FIQT: a simple, powerful method to accurately estimate effect sizes in genome scans

Tb, Bigdeli; Lee, D; Riley, B.P.; Vi, Vladimirov; Ah, Fanous; Ks, Kim; Bacanu, Silviu‐Alin

doi:10.1101/019299

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…However, the index individual outcome GWASs combined the UKB with independent, but demographically similar, samples of the same trait (UKB + International Lung Cancer Consortium for lung cancer (30,31), UKB + Psychiatric Genetic Consortium and 23andMe for depression (32), UKB + FinnGen for COPD and hypertension (33,34), UKB + CARDIoGRAMplusC4D for cardiovascular disease (35), and UKB + the European ancestry sub-sample of MEGASTROKE for stroke (36)). When needed, we tested the same-population assumption using the MRSamePopTest R package (37), and addressed the Winner’s Curse by using False discovery rate Inverse Quantile Transformation (FIQT) winners curse correction (26,38).…”

Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Assessing the Causal Effects of Environmental Tobacco Smoke Exposure: A meta-analytic Mendelian randomisation study

Woolf

Rajasundaram

Sallis

et al. 2023

Preprint

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Objectives: Cigarette smoking is a major cause of global morbidity and premature mortality. There is evidence that exposure to environmental tobacco smoke (ETS; "second-hand smoking" or "passive smoking") contributes substantially to ill health. However, it is difficult to establish causality given well-described problems of confounding and selection bias. We applied Mendelian randomisation (MR) to investigate the causal effects of ETS exposure. Design: We employed six MR estimation approaches: the effects of each parent's smoking on their offspring, the effects of each parent's smoking on the other parent, and the effects of self-reported exposure to second-hand smoking both inside and outside of the home. Setting: Genome-wide association studies (GWASs) with sample sizes ranging from 397,732 to 909,629 individuals of predominantly European ancestry. Interventions: To mimic exposure to ETS in the first four approaches, we used an individual's exposure to their relative's genetic liability to smoke, conditional on the original individual's genetic liability to smoke. In the final two approaches we used instruments associated with self-reported exposure to ETS. Outcomes: Lung cancer, chronic obstructive pulmonary disease (COPD), stroke, cardiovascular disease, hypertension, and depression. Results: Our findings support a causal effect of genetically predicted ETS exposure on lung cancer and COPD (odds per hour exposed in a typical week = 1.41 [95% CI: 1.23 to 1.61] and 1.11 [95% CI: 1.07 to 1.16] respectively). We did not find evidence supporting an effect on other outcomes. Discussion: ETS may cause both lung cancer and COPD. Although our results do not support an effect of ETS on other outcomes, this might reflect low statistical power to detect smaller effect sizes. These results support public health measures to limit exposure to ETS.

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Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Assessing the Causal Effects of Environmental Tobacco Smoke Exposure: A meta-analytic Mendelian randomisation study

Woolf

Rajasundaram

Sallis

et al. 2023

Preprint

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“…Note that the threshold chosen by this method is not the same as the threshold implicitly suggested by only considering the top K sample elements. We still consider the top K sample elements, even if this method produces some zero mean estimates. “Empirical Bayes estimator with gold standard, generalized maximum likelihood (GMLEB) estimator” of density f as in Jiang & Zhang (). “TN shrinkage estimator with fixed thresholds”

c = 1, 3

as in Ghosh, Zou, & Wright () (G1, G3). “FIQT estimator” as in Bigdeli et al ().…”

Section: A Simulation Studymentioning

confidence: 99%

“…Many authors have recognized the perils of the winner's curse and myriad interesting and fruitful estimators have been proposed to account for it. Approaches that have been developed include those relying on empirical Bayes, Tweedie's formula and density estimation (Johnstone & Silverman, ; Jiang & Zhang, ; Muralidharan, ; Efron, ; Xu, Craiu, & Sun, ; Ferguson et al, ; Wager, 2013); those relying on classical bias correction and resampling (Sun & Bull, ; Zollner & Pritchard, ; Ghosh, Zou, & Wright, ; Zhong & Prentice, ; Simon & Simon, ; Bigdeli et al, ); and thresholding (Donoho & Johnstone, ; Abramovich et al, ).…”

