scholarly journals Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study

2022 ◽  
Author(s):  
Laurence Howe ◽  
Humaira Rasheed ◽  
Paul R Jones ◽  
Dorret I Boomsma ◽  
David M Evans ◽  
...  
Keyword(s):  
Cigarette Smoking ◽  
Educational Attainment ◽  
Health Outcomes ◽  
Genetic Variant ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Individual Level ◽  
Beneficial Effects ◽  
Level Data ◽  
Random Segregation

Previous Mendelian randomization (MR) studies using population samples (population-MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. Mendelian randomization using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis. Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72,932 siblings from UK Biobank and the Norwegian HUNT study and summary-level data from a within-sibship Genome-wide Association Study including over 140,000 individuals. Both population and within-sibship MR estimates provided evidence that educational attainment influences BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect. These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independent of potential demographic and family-level confounders.

scholarly journals Atrial fibrillation and kidney function: A bidirectional Mendelian randomization study

2020 ◽  
Author(s):  
Sehoon Park ◽  
Soojin Lee ◽  
Yaerim Kim ◽  
Yeonhee Lee ◽  
Min Woo Kang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Kidney Function ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Individual Level ◽  
Level Data ◽  
Lower Egfr

Aims: To investigate the causal effects between atrial fibrillation (AF) and kidney function. Methods and Results: We performed a bidirectional Mendelian randomization (MR) analysis implementing the results from large-scale genome-wide association study (GWAS) for estimated glomerular filtration rate (eGFR) by the CKDGen (N = 1,046,070) and for AF (N = 588,190) to determine genetic instruments. A bidirectional two-sample MR based on summary-level data was performed. Inverse variance weighted method was the main MR method. For replication, an allele-score based MR was performed by individual-level data within the UK Biobank cohort of white British ancestry with eGFR values (N= 321,260). The genetical predisposition to AF was significantly associated with lower eGFR [beta -0.002 (standard error 0.0005), P < 0.001] and higher risk of chronic kidney disease [beta 0.051 (0.012), P < 0.001], and the significance remained in various MR sensitivity analyses. The causal estimates were consistent when we limited the analysis to individuals of European ancestry. The genetically predicted eGFR did not show significant association with risk of AF [beta -0.189 (0.184), P = 0.305]. The results were similar in allele-score based MR, as allele-score for AF was significantly associated with lower eGFR [beta -0.069 (0.021), P < 0.001] but allele-score for eGFR did not show significant association with risk of AF [beta -0.001 (0.009), P = 0.907]. Conclusions: Our study supports that genetical predisposition to AF is a causal risk factor for kidney function impairment. However, effect from kidney function on AF was not identified in this study.

scholarly journals Causal Associations Between Educational Attainment and 14 Urological and Reproductive Health Outcomes: A Mendelian Randomization Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Menghua Wang ◽  
Zhongyu Jian ◽  
Xiaoshuai Gao ◽  
Chi Yuan ◽  
Xi Jin ◽  
...  
Keyword(s):  
Reproductive Health ◽  
Educational Attainment ◽  
Health Outcomes ◽  
Lower Risk ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Bonferroni Correction ◽  
The Impact

Background: The impact of educational attainment (EA) on multiple urological and reproductive health outcomes has been explored in observational studies. Here we used Mendelian randomization (MR) to investigate whether EA has causal effects on 14 urological and reproductive health outcomes.Methods: We obtained summary statistics for EA and 14 urological and reproductive health outcomes from genome-wide association studies (GWAS). MR analyses were applied to explore the potential causal association between EA and them. Inverse variance weighted was the primary analytical method.Results: Genetically predicted one standard deviation (SD) increase in EA was causally associated with a higher risk of prostate cancer [odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05–1.25, P = 0.003] and a reduced risk of kidney stone (OR 0.73, 95% CI 0.62–0.87, P &lt; 0.001) and cystitis (OR 0.76, 95% CI 0.67–0.86, P &lt; 0.001) after Bonferroni correction. EA was also suggestively correlated with a lower risk of prostatitis (OR 0.76, 95% CI 0.59–0.98, P = 0.037) and incontinence (OR 0.64, 95% CI 0.47–0.87, P = 0.004). For the bioavailable testosterone levels and infertility, sex-specific associations were observed, with genetically determined increased EA being related to higher levels of testosterone in men (β 0.07, 95% CI 0.04–0.10, P &lt; 0.001), lower levels of testosterone in women (β −0.13, 95% CI−0.16 to−0.11, P &lt; 0.001), and a lower risk of infertility in women (OR 0.74, 95% CI 0.64–0.86, P &lt; 0.001) but was not related to male infertility (OR 0.79, 95% CI 0.52–1.20, P = 0.269) after Bonferroni correction. For bladder cancer, kidney cancer, testicular cancer, benign prostatic hyperplasia, and erectile dysfunction, no causal effects were observed.Conclusions: EA plays a vital role in urological diseases, especially in non-oncological outcomes and reproductive health. These findings should be verified in further studies when GWAS data are sufficient.

scholarly journals 204Effects of adiposity on the human proteome: Mendelian randomization study using individual-level data

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Lucy Goudswaard ◽  
Joshua Bell ◽  
David Hughes ◽  
Klaudia Walter ◽  
Nicole Soranzo ◽  
...  
Keyword(s):  
Plasma Proteins ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Genetic Risk Score ◽  
Human Proteome ◽  
Sex Hormone Binding Globulin ◽  
Cardiometabolic Health ◽  
Linear Regression Models ◽  
Standard Deviation Increase ◽  
Individual Level

