Comparison of variance estimators for meta-analysis of instrumental variable estimates

Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate. Methods: Two versions of the delta method (IV before or after pooling), four bootstrap estimators, a jack-knife estimator and a heteroscedasticity-consistent (HC) variance estimator were compared using simulation. Two types of meta-analyses were compared, a two-stage meta-analysis pooling results, and a one-stage meta-analysis pooling datasets. Results: Using a two-stage meta-analysis, coverage of the point estimate using bootstrapped estimators deviated from nominal levels at weak instrument settings and/or outcome probabilities ≤ 0.10. The jack-knife estimator was the least biased resampling method, the HC estimator often failed at outcome probabilities ≤ 0.50 and overall the delta method estimators were the least biased. In the presence of between-study heterogeneity, the delta method before meta-analysis performed best. Using a one-stage meta-analysis all methods performed equally well and better than two-stage meta-analysis of greater or equal size. Conclusions: In the presence of between-study heterogeneity, two-stage meta-analyses should preferentially use the delta method before meta-analysis. Weak instrument bias can be reduced by performing a one-stage meta-analysis

Comparison of variance estimators for meta-analysis of instrumental variable estimates

Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate. Methods: Two versions of the delta method (IV before or after pooling), four bootstrap estimators, a jack-knife estimator and a heteroscedasticity-consistent (HC) variance estimator were compared using simulation. Two types of meta-analyses were compared, a two-stage meta-analysis pooling results, and a one-stage meta-analysis pooling datasets. Results: Using a two-stage meta-analysis, coverage of the point estimate using bootstrapped estimators deviated from nominal levels at weak instrument settings and/or outcome probabilities ≤ 0.10. The jack-knife estimator was the least biased resampling method, the HC estimator often failed at outcome probabilities ≤ 0.50 and overall the delta method estimators were the least biased. In the presence of between-study heterogeneity, the delta method before meta-analysis performed best. Using a one-stage meta-analysis all methods performed equally well and better than two-stage meta-analysis of greater or equal size. Conclusions: In the presence of between-study heterogeneity, two-stage meta-analyses should preferentially use the delta method before meta-analysis. Weak instrument bias can be reduced by performing a one-stage meta-analysis

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.

MOTIVATION: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r(2)) of the variants. However, haplotypes rather than pairwise r(2), are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this paper, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. RESULTS: Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits (GIANT) height data, HAPRAP performs well with a small training sample size (N<2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by SNPs with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization). AVAILABILITY: The HAPRAP package and documentation are available online: http://apps.biocompute.org.uk/haprap

Mendelian Randomisation study of the influence of eGFR on coronary heart disease

Impaired kidney function, as measured by reduced estimated glomerular filtration rate (eGFR), has been associated with increased risk of coronary heart disease (CHD) in observational studies, but it is unclear whether this association is causal or the result of confounding or reverse causation. In this study we applied Mendelian randomisation analysis using 17 genetic variants previously associated with eGFR to investigate the causal role of kidney function on CHD. We used 13,145 participants from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium and 194,427 participants from the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease (CARDIoGRAMplusC4D) consortium. We observed significant association of an unweighted gene score with CHD risk (odds ratio = 0.983 per additional eGFR-increasing allele, 95% CI = 0.970-0.996, p = 0.008). However, using weights calculated from UCLEB, the gene score was not associated with disease risk (p = 0.11). These conflicting results could be explained by a single SNP, rs653178, which was not associated with eGFR in the UCLEB sample, but has known pleiotropic effects that prevent us from drawing a causal conclusion. The observational association between low eGFR and increased CHD risk was not explained by potential confounders, and there was no evidence of reverse causation, therefore leaving the remaining unexplained association as an open question

Marginal role for 53 common genetic variants in cardiovascular disease prediction

