Reciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.
Nat Commun
; 14(1): 1131, 2023 02 28.
Article
in En
| MEDLINE
| ID: mdl-36854672
ABSTRACT
Mendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Mendelian Randomization Analysis
Type of study:
Clinical_trials
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2023
Document type:
Article
Affiliation country:
China