Title |
Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel
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Published in |
Investigative Genetics, July 2013
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DOI | 10.1186/2041-2223-4-13 |
Pubmed ID | |
Authors |
Caroline M Nievergelt, Adam X Maihofer, Tatyana Shekhtman, Ondrej Libiger, Xudong Wang, Kenneth K Kidd, Judith R Kidd |
Abstract |
Accurate determination of genetic ancestry is of high interest for many areas such as biomedical research, personal genomics and forensics. It remains an important topic in genetic association studies, as it has been shown that population stratification, if not appropriately considered, can lead to false-positive and -negative results. While large association studies typically extract ancestry information from available genome-wide SNP genotypes, many important clinical data sets on rare phenotypes and historical collections assembled before the GWAS area are in need of a feasible method (i.e., ease of genotyping, small number of markers) to infer the geographic origin and potential admixture of the study subjects. Here we report on the development, application and limitations of a small, multiplexable ancestry informative marker (AIM) panel of SNPs (or AISNP) developed specifically for this purpose. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 25% |
United Kingdom | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 50% |
Members of the public | 1 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Portugal | 1 | 1% |
Germany | 1 | 1% |
South Africa | 1 | 1% |
Denmark | 1 | 1% |
Spain | 1 | 1% |
United States | 1 | 1% |
Unknown | 91 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 28 | 29% |
Student > Ph. D. Student | 15 | 15% |
Student > Master | 12 | 12% |
Other | 7 | 7% |
Student > Bachelor | 6 | 6% |
Other | 9 | 9% |
Unknown | 20 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 37 | 38% |
Biochemistry, Genetics and Molecular Biology | 22 | 23% |
Medicine and Dentistry | 4 | 4% |
Computer Science | 4 | 4% |
Neuroscience | 2 | 2% |
Other | 7 | 7% |
Unknown | 21 | 22% |