Based on prior knowledge, it is known that the sex of a patient can potentially have an association with heart disease. However, it is important to note that the relationship between sex and heart disease may vary depending on other factors, such as age, genetics, lifestyle, and co-morbidities.

To analyze the relationship between the "Sex" feature and the task of determining if the coronary angiography shows a heart disease, we can perform a statistical analysis or explore the available data. Assuming we have data on the sex of patients and their angiography results, we can examine the distribution of sex within each target class.

Once we determine the relationship, we can create a dictionary indicating the possible values of the "Sex" feature for each target class.

For instance, if our analysis shows that there is a higher incidence of heart disease in males compared to females, the dictionary would look like this:

```json
{
	"no": ["F"],
	"yes": ["M"]
}
```

On the other hand, if our analysis indicates that there is no significant difference in heart disease prevalence between males and females, the dictionary would look like this:

```json
{
	"no": ["M", "F"],
	"yes": ["M", "F"]
}
```

In this case, both target classes will have the same possible values for the "Sex" feature.

Overall, the analysis will depend on the available data and the insights gained from exploring the relationship between the "Sex" feature and the task of determining if the coronary angiography shows a heart disease.