Section: Introductionmentioning

confidence: 99%

Post‐selection point and interval estimation of signal sizes in Gaussian samples

2017

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We tackle the problem of the estimation of a vector of underlying means (signal sizes) from a single vector‐valued observation y. Often one is interested in estimating only a subvector of signals corresponding to a set of selected, “interesting” sample elements. These “interesting” sample elements tend to have the largest absolute size, gleaned by applying some selection procedure like that of Benjamini & Hochberg (2015). Previous work on this estimation task proposes the reduction in size of the largest (absolute) sample elements either via shrinkage (like James–Stein) or by subtracting biases estimated using empirical Bayes methodology. We take a novel approach and adapt recent developments by Lee et al. (2016) in post‐selection inference. Adapting and applying their distributional results to our problem post‐selection point and interval estimators for underlying signal sizes are proposed. Simulations suggest that our estimator seems to perform quite well against competitors. Furthermore we prove an upper bound to the so‐called “worst case risk” of our estimator—when combined with the Benjamini–Hochberg selection procedure—and show that it is within a constant multiple of the minimax risk over a rich set of parameter spaces meant to evoke sparsity. The Canadian Journal of Statistics 45: 128–148; 2017 © 2017 Statistical Society of Canada

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“…This results in variants appearing to have a larger association than they actually do. We therefore further filtered these variants using the False discovery rate Inverse Quantile Transformation (FIQT) winners curse correction developed by the SSGAC [ 41 ]. This uses an analogy between multiple testing and winners curse to apply an easy-to-implement correction to effect estimates.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Lifetime Effect of Children on Wellbeing Using Two-Sample Mendelian Randomisation

Woolf

Sallis

Munafò

2023

Genes

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Background: Observational research implies a negative effect of having children on wellbeing. Objectives: To provide Mendelian randomisation evidence of the effect of having children on parental wellbeing. Design: Two-sample Mendelian randomisation. Setting: Non-clinical European ancestry participants. Participants: We used the UK Biobank (460,654 male and female European ancestry participants) as a source of genotype-exposure associations, the Social Science Genetics Consortia (SSGAC) (298,420 male and female European ancestry participants), and the Within-Family Consortia (effective sample of 22,656 male and female European ancestry participants) as sources of genotype-outcome associations. Interventions: The lifetime effect of an increase in the genetic liability to having children. Primary and secondary outcome measures: The primary analysis was an inverse variance weighed analysis of subjective wellbeing measured in the 2016 SSGAC Genome Wide Association Study (GWAS). Secondary outcomes included pleiotropy robust estimators applied in the SSGAC and an analysis using the Within-Family consortia GWAS. Results: We did not find strong evidence of a negative (standard deviation) change in wellbeing (β = 0.153 (95% CI: −0.210 to 0.516) per child parented. Secondary outcomes were generally slightly deflated (e.g., −0.049 [95% CI: −0.533 to 0.435] for the Within-Family Consortia and 0.090 [95% CI: −0.167 to 0.347] for weighted median), implying the presence of some residual confounding and pleiotropy. Conclusions: Contrary to the existing literature, our results are not compatible with a measurable negative effect of number of children on the average wellbeing of a parent over their life course. However, we were unable to explore non-linearities, interactions, or time-varying effects.

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FIQT: a simple, powerful method to accurately estimate effect sizes in genome scans

Cited by 8 publications

References 25 publications

WITHDRAWN: Assessing the Causal Effects of Environmental Tobacco Smoke Exposure: A meta-analytic Mendelian randomisation study

WITHDRAWN: Assessing the Causal Effects of Environmental Tobacco Smoke Exposure: A meta-analytic Mendelian randomisation study

Post‐selection point and interval estimation of signal sizes in Gaussian samples

Exploring the Lifetime Effect of Children on Wellbeing Using Two-Sample Mendelian Randomisation

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