The genome projects in human and other species have made genetic data widely available and pose challenges as well as opportunities for statistical analysis. In this paper we elaborate the concept of integrated analysis of genetic data, such that most aspects of analyses can be done effectively and efficiently in environments with facility for database accessibility, graphics, mathematical/statistical routines, flexible programming language, re-use of available codes, Internet connectivity and availability. This extends an earlier discussion on software consolidation (Guo and Lange. Theor Pop Biol 57:1-11, 2000). A general context is laid out by recollecting the research paradigms for genetic mapping of complex traits and illustrated with the study of ageing, before turning to the computational tools currently used. We show that the R system (http://www.r-project.org) so far is the most comprehensive and widely available system. However, other commercial systems can potentially be successful. In particular, we compare SAS (http://www.sas.com), Stata (http://www.stata.com), S-PLUS (http://www.insightful.com) and give some indications of future development. Our investigation has important implications for both statisticians end other researchers actively engaged in analysis of genetic data.