Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Remo Monti, Pia Rautenstrauch, Mahsa Ghanbari, Alva Rani James, Matthias Kirchler, Uwe Ohler, Stefan Konigorski and Christoph Lippert ()
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Remo Monti: Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty
Pia Rautenstrauch: Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB)
Mahsa Ghanbari: Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB)
Alva Rani James: Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty
Matthias Kirchler: Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty
Uwe Ohler: Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB)
Stefan Konigorski: Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty
Christoph Lippert: Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

Date: 2022
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DOI: 10.1038/s41467-022-32864-2

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