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

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.Author summary: Genome-wide association studies have found many genetic variants that are correlated with traits, particularly complex traits such as body mass index (BMI). However, genetic association data cannot tell us how these variants influence the trait, or whether they influence the trait in the same way. Insight into these questions may be gained by analysing the associations between the variants and other related traits. Variants with similar patterns of associations across a set of traits may be thought to act via similar biological mechanisms. Here we present a new statistical method for grouping genetic variants according to their associations with chosen traits, so that each group represents variants acting on these traits in a distinct way. We apply the method to genetic variants associated with BMI and then study the effects of each of the identified groups of variants on coronary heart disease. We find a group of genetic variants associated with higher BMI and decreased risk of heart disease, which is in contrast to the established overall harmful effect of BMI on heart disease.