SOS9030 ¨C Introduction to Social Science Genetics

Course description

• Course content
• Learning outcome
• Admission
• Prerequisites

• Teaching
• Examination

Course content

A growing number of social science data sources are providing molecular genetic data and researchers all over the world are interested in utilizing this information in order to better understand various social phenomena.

Course leader:

Felix Tropf. Felix is a sociologist and his current interests focus on social demography, genetics, and the life course. He is an Assistant Professor in Social Science Genetics at CREST/ENSAE, an Associate member of Nuffield College in Oxford and a Visiting Scientist at the Queensland Institute for Medical Research (QIMR) in Australia. He received the European Demography Award for best PhD Thesis. Felix¡¯ research has been published, amongst others, in Demography, Nature Genetics, Nature Human Behaviour, JAMA Psychiatry, Proceedings of the National Academy of Sciences and Population Studies.

Learning outcome

In this course, we will learn about the history of social science and behaviour genetics as well as about the state-of-the-art research and cutting-edge methods. After attending this workshop, participants should have a basic understanding of the fundamental advantages of integrating genetics into social science. They should understand the basic technical terms from quantitative genetics literature and be able to read and interpret studies concerning social science genetics. They should be able to conduct basic quantitative genetics analyses and interpret their findings. Participants need an interest and a basic understanding of quantitative social science research and some experience concerning the software R & Stata.

Admission

Ph.D.-students at the Department of Sociology and Human Geography register for the course in StudentWeb.

Interested participants outside the Department of Sociology and Human Geography shall fill out this application form.

The deadline for registration is 15. May 2022. After the deadline shall all applicants receive a note about if the application is approved.

Prerequisites

Formal prerequisite knowledge

Basic statistics for social sciences and experience with statistical software (Stata, R).

Teaching

We will start with a general introduction of genetics in social sciences discussing potential research questions we can answer using genetic data. We subsequently learn about the theory behind twin and family models and how to estimate heritability as the proportion of observed variance in an outcome, which is explained by genetic effects. We move on to see how heritability is measured using molecular genetic data and discuss various challenges and applications. We use Plink software to prepare and analyze genetic data and GCTA software to estimate quantitative genetic models.

We will discuss how to genetic variants are discovered, which are associated with social science outcomes of interest and how we can utilize these results in social science research in terms of controlling for confounding effects, dealing with genetic heterogeneity in social science models, estimating gene-environment interaction models and using genes as instrumental variables. Substantively, we will rely on recently published genetic discovery studies on educational attainment, subjective well-being and fertility.

The course comprises five whole days and will be taught as a combination of lectures and practical exercises. Students will work with genomic data and perform data and analysis operations in a lab-like setting.

Teaching room: seminar room 201 and 035 PC-room, Harriet Holters Building. Taking part in the full program requires physical attendance.

Schedule 

22-26. August 2022

Readings

1: Introduction to Social Science Genetics

Benjamin, D. J. et al. The promises and pitfalls of genoeconomics, Annu. Rev. Econ., 4 (2012), pp. 627-662

Cesarini, D., & Visscher, P. M. (2017). Genetics and educational attainment. Npj Science of Learning, 2(1), 4. http://doi.org/10.1038/s41539-017-0005-6

Conley, D. (2009). The promise and challenges of incorporating genetic data into longitudinal social science surveys and research. Biodemography and Social Biology, 55(2), 238¨C251.

Conley, D. and J. Fletcher. (2017). The Genome Factor: What the Social Genomics Revolution Reveals about Ourselves, Our History and the Future. Princeton: Princeton University Press.

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020.

Mills, M. C., and F. C. Tropf. Sociology, Genetics, and the Coming of Age of Sociogenomics. Annual Review of Sociology 46 (2020).

Mills, M. C., & Tropf, F. C. (2016). The Biodemography of Fertility: A Review and Future Research Frontiers. K?lner Zeitschrift F¨¹r Soziologie Und Sozialpsychologie, 55(Special Issues Demography), 397¨C424.

