Risk genes influence the chance of an individual developing disease over their lifetime, although the age at onset (AAO) genes influence disease timing. These two categories are not disjoint; a gene that influences AAO might also appear to influence the risk. When an allele influences both AAO and risk, a reasonable question is whether we would have more power to detect association using a statistical test based on risk or AAO. To address this question, we compared power analytically for the Cochran-Armitage trend case-control test for risk and a linear regression case-only test for AAO. We also used simulations to compare the power of these tests with a 2-degree of freedom joint test (which combines the risk and AAO statistics) and the Cox proportional hazards survival model testing AAO (with censored data in controls). We found that when there is little heterogeneity, the case-control risk test has more power than the case-only AAO test (with equivalent sample sizes), but when the model is complex (e.g., with heterogeneity or reduced penetrance), the relationship reverses. The joint test generally outperforms the risk or AAO test alone and ultimately is our recommendation as a powerful alternative in many scenarios.