Title |
Population-based rare variant detection via pooled exome or custom hybridization capture with or without individual indexing
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Published in |
BMC Genomics, December 2012
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DOI | 10.1186/1471-2164-13-683 |
Pubmed ID | |
Authors |
Enrique Ramos, Benjamin T Levinson, Sara Chasnoff, Andrew Hughes, Andrew L Young, Katherine Thornton, Allie Li, Francesco LM Vallania, Michael Province, Todd E Druley |
Abstract |
Rare genetic variation in the human population is a major source of pathophysiological variability and has been implicated in a host of complex phenotypes and diseases. Finding disease-related genes harboring disparate functional rare variants requires sequencing of many individuals across many genomic regions and comparing against unaffected cohorts. However, despite persistent declines in sequencing costs, population-based rare variant detection across large genomic target regions remains cost prohibitive for most investigators. In addition, DNA samples are often precious and hybridization methods typically require large amounts of input DNA. Pooled sample DNA sequencing is a cost and time-efficient strategy for surveying populations of individuals for rare variants. We set out to 1) create a scalable, multiplexing method for custom capture with or without individual DNA indexing that was amenable to low amounts of input DNA and 2) expand the functionality of the SPLINTER algorithm for calling substitutions, insertions and deletions across either candidate genes or the entire exome by integrating the variant calling algorithm with the dynamic programming aligner, Novoalign. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 40% |
United Kingdom | 2 | 40% |
Australia | 1 | 20% |
Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
Austria | 1 | 1% |
United Kingdom | 1 | 1% |
South Africa | 1 | 1% |
Spain | 1 | 1% |
Belgium | 1 | 1% |
Unknown | 70 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 27 | 35% |
Student > Ph. D. Student | 20 | 26% |
Student > Postgraduate | 9 | 12% |
Student > Doctoral Student | 4 | 5% |
Other | 4 | 5% |
Other | 11 | 14% |
Unknown | 3 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 43 | 55% |
Biochemistry, Genetics and Molecular Biology | 13 | 17% |
Medicine and Dentistry | 11 | 14% |
Computer Science | 2 | 3% |
Engineering | 2 | 3% |
Other | 2 | 3% |
Unknown | 5 | 6% |