Arehart Christopher H, Lin Meng, Gibson Raine A, Raghavan Sridharan, Gignoux Christopher R, Stanislawski Maggie A, Grotzinger Andrew D, Evans Luke M
Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA.
Nat Commun. 2025 Aug 13;16(1):7494. doi: 10.1038/s41467-025-62730-w.
Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity between sexes, and differing health risks based on adipose distribution. Here, we construct a 4-factor genomic structural equation model using 18 measures, unveiling shared and distinct genetic architectures underlying birth size, abdominal size, adipose distribution, and adiposity. Multivariate genome-wide associations reveal the adiposity factor is enriched specifically in neural tissues and pathways, while adipose distribution is enriched more broadly across physiological systems. In addition, polygenic scores for the adiposity factor predict many adverse health outcomes, while those for body size and composition predict a more limited subset. Finally, we characterize the factors' genetic correlations with obesity-related traits and examine the druggable genome by constructing a bipartite drug-gene network to identify potential therapeutic targets.
肥胖相关疾病是可预防死亡的主要原因之一,且在全球范围内患病率不断上升。身体大小和组成是复杂的性状,由于环境和遗传影响、纵向变化、性别差异以及基于脂肪分布的不同健康风险,很难对其进行特征描述。在此,我们使用18项指标构建了一个四因素基因组结构方程模型,揭示了出生时大小、腹部大小、脂肪分布和肥胖程度背后共同和独特的遗传结构。多变量全基因组关联研究表明,肥胖因素在神经组织和通路中特异性富集,而脂肪分布在更广泛的生理系统中富集。此外,肥胖因素的多基因评分可预测许多不良健康结果,而身体大小和组成的多基因评分只能预测更有限的一部分。最后,我们描述了这些因素与肥胖相关性状的遗传相关性,并通过构建二分药物-基因网络来检查可药物基因组,以识别潜在的治疗靶点。