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

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Kirk, Paul DW 


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.


Funder: NIHR Cambridge Biomedical Research Centre


Body Mass Index, Cluster Analysis, Computational Biology, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Models, Genetic, Obesity

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PLoS Genet

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Public Library of Science (PLoS)
Wellcome Trust (204623/Z/16/Z)
National Institute for Health and Care Research (IS-BRC-1215-20014)
Medical Research Council (MC_UU_00002/7)
British Heart Foundation (None)
British Heart Foundation (CH/12/2/29428)
British Heart Foundation (RG/18/13/33946)
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