Bias correction for inverse variance weighting Mendelian randomization

Mounier, Ninon; Kutalik, Zoltán

doi:10.1101/2021.03.26.437168

Cited by 66 publications

(70 citation statements)

References 39 publications

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“…The positive causal e↵ect of smoking on MI, diabetes on asthma, the protective impact of higher birth weight on asthma, or higher education on smoking intensity, all of which were missed by standard MR could reflect the increased power of LHC-MR with its use of full-genome SNPs as opposed to genome-wide significant SNPs of classical MR approaches. Estimates from classical due to sample overlap is expected to be very small, and therefore not su cient to explain any discrepancy in the results [51] . Another possible reason for the discrepancy between our findings and those of standard MR methods is the presence of a significant heritable confounder found by LHC-MR with opposite e↵ect to the estimated causal e↵ect between the pair.…”

Section: Lhc Genetic Correlation Ldsc Genetic Correlationmentioning

confidence: 93%

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Darrous

Mounier

Kutalik

2020

Preprint

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Mendelian Randomisation (MR), an increasingly popular method that estimates the causal effects of risk factors on complex human traits, has seen several extensions that relax its basic assumptions. However, most of these extensions suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritability, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results, LHC-MR unravelled new mechanisms (how being diagnosed for certain diseases might lead to improved lifestyle) and revealed potential false positive findings of standard MR methods (apparent causal effect of body mass index on educational attainment may be driven by a strong ignored confounder). Phenome-wide search to identify LHC-implied heritable confounders showed remarkable agreement between the LHC-estimated causal effects of the latent confounder and those for the potentially identified ones. Finally, LHC-MR naturally decomposes genetic correlation to causal effect-driven and confounder-driven contributions, demonstrating that the genetic correlation between systolic blood pressure and diabetes is predominantly confounder-driven.

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Section: Lhc Genetic Correlation Ldsc Genetic Correlationmentioning

confidence: 93%

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Darrous

Mounier

Kutalik

2020

Preprint

Self Cite

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“…The two IV sets provided a partial confirmation of associations due to having different strengths; while IV set A was derived from a subsample of UK Biobank that did not overlap the sample used to estimate SNP-outcome effects, it is more likely to be underpowered, whereas IV set B maximizes power at the cost of introducing possible bias due to sample overlap. Recent simulations have indicated that bias due to sample overlap is minimal when using Mendelian randomization and is often negligible compared to other biases, such as winner's curse and weak-instrument bias [46,47]. We estimated potential bias due to sample overlap using the method proposed by Burgess et al [47], implemented in an online tool (https://sb452.shinyapps.io/overlap/).…”

Section: Genetic Associationsmentioning

confidence: 99%

Vitamin D and the risk of treatment-resistant and atypical depression: A Mendelian randomization study

et al. 2021

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Observational evidence has implicated vitamin D levels as a risk factor in major depressive disorder (MDD). Confounding or reverse causation may be driving these observed associations, with studies using genetics indicating little evidence of an effect. However, genetic studies have relied on broad definitions of depression. The genetic architecture of different depression subtypes may vary since MDD is a highly heterogenous condition, implying potentially diverging requirements in therapeutic approaches. We explored the associations between vitamin D and two subtypes of MDD, for which evidence of a causal link could have the greatest clinical benefits: treatment-resistant depression (TRD) and atypical depression (AD). We used a dual approach, combining observational data with genetic evidence from polygenic risk scores (PRS) and two-sample Mendelian randomization (MR), in the UK Biobank. There was some evidence of a weak association between vitamin D and both incident TRD (Ncases = 830) and AD (Ncases = 2366) in observational analyses, which largely attenuated when adjusting for confounders. Genetic evidence from PRS and two-sample MR, did not support a causal link between vitamin D and either TRD (Ncases = 1891, OR = 1.01 [95%CI 0.78, 1.31]) or AD (Ncases = 2101, OR = 1.04 [95%CI 0.80, 1.36]). Our comprehensive investigations indicated some evidence of an association between vitamin D and TRD/AD observationally, but little evidence of association when using PRS and MR, mirroring findings of genetic studies of vitamin D on broad depression phenotypes. Results do not support further clinical trials of vitamin D in these MDD subtypes but do not rule out that small effects may exist that require larger samples to detect.

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“…However, considering potential interventions, effects of liability to educational attainment are much less useful than effects of specific education phenotypes. Fourth, the gene-exposure and gene-outcome estimates in the within-sibship GWAS two-sample MR analyses were from largely overlapping samples which could have potentially induced bias, although recent evidence suggests the degree of bias is likely to be modest 38 . Fifth, there is evidence that within-sibship models using PGS based on weighs from population GWAS could actually introduce bias 39 .…”

Section: Discussionmentioning

confidence: 99%

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

Rasheed

Jones

Boomsma

et al. 2022

Preprint

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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.

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Bias correction for inverse variance weighting Mendelian randomization

Cited by 66 publications

References 39 publications

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Vitamin D and the risk of treatment-resistant and atypical depression: A Mendelian randomization study

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

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