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Both binary and continuous traits were simulated.
Our methodology only requires summary statistics and is able to handle both binary and continuous traits.
Power of the methods also was evaluated for binary and continuous traits under the single locus alternative model, with or without covariate effects.
Figure 3 presents the empirical type I error rates for both the binary and continuous traits, with or without covariate effects.
Thus, further testing is needed to apply this methodology to a more realistic scenario like the use of an animal model and joint analysis of binary and continuous traits.
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Germination and survival to reproduction were analyzed as binary traits using PROC GLIMMIX or GENMOD and continuous traits were analyzed using PROC MIXED or PROC GLM with SAS (Version 8.1 or 9.2; SAS Institute Inc., Cary, NC, USA).
Cost vectors associated with binary and continuous variables.
For FBAT-GEE, both binary and continuous phenotypes can be entered.
Binary and continuous outcomes were analyzed separately.
Analyses are presented separately for binary and continuous biomarkers.
To test this hypothesis we transformed the metabolic data into a binary matrix and compared the binary and continuous results.
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binary and continuous data
binary and continuous markers
binary and continuous covariates
binary and continuous outcomes
binary and continuous biomarkers
binary and continuous images
binary and continuous relationships
binary and ordinal traits
binary and continuous inputs
binary and continuous landscapes
binary and quantitative traits
binary and continuous markers/exposures
binary and continuous variables
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