| pubmed-article:18466543 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18466543 | lifeskim:mentions | umls-concept:C0026336 | lld:lifeskim |
| pubmed-article:18466543 | lifeskim:mentions | umls-concept:C0439962 | lld:lifeskim |
| pubmed-article:18466543 | lifeskim:mentions | umls-concept:C0004083 | lld:lifeskim |
| pubmed-article:18466543 | lifeskim:mentions | umls-concept:C2603343 | lld:lifeskim |
| pubmed-article:18466543 | pubmed:dateCreated | 2008-5-9 | lld:pubmed |
| pubmed-article:18466543 | pubmed:abstractText | A family-based association study design is not only able to localize causative genes more precisely than linkage analysis, but it also helps explain the genetic mechanism underlying the trait under study. Therefore, it can be used to follow up an initial linkage scan. For an association study of binary traits in general pedigrees, we propose a logistic mixture model that regresses the trait value on the genotypic values of markers under investigation and other covariates such as environmental factors. We first tested both the validity and power of the new model by simulating nuclear families inheriting a simple Mendelian trait. It is powerful when the correct disease model is specified and shows much loss of power when the dominance of a model is inversely specified, i.e., a dominant model is wrongly specified as recessive or vice versa. We then applied the new model to the Genetic Analysis Workshop (GAW) 15 simulation data to test the performance of the model when adjusting for covariates in the case of complex traits. Adjusting for the covariate that interacts with disease loci improves the power to detect association. The simplest version of the model only takes monogenic inheritance into account, but analysis of the GAW simulation data shows that even this simple model can be powerful for complex traits. | lld:pubmed |
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| pubmed-article:18466543 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18466543 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18466543 | pubmed:status | PubMed-not-MEDLINE | lld:pubmed |
| pubmed-article:18466543 | pubmed:issn | 1753-6561 | lld:pubmed |
| pubmed-article:18466543 | pubmed:author | pubmed-author:LuQingQ | lld:pubmed |
| pubmed-article:18466543 | pubmed:author | pubmed-author:ElstonRobert... | lld:pubmed |
| pubmed-article:18466543 | pubmed:author | pubmed-author:XingChaoC | lld:pubmed |
| pubmed-article:18466543 | pubmed:author | pubmed-author:XingGuanG | lld:pubmed |
| pubmed-article:18466543 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:18466543 | pubmed:volume | 1 Suppl 1 | lld:pubmed |
| pubmed-article:18466543 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18466543 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18466543 | pubmed:pagination | S44 | lld:pubmed |
| pubmed-article:18466543 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:18466543 | pubmed:articleTitle | A logistic mixture model for a family-based association study. | lld:pubmed |
| pubmed-article:18466543 | pubmed:affiliation | Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA. guan.xing@bms.com | lld:pubmed |
| pubmed-article:18466543 | pubmed:publicationType | Journal Article | lld:pubmed |