Zimmer, C. (2018). She Has Her Mother¡¯s Laugh: The Powers, Perversions, and Potential of Heredity. New York: MacMillan Publishers.

2: The Human Genome and Human Evolution

Courtiol, A., Tropf, F. C., & Mills, M. C. (2016). When genes and environment disagree: Making sense of trends in recent human evolution. Proceedings of the National Academy of Sciences, 113(28), 7693¨C7695. http://doi.org/10.1073/pnas.1608532113

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020. Chapters 1+ 3

Mukherjee, S. (2016). The Gene: An Intimate History. New York: Simon & Schuster.

Tropf, F. C. et al. (2015). Human fertility, molecular genetics, and natural selection in modern societies. PloS One, 10(6), e0126821

3: Genome-Wide Association Studies (GWAS)

Barban, N. et al. (2016). Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48.

Duncan, L. E., M. C. Keller, A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in Psychiatry. Am. J. Psychiatry . 168 , 1041¨C 1049 (2011).

Howe, Laurence J., et al. "Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects." Nature genetics 54.5 (2022): 581-592.

Karlsson Linn¨¦r, R. et al. (2019) Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet.. doi:10.1038/s41588-018-0309-3

Lee et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50 , 1112¨C 1121

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020. Chapter 4

Mills, M. C, C. Rahal (2019). A Scientometric Review of Genome-Wide Association Studies. Commun. Biol. 2 , doi:10.1038/s42003-018-0261-x.

Okbay, Aysu, et al. "Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals." Nature genetics 54.4 (2022): 437-449.

Okbay, A. et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539¨C542. http://doi.org/10.1038/nature17671

Okbay, A. et al. (2016). Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 1¨C13. doi:10.1038/ng.3552

Visscher, P. M. et al. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet. 101 , 5¨C 22.

4: (Missing) Heritability

Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families. Dordrecht, the Netherlands: Kluwer Academic Publishers.

Polderman, T. J. C. et al. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702¨C709.

Rietveld, C. A., et al. (2013). Molecular genetics and subjective well-being. Proceedings of the National Academy of Sciences, 110(24), 9692¨C9697.

Tropf, F. C., Barban, N., Mills, M. C., Snieder, H., & Mandemakers, J. J. (2015). Genetic influence on age at first birth of female twins born in the UK, 1919-68. Population Studies, 69(2), 129¨C145.

Tropf, F. C. et al. (2017). Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1, 757¨C765.

Turkheimer, E. Three Laws of Behavior Genetics and What They Mean. Curr. Dir. Psychol. Sci. 9, 160¨C164 (2000).

Turkheimer, E. et al (2003). Socioeconomic status modifies heritability of IQ in young children. Psychol. Sci. 14, 623¨C628.

5: Polygenic Scores

Belsky, D. W. & Israel, S. (2014). Integrating genetics and social science: genetic risk scores. Biodemography Soc. Biol. 60, 137¨C55.

Belsky, D. W. et al. The Genetics of Success. Psychol. Sci. 27, 957¨C972 (2016).

Belsky, D. W. & Harden, K. P. (2019). Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down. Curr. Dir. Psychol. Sci.. doi:10.1177/0963721418807729

Conley, D., et al. (2015). Is the Effect of Parental Education on Offspring Biased or Moderated by Genotype? Sociological Science, 2, 82¨C105. http://doi.org/10.15195/v2.a6

Conley, D. & Domingue, B. The Bell Curve Revisited: Testing Controversial Hypotheses with Molecular Genetic Data. (2016). doi:10.15195/v3.a23

Euesden, J., Lewis, C. M. & O¡¯Reilly, P. F. (2014). PRSice: Polygenic Risk Score software. Bioinformatics 31, btu848-1468.

