A generalized quasi-likelihood scoring approach for simultaneously testing the genetic association of multiple traits

In the genetic association analysis of Holstein cattle data, researchers are interested in testing the association between a genetic marker with more than one estimated breeding value phenotype. It is well known that testing each trait individually may lead to problems of controlling the overall type I error rate and simultaneous testing of the association between a marker and multiple traits is desired. The analysis of Holstein cattle data has additional challenges due to complicated relationships between subjects. Furthermore, phenotypic data in many other genetic studies can be quantitative, binary, ordinal, count data or a combination of different types of data. Motivated by these problems, we propose a novel statistical method that allows simultaneous testing of multiple phenotypes and the flexibility to accommodate data from a broad range of study designs. The empirical results indicate that this new method effectively controls the overall type I error rate at the desired level; it is also generally more powerful than testing each trait individually at a given overall type I error rate. The method is applied to the analysis of Holstein cattle data as well as to data from the Collaborative Study on the Genetics of Alcoholism to demonstrate the flexibility of the approach with different phenotypic data types.