Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis

Abstract

ObjectiveWe aimed to estimate genetic correlation, identify shared loci and test causality between leptin levels and type 2 diabetes (T2D).Research design and methodsOur study consists of three parts. First, we calculated the genetic correlation of leptin levels and T2D or glycemic traits by using linkage disequilibrium score regression analysis. Second, we conducted a large-scale genome-wide cross-trait meta-analysis using cross-phenotype association to identify shared loci between trait pairs that showed significant genetic correlations in the first part. In the end, we carried out a bidirectional MR analysis to find out whether there is a causal relationship between leptin levels and T2D or glycemic traits.ResultsWe found positive genetic correlations between leptin levels and T2D (Rg=0.3165, p=0.0227), fasting insulin (FI) (Rg=0.517, p=0.0076), homeostasis model assessment-insulin resistance (HOMA-IR) (Rg=0.4785, p=0.0196), as well as surrogate estimates of β-cell function (HOMA-β) (Rg=0.4456, p=0.0214). We identified 12 shared loci between leptin levels and T2D, 1 locus between leptin levels and FI, 1 locus between leptin levels and HOMA-IR, and 1 locus between leptin levels and HOMA-β. We newly identified eight loci that did not achieve genome-wide significance in trait-specific genome-wide association studies. These shared genes were enriched in pancreas, thyroid gland, skeletal muscle, placenta, liver and cerebral cortex. In addition, we found that 1-SD increase in HOMA-IR was causally associated with a 0.329 ng/mL increase in leptin levels (β=0.329, p=0.001).ConclusionsOur results have shown the shared genetic architecture between leptin levels and T2D and found causality of HOMA-IR on leptin levels, shedding light on the molecular mechanisms underlying the association between leptin levels and T2D.

The associations of leptin and adiponectin with the metabolic syndrome in an Indonesian and a Dutch population

“…In relation to the metabolic syndrome, our results in the Dutch population are supported by two Mendelian randomization studies on leptin. One Mendelian randomization study showed a causal relation between leptin and HOMA-IR, which may explain the association between leptin and type 2 diabetes [47], whereas another Mendelian randomization study showed that leptin was also potentially causally associated with blood pressure, particularly among current smokers [48]. The findings of these two studies, which were conducted in a White/European descent majority population, support our observations of positive associations between leptin with hyperglycemia and hypertension in the Dutch population.…”

Smoking Status and Type 2 Diabetes, and Cardiovascular Disease: A Comprehensive Analysis of Shared Genetic Etiology and Causal Relationship

“…To identify the regions of shared loci more precisely, fine-mapping credible set analysis based on a Bayesian algorithm was performed to determine credible sets of causal variants at each of the shared loci (43)(44)(45). The identified credible sets of causal variants were 99% likely to contain causal disease-associated SNPs by extracting variants that were highly linked (r 2 > 0.4) with the index SNP and within 500 kb of the index SNP (46).…”

“…Bidirectional MR is a form of causal inference analysis that can estimate causal directions and effects by employing genetic instruments selected from large-scale GWASs (55), even in the presence of unmeasured confounders. Three basic assumptions must be fulfilled to yield unbiased causal estimates in the MR analysis: 1) the genetic instruments used must be associated with the exposure, 2) the genetic instruments should be independent of the confounders between the exposure and outcome, and 3) the genetic instruments affect the outcome only through the exposure (46,56). In this study, we extracted genetic instruments (SNPs) with p < 5 × 10 −8 from the GWAS summary statistics of the exposure of interest, conducted the horizontal pleiotropy test, and selected independent genetic instruments at r 2 < 0.001 to satisfy these three assumptions.…”

Joint Analysis of Genome-Wide Association Data Reveals No Genetic Correlations Between Low Back Pain and Neurodegenerative Diseases

“…The last two decades have witnessed great strides in GWASs ( Visscher et al, 2017 ), particularly increased samples and augmented power, and numerous single-nucleotide polymorphisms (SNPs) have been identified for common disorders, including self-reported back pain ( Freidin et al, 2019 ) and chronic back pain ( Suri et al, 2018 ). From the perspective of human genomics and genetic epidemiology, cutting-edge statistical tools such as linkage disequilibrium score regression (LDSC) ( Bulik-Sullivan et al, 2015 ; Zheng et al, 2017 ) and Mendelian randomization (MR) ( Hemani et al, 2018 ; Walker et al, 2019 ), have made it possible to use GWAS summary-level data to explore genetic correlation ( Wang et al, 2020 ; Zhuang et al, 2021 ) and make causal inference ( He et al, 2020 ; Zhang et al, 2020 ) within a wide spectrum of complex traits.…”