Genetic studies of body mass index yield new insights for obesity biology

Glgc,; Icbp,; Investigators, Magic

doi:10.1038/nature14177

Cited by 4,048 publications

(4,683 citation statements)

References 75 publications

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“…Therefore, we further investigated the causal effect of BMI on PAI‐1 levels using 77 genome‐wide significant loci identified in a large BMI GWAS in European populations (Data S1). 36 This result shows that BMI has a causal effect on PAI‐1 in the positive direction (β: 0.21; 95% CI: 0.13, 0.29; Table S3), and was consistent with sensitivity analyses using a median estimator approach and MR‐Egger regression to test for potential pleiotropic effects.…”

Section: Resultssupporting

confidence: 80%

“…Thus, we considered the MR analyses viable and obtained summary statistics for circulating PAI‐1 levels, CHD and CHD risk factors from previous GWASs as listed in Table 1. These are based on the largest GWAS meta‐analysis for each phenotype at the time of analysis and primarily conducted on European ancestry samples 28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42…”

Section: Methodsmentioning

confidence: 99%

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Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

Song

Burgess

Eicher

et al. 2017

JAHA

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BackgroundPlasminogen activator inhibitor type 1 (PAI‐1) plays an essential role in the fibrinolysis system and thrombosis. Population studies have reported that blood PAI‐1 levels are associated with increased risk of coronary heart disease (CHD). However, it is unclear whether the association reflects a causal influence of PAI‐1 on CHD risk.Methods and ResultsTo evaluate the association between PAI‐1 and CHD, we applied a 3‐step strategy. First, we investigated the observational association between PAI‐1 and CHD incidence using a systematic review based on a literature search for PAI‐1 and CHD studies. Second, we explored the causal association between PAI‐1 and CHD using a Mendelian randomization approach using summary statistics from large genome‐wide association studies. Finally, we explored the causal effect of PAI‐1 on cardiovascular risk factors including metabolic and subclinical atherosclerosis measures. In the systematic meta‐analysis, the highest quantile of blood PAI‐1 level was associated with higher CHD risk comparing with the lowest quantile (odds ratio=2.17; 95% CI: 1.53, 3.07) in an age‐ and sex‐adjusted model. The effect size was reduced in studies using a multivariable‐adjusted model (odds ratio=1.46; 95% CI: 1.13, 1.88). The Mendelian randomization analyses suggested a causal effect of increased PAI‐1 level on CHD risk (odds ratio=1.22 per unit increase of log‐transformed PAI‐1; 95% CI: 1.01, 1.47). In addition, we also detected a causal effect of PAI‐1 on elevating blood glucose and high‐density lipoprotein cholesterol.ConclusionsOur study indicates a causal effect of elevated PAI‐1 level on CHD risk, which may be mediated by glucose dysfunction.

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Section: Resultssupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

Song

Burgess

Eicher

et al. 2017

JAHA

Self Cite

View full text Add to dashboard Cite

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“…Although it has become a common approach for the analysis of GWAS data, 25 there are reservations about the impact of INT on the results from association testing. 24 Both Beasley et al 24 and Buzkova 26 investigated the effect of INT when there is heteroskedasticity and demonstrated that Type I error was not well-controlled.…”

Section: Discussionmentioning

confidence: 99%

The effect of phenotypic outliers and non-normality on rare-variant association testing

2016

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Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.

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“…We excluded one known locus, in GCKR, owing to its known pleotropic effect on multiple human complex traits [14]. In addition, we constructed a GRS for circulating BCAA levels with and without the PPMK1 single nucleotide polymorphism (SNP) rs9637599 and performed analysis considering the association of rs9637599 with BMI (p = 0.035) [15] and obesity class I (p = 0.00042) [16] in the publicly available GWAS summary data.…”

Section: Measurement Of Insulin Resistancementioning

confidence: 99%

Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels

et al. 2017

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Aims/hypothesis Fasting plasma levels of branched-chain amino acids (BCAAs) are associated with insulin resistance, but it remains unclear whether there is a causal relation between the two. We aimed to disentangle the causal relations by performing a Mendelian randomisation study using genetic variants associated with circulating BCAA levels and insulin resistance as instrumental variables. Methods We measured circulating BCAA levels in blood plasma by NMR spectroscopy in 1,321 individuals from the ADDITION-PRO cohort. We complemented our analyses by using previously published genome-wide association study (GWAS) results from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (n = 46,186) and from a GWAS of serum BCAA levels (n = 24,925). We used a genetic risk score (GRS), calculated using ten established fasting serum insulin associated variants, as an instrumental variable for insulin resistance. A GRS of three variants increasing circulating BCAA levels was used as an instrumental variable for circulating BCAA levels. Conclusions/interpretation Our results suggest that higher BCAA levels do not have a causal effect on insulin resistance while increased insulin resistance drives higher circulating fasting BCAA levels. Results

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Genetic studies of body mass index yield new insights for obesity biology

Cited by 4,048 publications

References 75 publications

Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

The effect of phenotypic outliers and non-normality on rare-variant association testing

Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels

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