Based on prior knowledge, the relationship between gender and earning more than 50000 dollars per year can vary depending on the dataset and the context. However, it is commonly observed that gender is not a strong predictor of income on its own, as it is influenced by various other factors such as education, occupation, and experience.

To analyze the relationship in this specific dataset, we can examine the distribution of the gender feature among the two target classes: "yes" (earning more than 50000 dollars per year) and "no" (earning less than or equal to 50000 dollars per year).

Let's assume we have a dataset with the following observations:

|   gender  |   income    |
|-----------|-------------|
|   Male    |   yes       |
|   Female  |   yes       |
|   Male    |   no        |
|   Female  |   yes       |
|   Male    |   no        |
|   Female  |   yes       |
|   Male    |   no        |

Based on this dataset, we can generate the following dictionary:

```json
{
	"no": ["Male"],
	"yes": ["Male", "Female"]
}
```

In this dictionary, the possible values of the gender feature for the target class "no" (earning less than or equal to 50000 dollars per year) are ["Male"], and for the target class "yes" (earning more than 50000 dollars per year) are ["Male", "Female"].