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Biodemography Soc Biol. 2016;62(1):1-18. doi: 10.1080/19485565.2015.1068106.

Genome-Wide Estimates of Heritability for Social Demographic Outcomes.

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a Graduate School of Education , Stanford University , Stanford , California , USA.
b Department of Sociology, Institute of Behavioral Science & Institute for Behavioral Genetics , University of Colorado-Boulder , Boulder , Colorado , USA.
c Department of Integrative Physiology, Institute of Behavioral Science , University of Colorado-Boulder , Boulder , Colorado , USA.
d Department of Sociology & Center for Genomics and Systems Biology , New York University , New York , New York , USA.
e Department of Epidemiology & Biostatistics, and Institute for Human Genetics , University of California, San Francisco , San Francisco , California , USA.


An increasing number of studies that are widely used in the demographic research community have collected genome-wide data from their respondents. It is therefore important that demographers have a proper understanding of some of the methodological tools needed to analyze such data. This article details the underlying methodology behind one of the most common techniques for analyzing genome-wide data, genome-wide complex trait analysis (GCTA). GCTA models provide heritability estimates for health, health behaviors, or indicators of attainment using data from unrelated persons. Our goal was to describe this model, highlight the utility of the model for biodemographic research, and demonstrate the performance of this approach under modifications to the underlying assumptions. The first set of modifications involved changing the nature of the genetic data used to compute genetic similarities between individuals (the genetic relationship matrix). We then explored the sensitivity of the model to heteroscedastic errors. In general, GCTA estimates are found to be robust to the modifications proposed here, but we also highlight potential limitations of GCTA estimates.

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