Polygenic risk scores (PRSs), designed to capture genetic predisposition to specific traits,1 are becoming increasingly accessible at scale and are being used in physical activity research. PRSs are typically calculated by aggregating the effect sizes of single-nucleotide polymorphisms (SNPs) associated with a particular trait or disease, usually derived from genome-wide association studies (GWASs), into a score for each individual to reflect their genetic liability to that trait or disease. Various methodologies exist for constructing PRSs, ranging from simple to more complex.1 The simplest approach often involves selecting a subset of SNPs based on their associated p values from the GWAS, while more sophisticated methods may incorporate additional data, such as linkage disequilibrium patterns or functional genomic information.1 Regardless of the approach, the appeal of PRSs lies in their simplicity and versatility, especially as they can be readily computed in cohorts with existing genetic data. This makes PRSs valuable both for controlling confounding and as a research focus in their own right. This editorial aims to discuss some key challenges in using PRSs for analysing physical activity and health, focusing on the difficulty of distinguishing mechanisms behind associations and the limited clinical interpretability of effect estimates. We also offer some practical recommendations for future research. Recent studies have employed PRSs related to physical activity to investigate various outcomes, finding that high scores are associated with lower risk of cardiometabolic risk factors, coronary heart disease, stroke, hypertension, type 2 diabetes, obesity and all-cause mortality.2–4 However, the interpretation of these results is hampered by several challenges. A fundamental challenge is that several potential explanations exist for why a PRS for physical activity might be associated with health outcomes. These include a true causal effect of physical activity on health outcomes and the influence of shared genetics. Unfortunately, …

中文翻译：

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身体活动和健康的多基因风险分析：为什么相同的结果解释不同？


多基因风险评分 （PRS） 旨在捕捉特定特征的遗传易感性 1，正变得越来越容易获得大规模，并被用于身体活动研究。PRS 的计算方法是将与特定性状或疾病相关的单核苷酸多态性 （SNP） 的效应大小汇总，通常来自全基因组关联研究 （GWAS），为每个个体评分，以反映他们对该性状或疾病的遗传易感性。构建 PRS 的方法多种多样，从简单到复杂不等。最简单的方法通常涉及根据 GWAS 中 SNP 的相关 p 值选择 SNP 的子集，而更复杂的方法可能会包含其他数据，例如连锁不平衡模式或功能基因组信息。无论采用哪种方法，PRS 的吸引力在于其简单性和多功能性。 特别是因为它们可以很容易地在具有现有遗传数据的队列中计算。这使得 PRS 在控制混杂因素和作为研究重点方面都有价值。本社论旨在讨论使用 PRS 分析身体活动和健康的一些关键挑战，重点关注区分关联背后的机制的困难和效果估计的有限临床可解释性。我们还为未来的研究提供了一些实用的建议。最近的研究采用与身体活动相关的 PRS 来调查各种结果，发现高分与心脏代谢危险因素、冠心病、中风、高血压、2 型糖尿病、肥胖和全因死亡率的风险较低相关。然而，对这些结果的解释受到一些挑战的阻碍。 一个根本的挑战是，对于为什么身体活动的 PRS 可能与健康结果相关，存在几种可能的解释。这些包括身体活动对健康结果的真正因果影响以及共享遗传学的影响。不幸。。。