Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects
Mendelian randomization (MR) is a powerful and widely used method for causal inference leveraging genetic information. Here, the authors develop MRMix, an MR method using mixture models for more robust and efficient estimation of causal effects.
Guardado en:
Autores principales: | Guanghao Qi, Nilanjan Chatterjee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9449f88fcee4464bbb5f9b0d5946bae4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A robust and efficient method for Mendelian randomization with hundreds of genetic variants
por: Stephen Burgess, et al.
Publicado: (2020) -
Assessing causal estimates of the association of obesity-related traits with coronary artery disease using a Mendelian randomization approach
por: Xue Zhang, et al.
Publicado: (2018) -
The biomarker and causal roles of homoarginine in the development of cardiometabolic diseases: an observational and Mendelian randomization analysis
por: Ilkka Seppälä, et al.
Publicado: (2017) -
Estimating causal effects of atherogenic lipid-related traits on COVID-19 susceptibility and severity using a two-sample Mendelian randomization approach
por: Masahiro Yoshikawa, et al.
Publicado: (2021) -
Investigating the causal effect of smoking on hay fever and asthma: a Mendelian randomization meta-analysis in the CARTA consortium
por: Tea Skaaby, et al.
Publicado: (2017)