| pubmed-article:14502464 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0015576 | lld:lifeskim |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0039593 | lld:lifeskim |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0022885 | lld:lifeskim |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0392366 | lld:lifeskim |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0004083 | lld:lifeskim |
| pubmed-article:14502464 | lifeskim:mentions | umls-concept:C0679199 | lld:lifeskim |
| pubmed-article:14502464 | pubmed:issue | 4 | lld:pubmed |
| pubmed-article:14502464 | pubmed:dateCreated | 2003-9-25 | lld:pubmed |
| pubmed-article:14502464 | pubmed:abstractText | For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies. | lld:pubmed |
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| pubmed-article:14502464 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:14502464 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:14502464 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:14502464 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:14502464 | pubmed:issn | 0002-9297 | lld:pubmed |
| pubmed-article:14502464 | pubmed:author | pubmed-author:LairdNan MNM | lld:pubmed |
| pubmed-article:14502464 | pubmed:author | pubmed-author:WeissScott... | lld:pubmed |
| pubmed-article:14502464 | pubmed:author | pubmed-author:SilvermanEdwi... | lld:pubmed |
| pubmed-article:14502464 | pubmed:author | pubmed-author:LangeChristop... | lld:pubmed |
| pubmed-article:14502464 | pubmed:author | pubmed-author:DeMeoDawnD | lld:pubmed |
| pubmed-article:14502464 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:14502464 | pubmed:volume | 73 | lld:pubmed |
| pubmed-article:14502464 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:14502464 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:14502464 | pubmed:pagination | 801-11 | lld:pubmed |
| pubmed-article:14502464 | pubmed:dateRevised | 2009-11-19 | lld:pubmed |
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| pubmed-article:14502464 | pubmed:year | 2003 | lld:pubmed |
| pubmed-article:14502464 | pubmed:articleTitle | Using the noninformative families in family-based association tests: a powerful new testing strategy. | lld:pubmed |
| pubmed-article:14502464 | pubmed:affiliation | Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. clange@hsph.harvard.edu | lld:pubmed |
| pubmed-article:14502464 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:14502464 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
| pubmed-article:14502464 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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