Dataset Information

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

ABSTRACT: Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.

SUBMITTER: Morrison J

PROVIDER: S-EPMC7343608 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

Morrison Jean J Knoblauch Nicholas N Marcus Joseph H JH Stephens Matthew M He Xin X

Nature genetics 20200525 7

Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. ...[more]

PMID: 32451458

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Project description:BACKGROUND:Associations between type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) were discovered in observational studies in both European and East Asian populations. However, whether such associations are causal remains largely unknown. METHODS:We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of T2D with the risk of ALS in both European and East Asian populations. Our analysis was implemented using summary statistics obtained from large-scale genome-wide association studies with ~660,000 individuals for T2D and ~81,000 individuals for ALS in the European population, and ~191,000 individuals for T2D and ~4100 individuals for ALS in the East Asian population. The causal relationship between T2D and ALS in both populations was estimated using the inverse-variance-weighted methods and was further validated through extensive complementary and sensitivity analyses. RESULTS:Using multiple instruments that were strongly associated with T2D, a negative association between T2D and ALS was identified in the European population with the odds ratio (OR) estimated to be 0.93 (95% CI 0.88-0.99, p?=?0.023), while a positive association between T2D and ALS was observed in the East Asian population with OR?=?1.28 (95% CI 0.99-1.62, p?=?0.058). These results were robust against instrument selection, various modeling misspecifications, and estimation biases, with the Egger regression and MR-PRESSO ruling out the possibility of horizontal pleiotropic effects of instruments. However, no causal association was found between T2D-related exposures (including glycemic traits) and ALS in the European population. CONCLUSION:Our results provide new evidence supporting the causal neuroprotective role of T2D on ALS in the European population and provide empirically suggestive evidence of increasing risk of T2D on ALS in the East Asian population. Our results have an important implication on ALS pathology, paving ways for developing therapeutic strategies across multiple populations.

Dataset Information

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

Publications

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

Similar Datasets

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