Multi-trait analysis of genome-wide association summary statistics using MTAG

Turley, Patrick; Walters, Raymond; Maghzian, Omeed; Okbay, Aysu; Lee, James J.; Fontana, Mark Alan; Nguyen-Viet, Tuan Anh; Wedow, Robbee; Zacher, Meghan; Furlotte, Nicholas A.; Magnusson, Patrik K. E.; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M.; Laibson, David; Cesarini, David; Neale, Benjamin M.; Benjamin, Daniel J.

doi:10.1038/s41588-017-0009-4

Cited by 876 publications

(1,165 citation statements)

References 30 publications

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“…We use two, large independent samples to test the replicability of this association. In both samples, PGS for neuroticism was calculated based on the coefficient estimates from a large genome‐wide association study (GWAS) of neuroticism (N = 168 105) . A higher genetic propensity to neuroticism was expected to be associated with lower sleep quality concurrently and over time.…”

Section: Introductionmentioning

confidence: 99%

Polygenic score for neuroticism is related to sleep difficulties

Stéphan

Sutin

Luchetti

et al. 2020

Genes Brain and Behavior

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Neuroticism, a broad trait measure of the tendency to experience negative emotions and vulnerability to stress, is consistently related to poor sleep quality. Less is known about potential pleiotropy in the genetic risk for high neuroticism and poor sleep. Therefore, the present study examined whether polygenic score (PGS) for neuroticism is related to sleep quality in two large samples of adults. In addition, depressive symptoms, anxiety and phenotypical neuroticism were tested as mediators in both samples. Participants were 8316 individuals aged from 50 to 101 years (mean age = 68.29, SD = 9.83) from the Health and Retirement Study, and 4973 individuals aged from 63 to 67 years (mean age = 64.30, SD = 0.68) from the Wisconsin Longitudinal Study. Participants from both samples were genotyped and answered questions on sleep quality. A higher PGS for neuroticism was related to lower sleep quality concurrently and over time in both samples. Anxiety, depressive symptoms and neuroticism mediated these relationships in the two samples. Although effect sizes were small, the present study provides replicable evidence that individuals with a higher genetic predisposition to experience negative emotions and distress are at risk of sleep difficulties.

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Section: Introductionmentioning

confidence: 99%

Polygenic score for neuroticism is related to sleep difficulties

Stéphan

Sutin

Luchetti

et al. 2020

Genes Brain and Behavior

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“…Instead of calculating an ABF for an association between a given variant and a given trait measured in one GWAS, we calculate an ABF for an association between a variant and an arbitrary number of traits,

n

, measured in independent or (partially) overlapping studies. Recently, multivariate analysis of GWAS summary statistics has been implemented by the MTAG approach (Turley et al, ) and a framework involving Bayes factors to compare models of association has been developed to help determine relevant tissues in eQTL data (Flutre et al, ). When extending Wakefield's ABF to the multivariate case,

\overset{β}{true}

, which was a single effect size estimate from a single GWAS, becomes

\overset{β}{true}

, an

n

‐vector of effect size estimates from each of the

n

studies included in the meta‐analysis.…”

Section: The Methodsmentioning

confidence: 99%

“…However, combining multiple studies introduces the possibility of correlated true effects between pairs of them, which we capture in the terms

ρ_{i, j} : i, j \in {1, \dots, n}

boldΣ = ([], \begin{matrix} σ_{1}^{2} & ρ_{1, 2} σ_{1} σ_{2} & \dots & ρ_{1, n} σ_{1} σ_{n} \\ ρ_{1, 2} σ_{1} σ_{2} & σ_{2}^{2} & \dots & ρ_{2, n} σ_{2} σ_{n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ ρ_{1, n} σ_{1} σ_{n} & ρ_{2, n} σ_{2} σ_{n} & \dots & σ_{n}^{2} \end{matrix}) .

This matrix has a similar role as the

boldΩ

matrix in the MTAG approach ( Turley et al, ). Thus, by writing

f (bold-italicx; bold-italicm, bold-italicV)

for the density of a multivariate normal distribution with mean vector

bold-italicm

and covariance matrix

bold-italicV

, evaluated at

bold-italicx

, Equation becomes

ABF = \frac{f (\overset{β}{true}; bold-italic0, V_{\hat{bold-italicβ}} + boldΣ)}{f (\overset{β}{true}; bold-italic0, V_{\hat{bold-italicβ}})} .

…”

Section: The Methodsmentioning

confidence: 99%

“…By making use of the generality of the prior covariance matrix

boldΣ

, a range of other models can be defined based on particular assumptions on the relationship between traits. Where it might be appropriate to assume that the marginal effect at a variant is representative of the genome average, the estimates of the genetic correlation between two studies might be appropriate as prior information, and it has been shown that this can increase power in some scenarios (Turley et al, ).…”

Section: The Methodsmentioning

confidence: 99%

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Bayesian meta‐analysis across genome‐wide association studies of diverse phenotypes

Trochet

Pirinen

Jostins

et al. 2019

Genetic Epidemiology

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Genome‐wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta‐analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta‐analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.

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“…However, the use of this method is limited in practice, because the common risk factors shared by the trait and covariates are often not identified or measured in GWAS. In recent years, joint analytic methods of multiple traits, either using individual data or GWAS summary statistics of single traits, have been developed (Bhattacharjee et al, 2012; Chung, Yang, Li, Gelernter, & Zhao, 2014; Cotsapas et al, 2011; Hackinger & Zeggini, 2017; Huang, Johnson, & O’Donnell, 2011; Solovieff et al, 2013; Turley et al, 2018; Zhu et al, 2015). These multi-trait methods can gain substantial statistical power in certain situations, in particular in the presence of pleiotropy.…”

Section: Introductionmentioning

confidence: 99%

Adjustment for covariates using summary statistics of genome‐wide association studies

Wang

Xue

Xie

et al. 2018

Genetic Epidemiology

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Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the GIANT consortium.

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Multi-trait analysis of genome-wide association summary statistics using MTAG

Cited by 876 publications

References 30 publications

Polygenic score for neuroticism is related to sleep difficulties

Polygenic score for neuroticism is related to sleep difficulties

Bayesian meta‐analysis across genome‐wide association studies of diverse phenotypes

Adjustment for covariates using summary statistics of genome‐wide association studies

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