Expectations for papers performing Mendelian randomization analyses.
作者信息
Williams Scott M, Tang Hua, Cooper Gregory M, O'Donnell-Luria Anne, Girirajan Santhosh, Dudley Aimée M, Goriely Anne, Kutalik Zoltán, Zhu Xiaofeng, Sirugo Giorgio, Epstein Michael P
机构信息
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America.
Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
出版信息
PLoS Genet. 2025 Jul 17;21(7):e1011767. doi: 10.1371/journal.pgen.1011767. eCollection 2025 Jul.
Abstract
摘要
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本文引用的文献
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2
Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.基于全基因组关联研究汇总统计数据的孟德尔随机化方法因果推断的基准测试。
Am J Hum Genet. 2024 Aug 8;111(8):1717-1735. doi: 10.1016/j.ajhg.2024.06.016. Epub 2024 Jul 25.
3
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption.提高两样本汇总数据孟德尔随机化的准确性：超越 NOME 假设。
Int J Epidemiol. 2019 Jun 1;48(3):728-742. doi: 10.1093/ije/dyy258.
4
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.多变量孟德尔随机化在单样本和两样本汇总数据设置中的检验。
Int J Epidemiol. 2019 Jun 1;48(3):713-727. doi: 10.1093/ije/dyy262.
5
The MR-Base platform supports systematic causal inference across the human phenome.MR-Base 平台支持在人类表型全范围内进行系统因果推断。
Elife. 2018 May 30;7:e34408. doi: 10.7554/eLife.34408.
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MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.孟德尔随机化：使用汇总数据执行孟德尔随机化分析的 R 包。
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