha, H. Michael Kubisch, Amanda Vinson, Sree Kanthaswamy |
Abstract |
We compare the effectiveness of short tandem repeat (STR) and single nucleotide polymorphism (SNP) genotypes for estimating pairwise relatedness, using molecular data and pedigree records from a captive Chinese rhesus macaque population at the California National Primate Research Center. We find that a panel of 81 SNPs is as effective at estimating first-order kin relationships as a panel of 14 highly polymorphic STRs. We note, however, that the selected STRs provide more precise predictions of relatedness than the selected SNPs, and may be preferred in contexts that require the discrimination of kin related more distantly than first-order relatives. Additionally, we compare the performance of three commonly used relatedness estimation algorithms, and find that the Wang [2002] algorithm outperforms other algorithms when analyzing STR data, while the Queller & Goodnight [1989] algorithm outperforms other algorithms when analyzing SNP data. Future research is needed to address the number of SNPs required to reach the discriminatory power of a standard STR panel in relatedness estimation for primate colony management. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 2% |
Italy | 1 | 2% |
Unknown | 50 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 10 | 19% |
Researcher | 8 | 15% |
Student > Master | 7 | 13% |
Student > Doctoral Student | 5 | 10% |
Professor | 3 | 6% |
Other | 4 | 8% |
Unknown | 15 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 44% |
Biochemistry, Genetics and Molecular Biology | 10 | 19% |
Medicine and Dentistry | 2 | 4% |
Veterinary Science and Veterinary Medicine | 1 | 2% |
Engineering | 1 | 2% |
Other | 0 | 0% |
Unknown | 15 | 29% |