Association analysis of complex diseases using triads, parent-child dyads and singleton monads
1 Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Blvd, MSC 7510, Rockville, MD 20852, USA
2 Department of Biostatistics, Mailman School of Public Health, 722 West 168th street, 6th floor, Columbia University, New York, NY 10032, USA
3 Office of Biostatistics Research, Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bld. RKL2, Room 9196, Bethesda, MD 20892, USA
4 Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Blvd, MSC 7510, Rockville, MD 20852, USA
BMC Genetics 2013, 14:78 doi:10.1186/1471-2156-14-78Published: 4 September 2013
Triad families are routinely used to test association between genetic variants and complex diseases. Triad studies are important and popular since they are robust in terms of being less prone to false positives due to population structure. In practice, one may collect not only complete triads, but also incomplete families such as dyads (affected child with one parent) and singleton monads (affected child without parents). Since there is a lack of convenient algorithms and software to analyze the incomplete data, dyads and monads are usually discarded. This may lead to loss of power and insufficient utilization of genetic information in a study.
We develop likelihood-based statistical models and likelihood ratio tests to test for association between complex diseases and genetic markers by using combinations of full triads, parent-child dyads, and affected singleton monads for a unified analysis. A likelihood is calculated directly to facilitate the data analysis without imputation and to avoid computational complexity. This makes it easy to implement the models and to explain the results.
By simulation studies, we show that the proposed models and tests are very robust in terms of accurately controlling type I error evaluations, and are powerful by empirical power evaluations. The methods are applied to test for association between transforming growth factor alpha (TGFA) gene and cleft palate in an Irish study.