Gillespie Nathan A, Bell Tyler R, Hearn Gentry C, Hess Jonathan L, Tsuang Ming T, Lyons Michael J, Franz Carol E, Kremen William S, Glatt Stephen J
Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Virginia, USA.
QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
Am J Med Genet B Neuropsychiatr Genet. 2025 Jan;198(1):e33003. doi: 10.1002/ajmg.b.33003. Epub 2024 Aug 9.
Multivariate network-based analytic methods such as weighted gene co-expression network analysis are frequently applied to human and animal gene-expression data to estimate the first principal component of a module, or module eigengene (ME). MEs are interpreted as multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. As such, they have the potential to elucidate the mechanisms by which molecular genomic variation contributes to individual differences in complex traits. Although increasingly used to test for associations between modules and complex traits, the genetic and environmental etiology of MEs has not been empirically established. It is unclear if, and to what degree, individual differences in blood-derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N = 661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD = 2.6 years, range = 62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD = 0.08, range = 35%-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.
基于多元网络的分析方法，如加权基因共表达网络分析，经常应用于人类和动物的基因表达数据，以估计模块的第一主成分，即模块特征基因（ME）。ME被解释为模块内相关基因表达模式和基因间网络连通性的多变量汇总。因此，它们有可能阐明分子基因组变异导致复杂性状个体差异的机制。尽管越来越多地用于测试模块与复杂性状之间的关联，但ME的遗传和环境病因尚未通过实证确定。目前尚不清楚血液来源的ME中的个体差异在多大程度上反映了随机变异，还是由遗传或共同环境影响引起的家族聚集。我们使用生物统计学遗传分析来估计遗传和环境影响对从越南战争时期双胞胎衰老研究（VETSA）中N = 661名老年男性双胞胎样本采集的血液淋巴细胞中获得的ME的贡献，这些双胞胎在评估时的平均年龄为67.7岁（标准差 = 2.6岁，范围 = 62 - 74岁）。在检测到的26个ME中，有14个（56%）具有统计学上显著的加性遗传变异，平均遗传率为44%（标准差 = 0.08，范围 = 35% - 64%）。尽管样本量相对较小，但26个ME中有14个显示出显著的家族聚集以及遗传率估计，这表明基于血液的ME是可靠的，值得进一步探索它们与复杂性状和疾病的关联。
