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Home | Courses | Genome-wide Data Analysis (Not Offered in 2023-24)
Course

Genome-wide Data Analysis (Not Offered in 2023-24)

  • Teacher(s)
    Ronald de Vlaming, Aysu Okbay
  • Research field
    Data Science
  • Dates
    Period 3 - Jan 08, 2024 to Mar 01, 2024
  • Course type
    Field
  • Program year
    Second
  • Credits
    3

Course description

The goal of this course is to introduce key concepts, state-of-the-art methods, and computer tools in statistical genetics that can be applied in the social sciences. The course will be highly quantitative and interactive, covering topics such as the estimation and interpretation of heritability using molecular genetic data, genetic association studies, gene-based prediction, and identification strategies to isolate causal effects using genetic insights. It will emphasize methodological issues such as appropriate study design, data integrity, multiple testing, detecting and controlling for potential confounds, as well as factors influencing the accuracy of so-called polygenic indices.

External participants are invited to register for this course. (PhD) students register here, others register here. More information on course registration and course fees can be found here.

Prerequisites

A formal background in statistics or econometrics is required from students (at the level of a first year course in a graduate school PhD program in economics, psychology, or epidemiology), but no formal background in genetics will be assumed.

Course literature

Tentative list (more to be announced):

  • Falconer, D.S., Mackay, R.F.C. (1995). Introduction to Quantitative Genetics. 4th edition. Pearson. Chapters 1-2.
  • Harden, K.P. and Koellinger, P.D. (2020). Social Science Genetics: New Methods for Enduring Questions. Nature Human Behaviour. DOI:10.1038/s41562-020-0862-5 https://rdcu.be/b35XM
  • Plomin, R., DeFries, J., Knopik, V.S., Neiderhiser, J.M. (2013). Behavioral Genetics. 6th edition. New York: Worth Publishers. Chapters 2-4.
  • Visscher, P.M., Hill, W.G., Wray, N.R. (2008). Heritability in the genomics era — concepts and misconceptions. Nature Review Genetics, 9(4), 255-266.
  • Abdellaoui, A. et al. (2013). Population structure, migration, and diversifying selection in the Netherlands. European Journal of Human Genetics, 21(11), 1277-1285.
  • Marchini, J., Howie, B. (2010). Genotype imputation for genome-wide association studies. Nature Reviews Genetics, 11(7), 499-511.
  • Willer, C.J., Li, Y., Abecasis, G.R. (2010). METAL: fast and efficient meta-analysis of genome-wide association scans. Bioinformatics, 26(17), 2190-2191.
  • Winkler, T.W., et al. (2014) Quality control and conduct of genome-wide association metaanalyses. Nature Protocols, 9(5), 1192-1212.
  • Bulik-Sullivan, B., et al. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291-295.
  • Bulik-Sullivan, B., et al. (2015). An atlas of genetic correlations across human diseases and traits, Nature Genetics, 47(11), 1236-1241.
  • Ge, T. et al. (2019). Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications, 10, 1776.
  • Koellinger, P.D. and de Vlaming, R. (2019). Mendelian randomization: the challenge of unobserved environmental confounds. International Journal of Epidemiology, https://doi: 10.1093/ije/dyz138