A lot going on here.  Some things are important, some are not, and some require looking at something other than what you might think have the results.   First, look at the test of type 3 effects, and at the denominator degrees of freedom--is that giving expected results?  If so, don't worry so much about the table with subjects=1.  That merely indicates that you have not parameterized the first random statement with a subject= option.   Next, PROC GLIMMIX does not provide p values for testing individual variance components.   There are a couple reasons that spring to mind--distributional assumptions for testing are not easily input is the most important, and if there is more than a single covariance parameter, the tests involve mixtures of chi squared distributions.  However, you can use the COVTEST statement to get a variety of likelihood ratio tests that compare full and reduced models.  Alternatively, you might fit the data in separate runs, with different covariance structures or elements and compare information criteria to see which model best captures the information in the raw data.   Thre is more that I might suggest here, but getting the subject= issue sorted out is the most important, and has to be done before moving to a conditional model--which I think is strongly suggested if you are considering genotype as a random effect, rather than as a fixed effect.   Steve Denham ... View more