Title |
Testing for direct genetic effects using a screening step in family-based association studies
|
---|---|
Published in |
Frontiers in Genetics, January 2013
|
DOI | 10.3389/fgene.2013.00243 |
Pubmed ID | |
Authors |
Sharon M. Lutz, Stijn Vansteelandt, Christoph Lange |
Abstract |
In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are tested for a direct effect in the family-based study, a screening step can be used to minimize the burden of multiple comparisons in the causal analysis. We propose a 2-stage screening step that can be incorporated into the family-based association test (FBAT) approach similar to the conditional mean model approach in the Van Steen-algorithm (Van Steen et al., 2005). Simulations demonstrate that the type 1 error is preserved and this method is advantageous when multiple markers are tested. This method is illustrated by an application to the Framingham Heart Study. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 6% |
Unknown | 15 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 6 | 38% |
Student > Master | 3 | 19% |
Student > Ph. D. Student | 2 | 13% |
Professor | 1 | 6% |
Student > Bachelor | 1 | 6% |
Other | 2 | 13% |
Unknown | 1 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 5 | 31% |
Environmental Science | 1 | 6% |
Mathematics | 1 | 6% |
Nursing and Health Professions | 1 | 6% |
Computer Science | 1 | 6% |
Other | 5 | 31% |
Unknown | 2 | 13% |