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

Abstract

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.

PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment

“…The most apparent difference between the clustering of our method and that of MR-Clust is Another comparable clustering method by Grant et al [35] uses genetic variant associations with a set of traits to identify groups of IVs with similar biological mechanisms. Their method, NAvMix, uses a directional clustering algorithm and includes a noise-cluster to increase robustness to outliers.…”

Examining the evidence for Mendelian randomization homogeneity assumption violation using instrument association with exposure variance

“…First, IVs may be acting via distinct causal pathways, for example a previous study found a cluster of IVs for BMI that had protective effects on cardiovascular disease 20 whereas the remaining IVs were associated with adverse effects. Furthermore, evidence for IV-exposure variance association could arise under horizontal pleiotropy 21 , for example if the IV is acting on the exposure through several pathways and some of these are influenced by effect modification.…”

Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A Mendelian randomisation study

“…As an additional sensitivity analysis to explore heterogeneity identified in our primary analysis (using cis IL6R SNPS), we performed clustering to identify SNPs that had a consistent effect on multiple traits [ 41 ]. In this analysis, we identified all outcomes for which we had effect estimates for all 26 SNPs (31/35 outcomes, all except the GenOSept and GaINS consortium) and performed a form of mixture modelling (noise augmented directional clustering) to identify SNPs that had correlated effects on different traits and cluster them.…”

“…This was performed using the navmix package in R using default settings (K = 10) on standardised associations between traits. Subsequently, we performed MR within each cluster to identify any evidence of heterogeneity [ 41 ].…”

“…As an additional sensitivity analysis to explore heterogeneity identified in our primary analysis (using cisIL6R SNPS), we performed clustering to identify SNPs that had a consistent effect on multiple traits [41]. In this analysis, we identified all outcomes for which we had effect…”