Investigating the role of insulin in increased adiposity: Bi-directional Mendelian randomization study

AbstractInsulin may serve as a key causal agent which regulates fat accumulation in the body. Here we assessed the causal relationship between fasting insulin and adiposity using publicly-available results from two large-scale genome-wide association studies for body mass index and fasting insulin levels in a two-sample, bidirectional Mendelian Randomized approach. This approach is only valid on the condition that the two instruments are independent of one another. In analysis excluding overlapping loci, there was an increase of 0.20 (0.17, 0.23) log pmol/L fasting insulin per SD increase in BMI (P= 2.80 x 10−36), while there was a null effect of fasting insulin on BMI, with a 0.01 (−0.39, 0.38) SD decrease in BMI per log pmol/L increase in fasting insulin (P= 0.98). Furthermore, a high degree of heterogeneity in the causal estimates was obtained from the insulin-related variants, which may be attributed to varying mechanisms of action of the insulin-associated variants. Results were largely consistent when an Egger regression technique and weighted median and mode estimators were applied. Findings suggest that the positive correlation between adiposity and fasting insulin levels are at least in part explained by the causal effect of adiposity on increasing insulin, rather than vice versa.

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A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic etiology with obesity

European Respiratory Journal ◽

10.1183/13993003.00199-2021 ◽

2021 ◽

pp. 2100199

Author(s):

Zhaozhong Zhu ◽

Jiachen Li ◽

Jiahui Si ◽

Baoshan Ma ◽

Huwenbo Shi ◽

...

Keyword(s):

Lung Function ◽

Large Scale ◽

Causal Effect ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Etiology ◽

Trait Analysis ◽

Genome Wide ◽

Shared Genetic

Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, the knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWAS in other populations are lacking.We included 100 285 subjects from China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS were performed on FEV1, FVC, FEV1/FVC in CKB. We then performed genome-wide cross-trait analysis between the lung function and obesity traits (body mass index [BMI], BMI-adjusted waist-to-hip ratio, and BMI-adjusted waist circumference) to investigate the shared genetic effects in CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in CKB and its interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with CKB using 457 756 subjects from UK Biobank (UKB) for replication and investigation of ancestry specific effect.We identified 9 genome-wide significant novel loci for FEV1, 6 for FVC and 3 for FEV1/FVC in CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important pathways, including cell proliferation, embryo and tissue development. Mendelian randomisation analysis suggested significant negative causal effect of BMI on FEV1 and on FVC in both CKB and UKB. Lung function PRSs significantly modified the effect of change-in-BMI on change-in-lung function during an average follow-up of 8 years.This large-scale GWAS of lung function identified novel loci and shared genetic etiology between lung function and obesity. Change-in-BMI might affect change-in-lung function differently according to a subject's polygenic background. These findings may open new avenue for the development of molecular-targeted therapies for obesity and lung function improvement.

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Association Between Covid-19 and Heart Failure: Evidence From a Two-sample Mendelian Randomization Study

10.21203/rs.3.rs-277791/v1 ◽

2021 ◽

Author(s):

Huachen Wang ◽

Zheng Guo ◽

Yulu Zheng ◽

Bing Chen

Keyword(s):

Heart Failure ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Meta Analysis ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Reverse Causality ◽

Genome Wide ◽

Weighted Median

Abstract Background: Current research observing inconsistent associations of Corona Virus Disease 2019 (COVID-19) with heart failure (HF) are prone to bias based on reverse causality and residual confounding factors. Our aim was to apply a two-sample Mendelian randomization method to investigate whether COVID-19 has a causal effect on HF. Methods: Twenty-nine single nucleotide polymorphisms (SNPs) were proposed as candidate instrumental variables (IVs). A total of 3,523 patients with COVID-19 and 36,634 control participants were included in the genome-wide meta-analysis. We analyzed the largest genome-wide association studies (GWAS) meta-analysis of heart failure in individuals of European ancestry consisting of 47,309 patients with HF and 930,014 controls. The inverse variance weighted (IVW), the Mendelian randomization-Egger (MR-Egger) regression, the simple mode (SM), weighted median, and weighted mode were utilized for the MR analysis to test the stability and a causal effect. Results: The IVW, MR-Egger regression, SM, weighted median and weighted mode demonstrated there was no association between the genetically predicted COVID-19 infection and HF risk (OR, 1.004; 95%CI, 0.994-1.014; P=0.467; OR, 1.008; 95%CI, 0.996-1.019; P=0.218; OR, 0.968; 95%CI, 0.924-1.015; P=0.186; OR, 1.001; 95%CI, 0.988-1.014; P=0.881; OR, 1.001; 95%CI, 0.989-1.014; P=0.836; respectively). Conclusion: This two-sample Mendelian randomization analysis provided no evidence to sustain the causality of COVID-19 on HF.

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Robust inference in summary data Mendelian randomisation via the zero modal pleiotropy assumption

10.1101/126102 ◽

2017 ◽

Cited By ~ 14

Author(s):

Fernando Pires Hartwig ◽

George Davey Smith ◽

Jack Bowden

Keyword(s):

Instrumental Variable ◽

Causal Effect ◽

Association Studies ◽

Mendelian Randomisation ◽

Effect Estimate ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Weighted Median ◽

Summary Data ◽

Genetic Instruments

AbstractBackgroundMendelian randomisation (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions.MethodsHere, a new method –the mode-based estimate (MBE) – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.ResultsThe MBE presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.ConclusionsThe MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.Key MessagesSummary data Mendelian randomisation, typically in a two-sample setting, is being increasingly used due to the availability of summary association results from large genome- wide association studies.Mendelian randomisation analyses using multiple genetic instruments are prone to bias due to horizontal pleiotropy, especially when genetic instruments are selected based solely on statistical criteria.A causal effect estimate robust to horizontal pleiotropy can be obtained using the mode- based estimate (MBE).The MBE requires that the most common causal effect estimate is a consistent estimate of the true causal effect, even if the majority of instruments are invalid (i.e., the ZEro Modal Pleiotropy Assumption, or ZEMPA).Plotting the smoothed empirical density function is useful to explore the distribution of causal effect estimates, and to understand how the MBE is determined.

