The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 1 of 5
Back to Result List

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

  • 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 mis- sense 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 missenseHere 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 mis- sense 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.show moreshow less

Download full text files

  • zde016.pdfeng
    (1697KB)

    SHA-512:a7f87c7826c5c61868a0b28e5f0c8441f2ca2b6a7991982cec38c757ece641478d1600e1c8a84826ceef7d96e5cea263910db58523151d1cade22655a0f5bb13

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Remo MontiORCiD, Pia Rautenstrauch, Mahsa Ghanbari, Alva Rani James, Matthias KirchlerORCiD, Uwe Ohler, Stefan KonigorskiORCiDGND, Christoph LippertORCiD
URN:urn:nbn:de:kobv:517-opus4-586078
DOI:https://doi.org/10.25932/publishup-58607
Title of parent work (German):Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
Publication series (Volume number):Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät (16)
Publication type:Postprint
Language:English
Date of first publication:2022/11/10
Publication year:2022
Publishing institution:Universität Potsdam
Release date:2023/03/31
Issue:16
Number of pages:16
Source:Nature communications 13 (2022), 5332 DOI: 10.1038/s41467-022-32864-2
Organizational units:Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Peer review:Referiert
Publishing method:Open Access / Green Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Bibliographieeintrag der Originalveröffentlichung
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.