The SSGAC was founded in conjunction with the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium meeting in Boston on 12 Feb 2011. At the workshop, over fifty researchers from a range of disciplines met to discuss the need for and the feasibility of launching a research consortium for the study of the genetics of social-scientific outcomes. 


One major impetus for the formation of the SSGAC was growing recognition that existing approaches to gene discovery in social science have often not produced replicable findings. The pilot project of the SSGAC on educational attainment has demonstrated the feasibility of an alternative, rigorous, large-data approach to social science genetics. Ongoing and future projects of the consortium will continue this approach to genetic discovery, and work on integrating the insights from these discoveries into medical and social scientific research.

The CHARGE Consortium

The SSGAC operates under the auspices of CHARGE, a leading consortium for research in genetic epidemiology. Having access to the CHARGE infrastructure enables SSGAC researchers to benefit from the knowledge and expertise within the CHARGE community. The SSGAC also organizes workshops  in conjunction with the bi-annual CHARGE meetings. 

The Big Problem of Small Effects

Our research philosophy is described in the following two publications: Beauchamp et al. (2011) and Benjamin et al. (2012)In brief, our research strategy is motivated by the following observations: 

  • Genetic variants that are common in a population have very small individual effects on behavioral traits.

  • At present, it is difficult to use theory to restrict the set of genetic variants that may be associated with behavioral traits because most genetic variants could plausibly be linked to brain activity.

  • To keep the rate of false-positive findings at a tolerable level, studies must be well-powered and apply stringent significance thresholds.

  • Following in the footsteps of medical research, pooling samples from multiple cohorts may be a way to conduct adequately powered genetic-association studies.