Department of Psychiatry, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
Department of Statistics and Actuarial Science, Faculty of Science, University of Hong Kong, Hong Kong SAR, China.
Genet Epidemiol. 2020 Jun;44(4):395-399. doi: 10.1002/gepi.22294. Epub 2020 Mar 27.
We present an important characteristic of trio models which may lead to bias and loss of power when one parent is unmodeled in trio analyses. Motivated by recent interest in estimating parental effects on postnatal and later-life phenotypes, we consider a causal model where each parent has both an effect on their child's phenotype which is mediated through the genotype transmitted to the child and a direct effect on the phenotype through the parentally provided environment. We derive the power and bias of models in which one parent's genotype is not modeled, showing that while the effect of the child's genotype is biased in the direction of the unmodeled parent's effect as expected, the estimated effect of the observed parent's genotype is also biased in the opposite direction. While this phenomenon may not be intuitive under the assumption of random mating, it can be explained by intermediate confounding of the child's genotype-phenotype effect. These observations have implications for the accurate estimation of maternal and paternal effects in trio data sets with missing genotype data.
我们提出了 trio 模型的一个重要特征，当 trio 分析中忽略了一位父母时，这可能会导致偏差和降低功效。受最近对估计父母对产后和后期表型影响的兴趣的启发，我们考虑了一个因果模型，其中每个父母都对其子女的表型有影响，这种影响是通过传递给子女的基因型介导的，并且通过父母提供的环境对表型有直接影响。我们推导出了忽略一位父母基因型的模型的功效和偏差，结果表明，虽然正如预期的那样，子女基因型的效应在未建模的父母效应的方向上存在偏差，但观察到的父母的基因型的估计效应也存在相反方向的偏差。虽然在随机交配的假设下，这种现象可能不是直观的，但可以通过子女基因型-表型效应的中间混杂来解释。这些观察结果对准确估计 trio 数据集缺失基因型数据中的母系和父系效应具有重要意义。
