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Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits

Author

Listed:
  • Andrew D. Grotzinger

    (University of Texas at Austin)

  • Mijke Rhemtulla

    (University of California, Davis)

  • Ronald Vlaming

    (Vrije Universiteit Amsterdam
    Erasmus University Rotterdam Institute for Behavior and Biology)

  • Stuart J. Ritchie

    (University of Edinburgh
    University of Edinburgh)

  • Travis T. Mallard

    (University of Texas at Austin)

  • W. David Hill

    (University of Edinburgh
    University of Edinburgh)

  • Hill F. Ip

    (Vrije Universiteit University Amsterdam)

  • Riccardo E. Marioni

    (University of Edinburgh
    University of Edinburgh)

  • Andrew M. McIntosh

    (University of Edinburgh
    University of Edinburgh)

  • Ian J. Deary

    (University of Edinburgh
    University of Edinburgh)

  • Philipp D. Koellinger

    (Vrije Universiteit Amsterdam
    Erasmus University Rotterdam Institute for Behavior and Biology)

  • K. Paige Harden

    (University of Texas at Austin
    University of Texas at Austin)

  • Michel G. Nivard

    (Vrije Universiteit University Amsterdam)

  • Elliot M. Tucker-Drob

    (University of Texas at Austin
    University of Texas at Austin)

Abstract

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.

Suggested Citation

  • Andrew D. Grotzinger & Mijke Rhemtulla & Ronald Vlaming & Stuart J. Ritchie & Travis T. Mallard & W. David Hill & Hill F. Ip & Riccardo E. Marioni & Andrew M. McIntosh & Ian J. Deary & Philipp D. Koel, 2019. "Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits," Nature Human Behaviour, Nature, vol. 3(5), pages 513-525, May.
    • Handle: RePEc:nat:nathum:v:3:y:2019:i:5:d:10.1038_s41562-019-0566-x
    DOI: 10.1038/s41562-019-0566-x
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