Title |
Detection of identity by descent using next-generation whole genome sequencing data
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Published in |
BMC Bioinformatics, June 2012
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DOI | 10.1186/1471-2105-13-121 |
Pubmed ID | |
Authors |
Shu-Yi Su, Jay Kasberger, Sergio Baranzini, William Byerley, Wilson Liao, Jorge Oksenberg, Elliott Sherr, Eric Jorgenson |
Abstract |
Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and evidence of ancient IBD can be used to detect population structure in genetic association studies. Various methods for detecting IBD, including those implemented in the soft- ware programs fastIBD and GERMLINE, have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, in- cluding identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 3% |
Netherlands | 2 | 2% |
Germany | 2 | 2% |
Sweden | 2 | 2% |
Korea, Republic of | 1 | 1% |
Italy | 1 | 1% |
New Zealand | 1 | 1% |
United Kingdom | 1 | 1% |
Unknown | 82 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 32 | 34% |
Student > Ph. D. Student | 24 | 25% |
Student > Master | 11 | 12% |
Professor > Associate Professor | 7 | 7% |
Student > Doctoral Student | 5 | 5% |
Other | 13 | 14% |
Unknown | 3 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 54 | 57% |
Biochemistry, Genetics and Molecular Biology | 17 | 18% |
Medicine and Dentistry | 8 | 8% |
Computer Science | 3 | 3% |
Psychology | 3 | 3% |
Other | 8 | 8% |
Unknown | 2 | 2% |