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Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

Author

Listed:
  • Ricky Lali

    (Vascular and Stroke Research Institute
    Faculty of Health Sciences)

  • Michael Chong

    (Vascular and Stroke Research Institute
    Faculty of Health Sciences)

  • Arghavan Omidi

    (Vascular and Stroke Research Institute)

  • Pedrum Mohammadi-Shemirani

    (Vascular and Stroke Research Institute
    Faculty of Health Sciences)

  • Ann Le

    (Vascular and Stroke Research Institute
    Faculty of Health Sciences)

  • Edward Cui

    (Vascular and Stroke Research Institute)

  • Guillaume Paré

    (Vascular and Stroke Research Institute
    Faculty of Health Sciences
    Faculty of Health Sciences
    Faculty of Health Sciences)

Abstract

Rare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesize that rare variant burden over a large number of genes can be combined into a predictive rare variant genetic risk score (RVGRS). We propose a method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A calibrated RVGRS strongly associates with coronary artery disease (CAD) in European and South Asian populations by capturing the aggregate effect of rare variants through a polygenic model of inheritance. The RVGRS identifies 1.5% of the population with substantial risk of early CAD and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and a common variant genetic risk score.

Suggested Citation

  • Ricky Lali & Michael Chong & Arghavan Omidi & Pedrum Mohammadi-Shemirani & Ann Le & Edward Cui & Guillaume Paré, 2021. "Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26114-0
    DOI: 10.1038/s41467-021-26114-0
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