Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T08:55:03.719Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrating genomic data and social science: Challenges and opportunities

Published online by Cambridge University Press:  18 January 2016

Jeremy Freese
Jeremy Freese*
Affiliation:
Department of Sociology, Northwestern University, 1810 Chicago Avenue, Evanston, IL 60208. [email protected]
Get access

Extract

Why should social scientists be interested in using molecular genetic data? Here are five reasons:

Type
NSF Workshop Report
Information
Politics and the Life Sciences , Volume 30 , Issue 2 , Fall 2011 , pp. 88 - 92
Copyright
Copyright © Association for Politics and the Life Sciences 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. See also Freese, Jeremy, “Genetics and the social science explanation of individual outcomes,” American Journal of Sociology 2008, 114:S1–S35.Google Scholar
2. Fletcher, Jason M. and Lehrer, Steven F., “Using genetic lotteries within families to examine the causal impact of poor health on academic achievement,” NBER Working Paper Series, July 2009, http://ssrn.com/abstract=1434663 CrossRefGoogle Scholar
3. Ioannidis, John P. A., “Why most published research findings are false,” PLoS Medicine 2005, 2:e124.Google Scholar
4. Shanahan, Michael, Vaisey, Stephen, Erickson, Lance D., and Smolen, Andrew, “Environmental contingencies and genetic propensities: Social capital, educational continuation, and a dopamine receptor polymorphism,” American Journal of Sociology 2008, 114:S260–S286.Google Scholar
5. Hewitt, John K., “Editorial policy on candidate gene association and candidate gene-by-environment interaction studies of complex traits,” Behavior Genetics 2012, 42:1–2.Google Scholar
6. Boardman, Jason D., “State-level moderation of genetic tendencies to smoke,” American Journal of Public Health 2009, 99:480–486.Google Scholar
7. Visscher, Peter, Medland, Sarah E., Ferreira, Manuel A. R., Morley, Katherine, Zhu, Gu, Cornes, Belinda K., Montgomery, Grant W., and Martin, Nicholas G., “Assumption-free estimation of heritability from genome-wide identity by descent sharing between full siblings,” PLoS Genetics 2006, 2:e41.Google Scholar
8. Lee, Sang Hong, Wray, Naomi R., Goddard, Michael E., and Visscher, Peter M., “Estimating missing heritability for disease from genome-wide association studies,” American Journal of Human Genetics 2011, 88: 294–305.Google Scholar
9. Smith, George Davey, “Mendelian randomization for strengthening causal inference in observational studies: Application to gene-by-environment interactions,” Perspectives on Psychological Science 2010, 5:527–545.Google Scholar
10. Lawlor, Debbie A., Harbord, Roger M., Sterne, Jonathan A. C., Timpson, Nic J., and Smith, George Davey, “Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology,” Statistics in Medicine 2008, 27:1133–1163.CrossRefGoogle Scholar
11. Lutfey, Karen and Freese, Jeremy, “Toward some fundamentals of fundamental causality: Socioeconomic status and health in the routine clinic visit for diabetes,” American Journal of Sociology 2005, 110:1326–1372.CrossRefGoogle Scholar
12. Vander Weele, Tyler J., “Genetic self-knowledge and the future of epidemiologic confounding,” American Journal of Human Genetics 2010, 87(2):168–172.Google Scholar