Evidence map›Paper›PMID 34793444›Full record

ArticlePLoS genetics2021

Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model.

Zhaotong Lin, Yangqing Deng, Wei Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
196citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

196 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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136 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhaotong LinDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0001-8723-4392
Yangqing DengDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0001-6218-3437
Wei PanDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0002-1159-0582

Funding

Deep Learning with Neuroimaging Genetic Data for Alzheimer's DiseaseR01AG069895 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2020 to 2024
$3.4M
Association analysis of rare variants with sequencing dataR01HL116720 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, WEI, PENG · 2013 to 2020
$3.3M
Discovering causal genes, brain regions and other risk factors for Alzheimer's DiseaseR01AG065636 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2024
$3.1M
Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL dataRF1AG067924 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2020
$1.9M
Estimation and Inference of Gene Regulatory NetworksR01GM126002 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2017 to 2020
$1.4M
Statistical Methods for Genomic DataR01GM113250 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2014 to 2017
$702k
NHLBI NIH HHS R01 HL116720NIA NIH HHS R01 AG065636NIA NIH HHS R01 AG069895NIA NIH HHS RF1 AG067924NIGMS NIH HHS R01 GM113250NIGMS NIH HHS R01 GM126002
6 · The paper itself

Abstract

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Indexed as

Genetic Predisposition to DiseaseGenetic PleiotropyGenome-Wide Association StudyHumansMendelian Randomization AnalysisPolymorphism, Single NucleotideRegression Analysis

Identifiers

PMID34793444
PMCPMC8639093

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.