simmrd: An open-source tool to perform simulations in Mendelian randomization

IF 1.7 4区医学 Q3 GENETICS & HEREDITY

Genetic Epidemiology Pub Date : 2024-01-23 DOI:10.1002/gepi.22544

Noah Lorincz-Comi, Yihe Yang, Xiaofeng Zhu

引用次数: 0

Abstract

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

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simmrd：用于执行孟德尔随机化模拟的开源工具。

孟德尔随机化（MR）已成为推断疾病风险因素因果关系的常用工具。目前有超过 45 种不同的方法可用于执行 MR，反映出这一研究领域极为活跃。我们希望有一个标准的模拟环境来客观地评估现有和未来的方法。我们推出的 simmrd 是一款开源软件，用于进行模拟，评估 MR 方法在实际应用中的各种情况下的性能。研究人员可以直接修改 simmrd 的源代码，这样研究界就可以形成一个广为接受的框架，供研究人员评估不同磁共振方法的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genetic Epidemiology 医学-公共卫生、环境卫生与职业卫生

CiteScore

4.40

自引率

9.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.