Title |
Gene Size Matters
|
---|---|
Published in |
PLOS ONE, November 2012
|
DOI | 10.1371/journal.pone.0049093 |
Pubmed ID | |
Authors |
Alexandra Mirina, Gil Atzmon, Kenny Ye, Aviv Bergman |
Abstract |
In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias. |
X Demographics
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Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 33% |
Peru | 1 | 17% |
United Kingdom | 1 | 17% |
Ireland | 1 | 17% |
Belgium | 1 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 50% |
Members of the public | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 6% |
United Kingdom | 1 | 3% |
Brazil | 1 | 3% |
Unknown | 29 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 24% |
Professor | 5 | 15% |
Student > Ph. D. Student | 5 | 15% |
Student > Bachelor | 3 | 9% |
Student > Postgraduate | 2 | 6% |
Other | 6 | 18% |
Unknown | 4 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 17 | 52% |
Biochemistry, Genetics and Molecular Biology | 6 | 18% |
Psychology | 2 | 6% |
Computer Science | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 1 | 3% |
Unknown | 5 | 15% |