Abstract
BackgroundMany disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.ResultsThe proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.ConclusionsThe proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/.
Highlights
Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes
We showed that our method is statistically more efficient than existing methods, and its computational simplicity makes possible large-scale genome-wide association analysis
The most efficient choices of them makes the proposed score test equivalent to more powerful quasi-likelihood score test (MQLS) statistic [33], and we extend this approach to the joint analysis of multiple phenotypes and genotypes
Summary
Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. If multiple genes have a causal effect on multiple phenotypes, and the genotype-phenotype models are multidimensional, multivariate analyses are often expected to be most efficient [7]. In such a case, if the marginal effects of genotypes on multiple phenotypes are separately tested, multiple p-values for each marginal effect need to be adjusted with multiple comparison correction methods [10,11,12], and for a large number of p-values, the chance to identify the disease susceptibility loci becomes smaller.
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