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The causal effect of interleukin-17 on the risk of psoriatic arthritis: a Mendelian randomization study

Rheumatology ◽

10.1093/rheumatology/keaa629 ◽

2020 ◽

Author(s):

Dongze Wu ◽

Priscilla Wong ◽

Steven H M Lam ◽

Edmund K Li ◽

Ling Qin ◽

...

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Preliminary Evidence ◽

Interleukin 17 ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Tnf Α ◽

Weighted Median

Abstract Objective To determine causal associations between genetically predicted TNF-α, IL-12p70 and IL-17 levels and risk of PsA. Methods The publicly available summary-level findings from genome-wide association studies (GWAS) was used to identify loci influencing normal physiological concentrations of TNF-α, IL-12p70 and IL-17 (n = 8293) among healthy individuals as exposure and a GWAS for PsA from the UK Biobank (PsA = 900, control = 462 033) as the outcome. A two-sample Mendelian randomization (MR) analysis was performed using the inverse-variance weighted (IVW), weighted median and MR–Egger regression methods. Sensitivity analysis and MR–Egger regression analysis were performed to evaluate the heterogeneity and pleiotropic effects of each variant. Results Single-nucleotide polymorphisms (SNPs) at genome-wide significance from GWASs on TNF-α, IL-12p70 and IL-17 were identified as the instrumental variables. The IVW method indicated a causal association between increased IL-17 level and risk of PsA (β = −0.00186 per allele, s.e. = 0.00043, P = 0.002). Results were consistent in the weighted median method (β = −0.00145 per allele, s.e. = 0.00059, P = 0.014) although the MR–Egger method suggested a non-significant association (β = −0.00133 per allele, s.e. = 0.00087; P = 0.087). Single SNP MR results revealed that the C allele of rs117556572 was robustly associated with risk of PsA (β = 0.00210, s.e. = 0.00069, P = 0.002). However, no evidence for a causal effect was observed between TNF-α, IL-12p70, decreased IL-17 levels and risk of PsA. Conclusion Our findings provide preliminary evidence that genetic variants predisposing to higher physiological IL-17 level are associated with decreased risk of PsA.

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GWASpro: a high-performance genome-wide association analysis server

Bioinformatics ◽

10.1093/bioinformatics/bty989 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2512-2514 ◽

Cited By ~ 4

Author(s):

Bongsong Kim ◽

Xinbin Dai ◽

Wenchao Zhang ◽

Zhaohong Zhuang ◽

Darlene L Sanchez ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Linear Mixed Model ◽

Association Studies ◽

Learning Curves ◽

Experimental Designs ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

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A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Journal of Endocrinology ◽

10.1530/joe-12-0144 ◽

2012 ◽

Vol 215 (1) ◽

pp. 17-28 ◽

Cited By ~ 18

Author(s):

Georg Homuth ◽

Alexander Teumer ◽

Uwe Völker ◽

Matthias Nauck

Keyword(s):

Blood Cells ◽

Large Scale ◽

Genetic Factors ◽

Association Studies ◽

Transcriptional Profiling ◽

Genome Wide Association Studies ◽

Protein Levels ◽

Future Developments ◽

Genome Wide ◽

Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

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Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

10.1101/284976 ◽

2018 ◽

Cited By ~ 6

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Confounding Bias ◽

Conserved Regions ◽

Genome Wide ◽

Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽

10.1371/journal.pgen.1009315 ◽

2021 ◽

Vol 17 (1) ◽

pp. e1009315

Author(s):

Ardalan Naseri ◽

Junjie Shi ◽

Xihong Lin ◽

Shaojie Zhang ◽

Degui Zhi

Keyword(s):

Large Scale ◽

Association Studies ◽

Scale Up ◽

Data Driven ◽

Genome Wide Association Studies ◽

Inference Method ◽

Genome Wide ◽

Familial Relationship ◽

Kinship Coefficients ◽

Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

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bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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Genetics of juvenile rheumatic diseases

10.1093/med/9780199642489.003.0043_update_002 ◽

2015 ◽

Author(s):

Anne Hinks ◽

Wendy Thomson

Keyword(s):

Risk Factors ◽

Rheumatic Diseases ◽

Large Scale ◽

Association Studies ◽

Genetic Diseases ◽

Response To Treatment ◽

Genome Wide Association Studies ◽

Established Risk Factor ◽

Genome Wide ◽

Juvenile Rheumatic Diseases

Juvenile rheumatic diseases are heterogeneous, complex genetic diseases; to date only juvenile idiopathic arthritis (JIA) has been extensively studied in terms of identifying genetic risk factors. The MHC region is a well-established risk factor but in the last few years candidate gene and large-scale genome-wide association studies have been utilized in the search for non-HLA risk factors. There are now 17 JIA susceptibility loci which reach the genome-wide significance threshold for association and a further 7 regions with evidence for association in more than one study. In addition, some subtype-specific associations are emerging. These risk loci now need to be investigated further using fine-mapping strategies and then appropriate functional studies to show how the variant alters the gene function. This knowledge will not only lead to a better understanding of disease pathogenesis for juvenile rheumatic diseases but may also aid in the classification of these heterogeneous diseases. It may identify new pathways for potential therapeutic targets and help in the prediction of disease outcome and response to treatment.

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