diff --git a/docs/quick-start.md b/docs/quick-start.md
index 7fe6eaf..71eee07 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -157,7 +157,7 @@ The simulated phenotypes can be scaled by using the {func}`normalise_phenotypes`
 will first normalise the phenotype by subtracting the mean of the input phenotype from each
 value and divide it by the standard devitation of the input phenotype.
 Afterwards, it scales the normalised phenotype based on the mean and variance input.
-The output of {func}`normalise_phenotype` is a {class}`pandas.DataFrame` object with the scaled phenotypes.
+The output of {func}`normalise_phenotypes` is a {class}`pandas.DataFrame` object with the scaled phenotypes.
 
 An example usage of this function is shown below:
 
diff --git a/tstrait/simulate_phenotype.py b/tstrait/simulate_phenotype.py
index 877a704..aa6ea94 100644
--- a/tstrait/simulate_phenotype.py
+++ b/tstrait/simulate_phenotype.py
@@ -56,6 +56,9 @@ def sim_phenotype(
         Trait model that will be used to simulate effect sizes.
     num_causal : int, default None
         Number of causal sites. If None, number of causal sites will be 1.
+    causal_sites : list, default None
+        List of site IDs that have causal allele. If None, causal site IDs will be
+        chosen randomly according to num_causal.
     alpha : float, default None
         Parameter that determines the degree of the frequency dependence model. Please
         see :ref:`frequency_dependence` for details on how this parameter influences