A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

LeBlanc, Marissa; Zuber, Verena; Thompson, Wesley K.; Andreassen, Ole A.; Frigessi, Arnoldo; Andreassen, Bettina Kulle

doi:10.1186/s12864-018-4859-7

Cited by 48 publications

(52 citation statements)

References 26 publications

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“…Assume trait k is from cohort A with sample size n A and trait k′ is from cohort B with sample size n B , and there are n C overlapping subjects in these two cohorts. For any SNP j not associated with the traits, the correlation matrix of Z-score U kj = ffiffiffiffiffiffi V kj p among traits is invariant to SNP j 39,47 . In particular, if both traits k and k′ are quantitative, we have…”

Section: Methodsmentioning

confidence: 99%

Multi-trait analysis of rare-variant association summary statistics using MTAR

et al. 2020

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Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in genome-wide association studies. Here, we introduce multi-trait analysis of rarevariant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.

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Section: Methodsmentioning

confidence: 99%

Multi-trait analysis of rare-variant association summary statistics using MTAR

et al. 2020

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“…The sample overlap between the GWAS on psychiatric disorders due to comorbidity should also be considered. This topic had previously been investigated using data from PGC (LeBlanc et al, 2018). They found that the overlap was less than 2% for eight psychiatric disorders.…”

Section: Limitationsmentioning

confidence: 99%

The Role of Genetic Variation of BMI, Body Composition, and Fat Distribution for Mental Traits and Disorders: A Look-Up and Mendelian Randomization Study

et al. 2020

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Anthropometric traits and mental disorders or traits are known to be associated clinically and to show genetic overlap. We aimed to identify genetic variants with relevance for mental disorders/traits and either (i) body mass index (or obesity), (ii) body composition, (and/or) (iii) body fat distribution. We performed a look-up analysis of 1,005 genomewide significant SNPs for BMI, body composition, and body fat distribution in 15 mental disorders/traits. We identified 40 independent loci with one or more SNPs fulfilling our threshold significance criterion (P < 4.98 × 10 −5) for the mental phenotypes. The majority of loci was associated with schizophrenia, educational attainment, and/or intelligence. Fewer associations were found for bipolar disorder, neuroticism, attention deficit/hyperactivity disorder, major depressive disorder, depressive symptoms, and well-being. Unique associations with measures of body fat distribution adjusted for BMI were identified at five loci only. To investigate the potential causality between body fat distribution and schizophrenia, we performed two-sample Mendelian randomization analyses. We found no causal effect of body fat distribution on schizophrenia and vice versa. In conclusion, we identified 40 loci which may contribute to genetic overlaps between mental disorders/traits and BMI and/or shape related phenotypes. The majority of loci identified for body composition overlapped with BMI loci, thus suggesting pleiotropic effects.

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“…To the best of our knowledge, few studies have investigated overlapping-sample MR. Burgess et al [13] have shown that sample overlap increases type I error and leads to bias in classic MR methods. LeBlanc et al [14] have developed a correction method of overlapping samples by decorrelation. There is a pressing need for the development of a flexible MR approach that can be used for one-, two-as well as overlapping-samples.…”

Section: Introductionmentioning

confidence: 99%

Overlapping-sample Mendelian randomisation with multiple exposures: a Bayesian approach

Linyi

Guo

Berzuini

2020

BMC Med Res Methodol

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Background Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an outcome. Traditional MR methods have mainly focussed on a two-sample setting in which IV-exposure association study and IV-outcome association study are independent. However, it is not uncommon that participants from the two studies fully overlap (one-sample) or partly overlap (overlapping-sample). Methods We proposed a Bayesian method that is applicable to all the three sample settings. In essence, we converted a two- or overlapping- sample MR to a one-sample MR where data were partly unmeasured. Assume that all study individuals were drawn from the same population and unmeasured data were missing at random. Then the missing data were treated au pair with the model parameters as unknown quantities, and thus, were imputed iteratively conditioning on the observed data and estimated parameters using Markov chain Monte Carlo. We generalised our model to allow for pleiotropy and multiple exposures and assessed its performance by a number of simulations using four metrics: mean, standard deviation, coverage and power. We also compared our method with classic MR methods. Results In our proposed method, higher sample overlapping rate and instrument strength led to more precise estimated causal effects with higher power. Pleiotropy had a notably negative impact on the estimates. Nevertheless, the coverages were high and our model performed well in all the sample settings overall. In comparison with classic MR, our method provided estimates with higher precision. When the true causal effects were non-zero, power of their estimates was consistently higher from our method. The performance of our method was similar to classic MR in terms of coverage. Conclusions Our model offers the flexibility of being applicable to any of the sample settings. It is an important addition to the MR literature which has restricted to one- or two- sample scenarios. Given the nature of Bayesian inference, it can be easily extended to more complex MR analysis in medical research.

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A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

Cited by 48 publications

References 26 publications

Multi-trait analysis of rare-variant association summary statistics using MTAR

Multi-trait analysis of rare-variant association summary statistics using MTAR

The Role of Genetic Variation of BMI, Body Composition, and Fat Distribution for Mental Traits and Disorders: A Look-Up and Mendelian Randomization Study

Overlapping-sample Mendelian randomisation with multiple exposures: a Bayesian approach

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