Luo Yu L L, Haworth Claire M A, Plomin Robert
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China.
Twin Res Hum Genet. 2010 Oct;13(5):426-36. doi: 10.1375/twin.13.5.426.
Using longitudinal cross-lagged analysis to infer causal directions of reciprocal effects is one of the most important tools in the developmental armamentarium. The strength of these analyses can be enhanced by analyzing the genetic and environmental aetiology underlying cross-lagged relationships, for which we present a novel approach here. Our approach is based on standard Cholesky decomposition. Standardized path coefficients are employed to assess genetic and environmental contributions to cross-lagged associations. We indicate how our model differs importantly from another approach that does not in fact analyze genetic and environmental contributions to cross-lagged associations. As an illustration, we apply our approach to the analysis of the cross-lagged relationships between self-perceived abilities and school achievement from age 9 to age 12. Self-perceived abilities of 3852 pairs of twins from the UK Twins Early Development Study were assessed using a self-report scale. School achievement was assessed by teachers based on UK National Curriculum criteria. The key cross-lagged association between self-perceived abilities at age 9 and school achievement at age 12 was mediated by genetic influences (28%) as well as shared (55%) and non-shared (16%) environment. The reverse cross-lagged association from school achievement at 9 to self-perceived abilities at 12 was primarily genetically mediated (73%). Unlike the approach to cross-lagged genetic analysis used in recent research, our approach assesses genetic and environmental contributions to cross-lagged associations per se. We discuss implications of finding that genetic factors contribute to the cross-lag between self-perceived abilities at age 9 and school achievement at age 12.
使用纵向交叉滞后分析来推断相互效应的因果方向是发展研究工具库中最重要的工具之一。通过分析交叉滞后关系背后的遗传和环境病因，可以增强这些分析的力度，我们在此提出一种新方法。我们的方法基于标准的Cholesky分解。采用标准化路径系数来评估遗传和环境对交叉滞后关联的贡献。我们指出了我们的模型与另一种实际上并未分析遗传和环境对交叉滞后关联贡献的方法的重要区别。作为一个例证，我们将我们的方法应用于分析9岁至12岁自我认知能力与学业成绩之间的交叉滞后关系。使用自我报告量表评估了来自英国双胞胎早期发展研究的3852对双胞胎的自我认知能力。教师根据英国国家课程标准对学业成绩进行评估。9岁时的自我认知能力与12岁时的学业成绩之间的关键交叉滞后关联由遗传影响（28%）以及共享（55%）和非共享（16%）环境介导。从9岁时的学业成绩到12岁时的自我认知能力的反向交叉滞后关联主要由遗传介导（73%）。与近期研究中使用的交叉滞后遗传分析方法不同，我们的方法评估遗传和环境对交叉滞后关联本身的贡献。我们讨论了发现遗传因素对9岁时的自我认知能力与12岁时的学业成绩之间的交叉滞后有贡献的意义。
