The referent level of a factor is chosen automatically if you don't specify (usually as it would appear in a
sort()). With your output, it looks like you had 3 levels to genotype, and those would have been genotype.0, genotype.1, genotype.2.
Since the first one is not shown, it was used as the referent.
So your regression estimates for the first (non-intercept) line would be interpreted as genotype.1 compared to genotype.0 after adjusting for height and weight in the model.
If you were looking at 0,1,2 copies of an allele, then 1 copy of the allele is associated with a change of -.184505 unit of the outcome versus no copy of the allele.
That is the best I can do without a reproducible example.
To illustrate, let's use the
iris() dataset that is built into R.
We can see the levels of the factor variable
 "setosa" "versicolor" "virginica"
The first level is the referent.
If we call a regression to explain the variation in Sepal.Length with the variation in species, sepal.width, and petal.length, we will see the following output.
summary(lm(Sepal.Length ~ Species + Sepal.Width + Petal.Length, data = iris))
lm(formula = Sepal.Length ~ Species + Sepal.Width + Petal.Length,
data = iris)
Min 1Q Median 3Q Max
-0.82156 -0.20530 0.00638 0.22645 0.74999
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.39039 0.26227 9.114 5.94e-16 ***
Speciesversicolor -0.95581 0.21520 -4.442 1.76e-05 ***
Speciesvirginica -1.39410 0.28566 -4.880 2.76e-06 ***
Sepal.Width 0.43222 0.08139 5.310 4.03e-07 ***
Petal.Length 0.77563 0.06425 12.073 < 2e-16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3103 on 145 degrees of freedom
Multiple R-squared: 0.8633, Adjusted R-squared: 0.8595
F-statistic: 228.9 on 4 and 145 DF, p-value: < 2.2e-16
You can see that the
virginica levels were added to the
species variable name. The missing level is the
referent and is
So in this model, both
virginica are negatively associated with the variation in
Sepal.Length after adjusted for the other covariates in the model.