Table 1. Summary of covariance models.

Model	df.	Covariance parameters	Description	HR and C different among-line variance	HR and C different within-line variance	HR and C same among-line variance	HR and C same within-line variance	SAS Code
Full	6	4	Random effects for replicate line within selection treatment (linetype) and for mouse within line and linetype, allowing for separate variance estimates for both lines within linetype and mouse within line and linetype	x	x			proc mixed data = locus method = mivque0;
								class pop sub mouse;
								model COL1 = pop/solution;
								random sub(pop) /group = pop;
								random mouse(sub pop) /group = pop;
SepVarLines	6	3	Random effects for replicate line within selection treatment (linetype) and for mouse within line and linetype, allowing for separate variance estimates for line within linetype	x			x	proc mixed data = locus method = mivque0;
								class pop sub mouse;
								model COL1 = pop/solution;
								random sub(pop) /group = pop;
								random mouse(sub pop);
SepVarInd	6	3	Random effects for replicate line within selection treatment (linetype) and for mouse within line and linetype, allowing for separate variance estimates for mouse within line and linetype		x	x		proc mixed data = locus method = mivque0;
								class pop sub mouse;
								model COL1 = pop/solution;
								random sub(pop);
								random mouse(sub pop) /group = pop;
Simple	6	2	Random effects for replicate line within selection treatment (linetype) and for mouse within line and linetype (as used by Xu and Garland (2017))			x	x	proc mixed data = locus method = mivque0;
								class pop sub mouse;
								model COL1 = pop/solution;
								random sub(pop);
								random mouse(sub pop);

Multiple models used to analyze the allelic SNP data (two values per mouse) for whole-genome sequences from 79 mice. For each model, we used SAS Procedure Mixed with MIVQUE estimation (Xu and Garland 2017) to obtain the test statistic (F), significance level (P), and AICc (df. method was containment). For some loci, the within-line variance was zero for all eight lines. In those cases, we used direct enumeration to calculate a significance level, i.e., the probability of observing the pattern vs. the 23 possible combinations. See text for further details.