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Beyond the factor indeterminacy problem using genome-wide association data

Nature Human Behaviour volume 8, pages 205–218 (2024)Cite this article

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Abstract

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

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Fig. 1: Metaphor for factor analysis.
Fig. 2: The factor model and alternative models.
Fig. 3: Heuristic representation of the causal chain from genetic polymorphisms to complex phenotypes under a factor model scenario and an overlap scenario.
Fig. 4: Structure of simulations for common factor and overlap generating models.
Fig. 5: Correlation matrices generated from process overlap and common factor models.
Fig. 6: The distribution of the heterogeneity statistic and the association statistic across simulation conditions.

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Acknowledgements

We thank M. Rhemtulla (University of California Davis), B. Domingue (Stanford University), K. Kanopka (New York University), S. Trejo (Princeton University), D. Londono-Correa (University of Texas at Austin) and C. Williams (University of Texas at Austin) for their invaluable feedback on earlier versions of this work. This research was supported by National Institutes of Health (NIH) grants R01MH120219 and RF1AG073593. E.M.T.-D. is a member of the Population Research Center and Center on Aging and Population Sciences at the University of Texas at Austin, which are supported by NIH grants P2CHD042849 and P30AG066614, respectively.

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Authors and Affiliations

  1. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Margaret L. Clapp Sullivan, K. Paige Harden & Elliot M. Tucker-Drob

  2. Department of Psychology, Michigan State University, East Lansing, MI, USA

    Ted Schwaba

  3. Population Research Center, University of Texas at Austin, Austin, TX, USA

    K. Paige Harden & Elliot M. Tucker-Drob

  4. Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA

    Andrew D. Grotzinger

  5. Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA

    Andrew D. Grotzinger

  6. Department of Biological Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Michel G. Nivard

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Correspondence to Margaret L. Clapp Sullivan.

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Clapp Sullivan, M.L., Schwaba, T., Harden, K.P. et al. Beyond the factor indeterminacy problem using genome-wide association data. Nat Hum Behav 8, 205–218 (2024). https://doi.org/10.1038/s41562-023-01789-1

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