Abstract Background Variation in body mass index (BMI) is associated with cardiometabolic health outcomes such as diabetes, but the mechanism(s) leading from BMI to disease remain unclear. This study used proteomic data measured by SomaLogic from healthy adults from the INTERVAL study to explore the effect of BMI on 3,622 unique plasma proteins using observational and genetically informed methods. Methods Linear regression models were used, complemented by one-sample Mendelian randomization (MR) analyses. A BMI genetic risk score (GRS) comprised of 654 SNPs from a recent genome-wide association study (GWAS) of adult BMI was used in both observational and MR analysis. Results Observationally, BMI was associated with 1,576 proteins at p &lt;1.4x10-5 including leptin and sex hormone binding globulin (SHBG). The BMI-GRS was positively associated with BMI (R2=0.028) but not with reported confounders. MR analysis indicated a causal association between each standard deviation increase in BMI and eight unique proteins at p &lt;1.4x10-5, including leptin (0.63 SD, 95% CI 0.48-0.79, p = 1.6x10-15) and SHBG (-0.45 SD, 95% CI -0.65 to -0.25, p = 1.4x10-5). There was strong agreement in the direction and magnitude of observational and MR estimates (R2 = 0.33). Finally, there was evidence that proteins which showed associations with BMI were enriched in cardiovascular disease. Conclusions This study provides evidence for a profound impact of higher adiposity on the human proteome. Such protein alterations could be important mechanistic drivers of obesity-related diseases. Key messages Changes in plasma proteins could be important intermediates between obesity and the onset of disease.

scholarly journals Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriaan van der Graaf ◽  
◽  
Annique Claringbould ◽  
Antoine Rimbert ◽  
Harm-Jan Westra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Summary Statistics ◽  
Low Density Lipoprotein Cholesterol ◽  
Causal Inferences ◽  
Individual Level ◽  
Level Data

Abstract Inference of causality between gene expression and complex traits using Mendelian randomization (MR) is confounded by pleiotropy and linkage disequilibrium (LD) of gene-expression quantitative trait loci (eQTL). Here, we propose an MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data, even when only one eQTL variant is present. In simulations, MR-link shows false-positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other tested MR methods and coloc. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals with expression and protein QTL summary statistics from blood and liver identifies 25 genes causally linked to LDL-C. These include the known SORT1 and ApoE genes as well as PVRL2, located in the APOE locus, for which a causal role in liver was not known. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

scholarly journals An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting

Biostatistics ◽  
2020 ◽  
Author(s):  
Andrew J Grant ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Robust Estimator ◽  
Simulation Studies ◽  
Individual Level ◽  
Large Numbers ◽  
Level Data ◽  
Randomization Analysis

Summary Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.

scholarly journals PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS

2020 ◽  
Author(s):  
Nick Strayer ◽  
Jana K Shirey-Rice ◽  
Yu Shyr ◽  
Joshua C Denny ◽  
Jill M Pulley ◽  
...  
Keyword(s):  
Genetic Variant ◽  
Statistical Tests ◽  
R Package ◽  
Supplementary Information ◽  
Health Records ◽  
Individual Level ◽  
Level Data ◽  
Phenotype Data ◽  
Tests Of Association ◽  
Web App

Abstract Summary Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene–disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. Availability and implementation A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Approximate conditional phenotype analysis based on genome wide association summary statistics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peitao Wu ◽  
Biqi Wang ◽  
Steven A. Lubitz ◽  
Emelia J. Benjamin ◽  
James B. Meigs ◽  
...  
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data ◽  
A Genome ◽  
Phenotype Analysis

AbstractBecause single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.

scholarly journals Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

2020 ◽  
Author(s):  
Ciarrah Barry ◽  
Junxi Liu ◽  
Rebecca Richmond ◽  
Martin K Rutter ◽  
Deborah A Lawlor ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
General Procedure ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Individual Level ◽  
Level Data ◽  
Summary Data ◽  
Collider Bias

AbstractOver the last decade the availability of SNP-trait associations from genome-wide association studies data has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification.In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. A weighted sum of these estimates is then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes.Our approach is closely related to the work of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our paper serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

scholarly journals Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

PLoS Genetics ◽  
2021 ◽  
Vol 17 (8) ◽  
pp. e1009703
Author(s):  
Ciarrah Barry ◽  
Junxi Liu ◽  
Rebecca Richmond ◽  
Martin K. Rutter ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
General Procedure ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Individual Level ◽  
Level Data ◽  
Summary Data ◽  
Collider Bias

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

The stratification of education systems and social background inequality of educational opportunity

2021 ◽  
pp. 002071522110330
Author(s):  
Claudia Traini
Keyword(s):  
Educational Attainment ◽  
Educational Opportunity ◽  
Social Background ◽  
European Social Survey ◽  
Two Dimensions ◽  
Validity And Reliability ◽  
Education Systems ◽  
Individual Level ◽  
Level Data ◽  
Contextual Indicators

This article aims to identify the moderating effect of two dimensions of the stratification of education systems (the extent to which the first selection is based on students’ ability and the age of first selection) on social background gradient in educational attainment. Individual-level data of the European Social Survey (round 1 to 9) is complemented with new contextual indicators measuring various education systems’ characteristics. This article’s contribution to the debate is twofold. First, it simultaneously investigates two dimensions of the stratification of education systems that have never been analyzed in cross-country studies investigating long-term educational outcomes. Second, it provides a series of indicators of education systems’ characteristics collected by means of an online expert survey whose validity and reliability is also tested. Findings show that the two dimensions of the stratification of education systems have opposite effects. As the first selection is increasingly based on students’ ability, social background gradient in educational attainment increases. In contrast, postponing the age of first selection decreases social inequality in educational opportunity.

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