Sorry, from our other old thread where i just picked up your added comment: C: Gaussian modeling of GWAS data

Yes, that is possible. In that case, you would use a linear model with cholesterol as the y variable:

lm(cholesterol ~ gene1)
lm(cholesterol ~ gene2)
lm(cholesterol ~ gene3)
...
lm(cholesterol ~ geneX)

Then take the statistically significant genes and put them together into a preliminary 'final' model that would require yet further testing:

lm(cholesterol ~ gene4 + gene10 + gene678)

( for further testing of the preliminary final model, see here: A: Resources for gene signature creation )

lm() will assume Gaussian.

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You could also just use glm() and specify Gaussian as the family, which would be the same (but can differ if you specify a particular link function with the family).

glm(cholesterol ~ gene1, family=gaussian)