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

Ricky Lali, Michael Chong, Arghavan Omidi, Pedrum Mohammadi-Shemirani, Ann Le, Edward Cui and Guillaume Paré ()
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Ricky Lali: Vascular and Stroke Research Institute
Michael Chong: Vascular and Stroke Research Institute
Arghavan Omidi: Vascular and Stroke Research Institute
Pedrum Mohammadi-Shemirani: Vascular and Stroke Research Institute
Ann Le: Vascular and Stroke Research Institute
Edward Cui: Vascular and Stroke Research Institute
Guillaume Paré: Vascular and Stroke Research Institute

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26114-0

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DOI: 10.1038/s41467-021-26114-0

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