Based on my prior knowledge, the feature "native-country" could potentially have some relationship with the target variable "Does this person earn more than 50000 dollars per year?". 

To analyze this relationship, we can calculate the percentage of individuals from each native-country category who earn more than 50000 dollars per year. If there is a significant difference in percentages among different native countries, it might suggest a relationship between the feature and the target.

Based on the dataset provided, we can create a dictionary with the possible values of the feature "native-country" for each target class ("yes" and "no"):

```json
{
	"no": ["United-States", "Mexico", "Philippines", "Germany", "Canada", "Puerto-Rico", "El-Salvador", "India", "Cuba", "Italy", "England", "China", "South", "Japan", "Jamaica", "Greece", "Dominican-Republic"], 
	"yes": ["?"]
}
```

Please note that in this dictionary, the "no" class includes the native countries where the percentage of individuals earning more than 50000 dollars per year is lower, and the "yes" class includes the native country "?", which is hard to predict without more information.