Summary

Evaluating the degree to which statistical models can explain or predict social outcomes such as educational attainment, fertility or wellbeing can contribute to advances in theory and scientific discovery, and help to understand how a society endures change and stress.

However, this research can rely on methodology which can exclude non-social factors such as genes.

Quantitative geneticists have developed a methodology to tackle this challenge, which brings together inference statistical models and measured genes. This has been used to explain 70 per cent of individual differences in height, for example.

The FINDME project combines social sciences with knowledge from genetic methods to build on this work.

It will:

  • separate genetic from social factors and take gene-environment interaction into account
  • provide interpretable statistical model specifications
  • compare societies and evaluate the stability of sociological explanations
  • quantify the relative contributions of social and non-social domains to individual differences.

FINDME uses data from the Millennium Cohort Study as it combines extensive social and environmental research data with molecular genetics data.

Principal Investigator

Felix Tropf

Felix Tropf

Associate Professor of Sociology, Purdue University, and in Population Data Science at the Centre for Longitudinal Studies

Felix is Associate Professor in Sociology at Purdue University and in Population Data Science at the Centre for Longitudinal Studies. He is an associate member of Nuffield College, Oxford, and a supervisor in the European Social Science Genetics Network. His research focuses on topics in social demography, quantitative genetics, and data science.

Research project team

Rafael Geurgas

Postdoctoral researcher

Rafael Geurgas

Laura Sheppard

Research Fellow

Katherine Thompson

Postdoctoral researcher

Katherine Thompson

Saul Justin Newman

Senior Research Fellow

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Centre for Longitudinal Studies
UCL Social Research Institute

20 Bedford Way
London WC1H 0AL

Email: clsdata@ucl.ac.uk

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