I'm assuming that when you say you have "10 results for each group", you mean you have 10 independent mice in each group, and one measurement/mouse. Also, the assumption is that you don't have a priori predictions about specific comparisons you want to make.
If so, then you need to run a factoral analysis of variance (if you are using the menu system in SPSS, go to Analyze > General Linear Model > Univariate...; NOTE: That is GENERAL Linear Model, not GENERALIZED Linear Model).
Again, if you don't have specific comparisons that you are predicting a priori, then you might want to chose the option for post hoc contrasts to compare the 6 groups (2 diets X 3 genotypes). You might want to find out what post hoc method is most common in your discipline, but Tukey's HSD (Called just Tukey, in SPSS I believe) is pretty popular. The Bonferroni is also a pretty widely accepted method. These will make all pair-wise comparisons between your 6 groups, controlling for the inflation in your Type I error rate because you are making multiple comparisons and because you are doing this post hoc (i.e., after you already know that at least some of the groups are different because you have the ANOVA results).