OBJECTIVE: We investigated discrimination and calibration of cardiovascular disease (CVD) risk scores when genotypic was added to phenotypic information. The potential of genetic information for those at intermediate risk by a phenotype-based risk score was assessed. METHODS: Data were from seven prospective studies including 11 851 individuals initially free of CVD or diabetes, with 1444 incident CVD events over 10 years' follow-up. We calculated a score from 53 CVD-related single nucleotide polymorphisms and an established CVD risk equation 'QRISK-2' comprising phenotypic measures. The area under the receiver operating characteristic curve (AUROC), detection rate for given false-positive rate (FPR) and net reclassification improvement (NRI) index were estimated for gene scores alone and in addition to the QRISK-2 CVD risk score. We also evaluated use of genetic information only for those at intermediate risk according to QRISK-2. RESULTS: The AUROC was 0.635 for QRISK-2 alone and 0.623 with addition of the gene score. The detection rate for 5% FPR improved from 11.9% to 12.0% when the gene score was added. For a 10-year CVD risk cut-off point of 10%, the NRI was 0.25% when the gene score was added to QRISK-2. Applying the genetic risk score only to those with QRISK-2 risk of 10%-<20% and prescribing statins where risk exceeded 20% suggested that genetic information could prevent one additional event for every 462 people screened. CONCLUSION: The gene score produced minimal incremental population-wide utility over phenotypic risk prediction of CVD. Tailored prediction using genetic information for those at intermediate risk may have clinical utility

Causal Associations of Adiposity and Body Fat Distribution with Coronary Heart Disease, Stroke Subtypes, and Type 2 Diabetes Mellitus: A Mendelian Randomization Analysis

BACKGROUND: The implications of different adiposity measures on cardiovascular disease etiology remain unclear. In this article, we quantify and contrast causal associations of central adiposity (waist-to-hip ratio adjusted for body mass index [WHRadjBMI]) and general adiposity (body mass index [BMI]) with cardiometabolic disease. METHODS: Ninety-seven independent single-nucleotide polymorphisms for BMI and 49 single-nucleotide polymorphisms for WHRadjBMI were used to conduct Mendelian randomization analyses in 14 prospective studies supplemented with coronary heart disease (CHD) data from CARDIoGRAMplusC4D (Coronary Artery Disease Genome-wide Replication and Meta-analysis [CARDIoGRAM] plus The Coronary Artery Disease [C4D] Genetics; combined total 66 842 cases), stroke from METASTROKE (12 389 ischemic stroke cases), type 2 diabetes mellitus from DIAGRAM (Diabetes Genetics Replication and Meta-analysis; 34 840 cases), and lipids from GLGC (Global Lipids Genetic Consortium; 213 500 participants) consortia. Primary outcomes were CHD, type 2 diabetes mellitus, and major stroke subtypes; secondary analyses included 18 cardiometabolic traits. RESULTS: Each one standard deviation (SD) higher WHRadjBMI (1 SD≈0.08 U) associated with a 48% excess risk of CHD (odds ratio [OR] for CHD, 1.48; 95% confidence interval [CI], 1.28-1.71), similar to findings for BMI (1 SD≈4.6 kg/m(2); OR for CHD, 1.36; 95% CI, 1.22-1.52). Only WHRadjBMI increased risk of ischemic stroke (OR, 1.32; 95% CI, 1.03-1.70). For type 2 diabetes mellitus, both measures had large effects: OR, 1.82 (95% CI, 1.38-2.42) and OR, 1.98 (95% CI, 1.41-2.78) per 1 SD higher WHRadjBMI and BMI, respectively. Both WHRadjBMI and BMI were associated with higher left ventricular hypertrophy, glycemic traits, interleukin 6, and circulating lipids. WHRadjBMI was also associated with higher carotid intima-media thickness (39%; 95% CI, 9%-77% per 1 SD). CONCLUSIONS: Both general and central adiposity have causal effects on CHD and type 2 diabetes mellitus. Central adiposity may have a stronger effect on stroke risk. Future estimates of the burden of adiposity on health should include measures of central and general adiposity