Abstract
Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes. However, lack of adequate data has for long time prevented investigations on the pathogenesis of diabetes where BMI was a mediator of the genetic causal effects on this disease. Of our particular interest is the underlying causal mechanisms of diabetes. We leveraged the summary statistics reported in two studies: UK Biobank (N = 336,473) and Genetic Investigation of ANthropometric Traits (GIANT, N = 339,224) to investigate BMI-mediated genetic causal pathways to diabetes. We first estimated the causal effect of BMI on diabetes by using four Mendelian randomization methods, where a total of 76 independent BMI-associated SNPs (R2 ≤ 0.001, P < 5 × 10−8) were used as instrumental variables. It was consistently shown that higher level of BMI (kg/m2) led to increased risk of diabetes. We then applied two Bayesian colocalization methods and identified shared causal SNPs of BMI and diabetes in genes TFAP2B, TCF7L2, FTO and ZC3H4. This study utilized integrative analysis of Mendelian randomization and colocalization to uncover causal relationships between genetic variants, BMI and diabetes. It highlighted putative causal pathways to diabetes mediated by BMI for four genes.
Highlights
Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes
Genome-wide association studies (GWASs) have identified hundreds of genetic variants, in particular, single nucleotide polymorphisms (SNPs) that are associated with both BMI and diabetes[10,11,12,13,14], which have induced investigations on the role of BMI-associated SNPs in the development of diabetes[2,15]
This work focused on “diabetes diagnosed by doctor” with data collected from a touchscreen question “Has a doctor ever told you that you have diabetes?”
Summary
Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes. We aim to (i) couple MR with Bayesian colocalization to explore whether BMI is a mediator in the genetic causal pathways to diabetes; (ii) investigate the performance of two Bayesian colocalization methods, COLOC and eCAVIAR23,25. COLOC estimates how likely there is a shared causal SNP in a genetic test region for a pair of traits, by assuming there exists at most one causal SNP in the region for either trait, while eCAVIAR allows for multiple causal SNPs. In particular, we exploit the summary results of two independent large-scale GWASs: BMI from the Genetic Investigation of ANthropometric Traits (GIANT) consortium[10] and diabetes from the UK Biobank[31] (round 1), by using BMI-associated SNPs as instruments in MR analysis to estimate causal effect of BMI on diabetes. If there is evidence for a statistically significant causal effect, we further investigate whether there are shared causal SNPs between BMI and diabetes using COLOC, and gain insights into the underlying mechanisms of diabetes (Fig. 1)
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