Harden, et al. Genetic Associations with Mathematics Tracking and Persistence in Secondary School, bioRxiv, doi: https://doi.org/10.1101/598532

Liu, Hexuan. 2018. Social and Genetic Pathways in Multigenerational Transmission of Educational Attainment. American Sociological Review 83(2): 278¨C304. http://journals.sagepub.com/doi/10.1177/0003122418759651 (May 29, 2018)

Mehta, D., Tropf, F. C., Gratten, J., Bakshi, A., Zhu, Z., Bacanu, S.-A., ¡­ Wu, J. Q. (2016). Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women. JAMA Psychiatry, 73(5), 497¨C505. http://doi.org/10.1001/jamapsychiatry.2016.0129

Mills, M. C., Barban, N. & Tropf, F. C. (2018). The Sociogenomics of Polygenic Scores of Reproductive Behavior and Their Relationship to Other Fertility Traits. RSF Russell Sage Found. J. Soc. Sci. 4.

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020. Chapter 5+11

Vilhjalmsson, B. J. et al. (2015). Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am J Hum Genet 97, 576¨C592.

6: Gene-Environment Interplay

Conley, D et al. (2016). Changing Polygenic Penetrance on Phenotypes in the 20th Century Among Adults in the US Population. Sci. Rep. 6, 30348.

Domingue, B. W., H. Liu, A. Okbay, D. W. Belsky (2017). Genetic heterogeneity in depressive symptoms following the death of a spouse: Polygenic score analysis of the U.S. Health and retirement study. Am. J. Psychiatry, doi:10.1176/appi.ajp.2017.16111209.

Engzell, Per, and Felix C. Tropf. Heritability of education rises with intergenerational mobility. Proceedings of the National Academy of Sciences 116.51 (2019): 25386-25388.

Isungset, Martin A., et al. "Social and genetic associations with educational performance in a Scandinavian welfare state." Proceedings of the National Academy of Sciences 119.25 (2022): e2201869119.

Tucker-Drob, E. M. & Bates, T. C. (2016). Large Cross-National Differences in Gene ¡Á Socioeconomic Status Interaction on Intelligence. Psychol. Sci.. doi:10.1177/0956797615612727

Wedow, R. et al. Education, Smoking, and Cohort Change: Forwarding a Multidimensional Theory of the Environmental Moderation of Genetic Effects. Am. Sociol. Rev. (2018). doi:10.1177/0003122418785368

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020. Chapters 6+11

7: Practical

Mills, Melinda C., Nicola Barban, and Felix C. Tropf. An Introduction to Statistical Genetic Data Analysis. MIT Press, 2020. Chapters 7-11

7: Else

Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236¨C1241 (2015).

Conley, D. et al. Assortative mating and differential fertility by phenotype and genotype across the 20th century. Proc. Natl. Acad. Sci. 1523592113 (2016). doi:10.1073/pnas.1523592113

Domingue, B. W. et al. The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health. Proc. Natl. Acad. Sci. (2018). doi:10.1073/pnas.1711803115

Fry et al. (2017). Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am. J. Epidemiol. 186 , 1026¨C 1034.

Grotzinger, A. D. et al. Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits. bioRxiv 305029 (2018). doi:10.1101/305029

Mardis,  E. R. (2011).  A decade¡¯s perspective on DNA sequencing technology. Nature. 470, 198 ¨C203.

Smith, G. D. & Ebrahim, S. ¡®Mendelian randomization¡¯: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology (2003). doi:10.1093/ije/dyg070

Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229¨C237 (2018).

Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. (2018). doi:10.1038/s41467-017-02317-2

Torvik, Fartein Ask, et al. "Modeling assortative mating and genetic similarities between partners, siblings, and in-laws." Nature communications 13.1 (2022): 1-10.

Examination

The entire five-day event makes up the PhD course, with the equivalent of 5 credits. For approval you need to be an active participant throughout the course, be present on all days, read the curriculum, take actively part in the excercises, prepare and conduct a presentation and write a short essay. The deadline for the paper is 26. September 2022.