Is the linear mixed-effects model the right choice for analysing my data?

I'm in desperate need of advice in terms of the choice of statistical test for my analysis.

Briefly to explain what I am analyzing: I want to see the effect of 2 categorical variables ("genetic_profile" and "sex") and "age" variable (recorded in exact days which vary between subjects but the data is generally split into 3 and 9 months-old) on the continuous variable - "measurement" (measurement). There are 52 measurements (24 come from paired animals - i.e. measure was taken twice in 12 out of 38 animals).

Outcome variable: measurement
Predictor variables: genetic_profile (mutant/wild-type), sex (female/male), age (in days),
This is the model in R (also accounting for a possible interaction between genetic_profile and age). The random effect comes from animal.

model = lmer(measuremnet ~ Genetic_profile*Age + Gender + (1 | animal), data = data_set)

I attached a picture of a plot of residals vs fitted (plot(model)) and it definitely does not look like the right fit (residuals form 3 parallel stripes across the plot).


I believe it's important to mention that what I'm measuring is a small anatomical feature and therefore it has presumably a large degree of error since the precision measure was to just 1 decimal place and most measurements were very close or the same and differed by 0.1 or 0.2 or 0.3 mm at most).

Should I try and find a way to apply this model or was the suggestion wrong? I presume that a t-test should be applied if the linear mixed model is incorrect?