| Title |
GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits
|
|---|---|
| Published in |
BMC Genomic Data, January 2011
|
| DOI | 10.1186/1471-2156-12-12 |
| Pubmed ID | |
| Authors |
Sheng Feng, Shengchu Wang, Chia-Cheng Chen, Lan Lan |
| Abstract |
In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Australia | 1 | 25% |
| Japan | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 2% |
| France | 2 | 1% |
| United States | 2 | 1% |
| Australia | 1 | <1% |
| Sweden | 1 | <1% |
| Hong Kong | 1 | <1% |
| Mexico | 1 | <1% |
| Germany | 1 | <1% |
| Japan | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 153 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 53 | 32% |
| Student > Ph. D. Student | 38 | 23% |
| Student > Master | 10 | 6% |
| Student > Doctoral Student | 10 | 6% |
| Other | 8 | 5% |
| Other | 27 | 16% |
| Unknown | 22 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 67 | 40% |
| Biochemistry, Genetics and Molecular Biology | 20 | 12% |
| Medicine and Dentistry | 19 | 11% |
| Mathematics | 8 | 5% |
| Veterinary Science and Veterinary Medicine | 6 | 4% |
| Other | 19 | 11% |
| Unknown | 29 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2015.
All research outputs
#15,169,949
of 25,374,647 outputs
Outputs from BMC Genomic Data
#480
of 1,204 outputs
Outputs of similar age
#147,169
of 193,626 outputs
Outputs of similar age from BMC Genomic Data
#9
of 13 outputs
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