Title |
GACT: a Genome build and Allele definition Conversion Tool for SNP imputation and meta-analysis in genetic association studies
|
---|---|
Published in |
BMC Genomics, July 2014
|
DOI | 10.1186/1471-2164-15-610 |
Pubmed ID | |
Authors |
Arvis Sulovari, Dawei Li |
Abstract |
Genome-wide association studies (GWAS) have successfully identified genes associated with complex human diseases. Although much of the heritability remains unexplained, combining single nucleotide polymorphism (SNP) genotypes from multiple studies for meta-analysis will increase the statistical power to identify new disease-associated variants. Meta-analysis requires same allele definition (nomenclature) and genome build among individual studies. Similarly, imputation, commonly-used prior to meta-analysis, requires the same consistency. However, the genotypes from various GWAS are generated using different genotyping platforms, arrays or SNP-calling approaches, resulting in use of different genome builds and allele definitions. Incorrect assumptions of identical allele definition among combined GWAS lead to a large portion of discarded genotypes or incorrect association findings. There is no published tool that predicts and converts among all major allele definitions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 40% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 80% |
Members of the public | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 4% |
Unknown | 26 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 11 | 41% |
Student > Ph. D. Student | 8 | 30% |
Student > Master | 2 | 7% |
Student > Bachelor | 2 | 7% |
Lecturer > Senior Lecturer | 1 | 4% |
Other | 2 | 7% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 9 | 33% |
Computer Science | 6 | 22% |
Biochemistry, Genetics and Molecular Biology | 5 | 19% |
Mathematics | 2 | 7% |
Medicine and Dentistry | 2 | 7% |
Other | 0 | 0% |
Unknown | 3 | 11% |