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Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity

2022 Grant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0 ;ISSN: 1553-7390 ;EISSN: 1553-7404 ;DOI: 10.1371/journal.pgen.1009975

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  • Title:
    Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity
  • Author: Grant, AJ ; Gill, D ; Kirk, PDW ; Burgess, S
  • Subjects: 0604 Genetics ; Developmental Biology
  • Description: Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.
  • Publisher: Public Library of Science (PLoS)
  • Creation Date: 2021-12
  • Language: English
  • Identifier: ISSN: 1553-7390
    EISSN: 1553-7404
    DOI: 10.1371/journal.pgen.1009975
  • Source: PubMed Central database
    Geneva Foundation Free Medical Journals at publisher websites
    PLoS
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    Directory of Open Access Journals at publisher websites
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