Miller Thomas A, Hernandez Edgar J, Gaynor J William, Russell Mark W, Newburger Jane W, Chung Wendy, Goldmuntz Elizabeth, Cnota James F, Zyblewski Sinai C, Mahle William T, Zak Victor, Ravishankar Chitra, Kaltman Jonathan R, McCrindle Brian W, Clarke Shanelle, Votava-Smith Jodie K, Graham Eric M, Seed Mike, Rudd Nancy, Bernstein Daniel, Lee Teresa M, Yandell Mark, Tristani-Firouzi Martin
Department of Pediatrics, Maine Medical Center, Portland, ME, USA.
Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.
Commun Med (Lond). 2023 Sep 27;3(1):127. doi: 10.1038/s43856-023-00361-2.
Recent large-scale sequencing efforts have shed light on the genetic contribution to the etiology of congenital heart defects (CHD); however, the relative impact of genetics on clinical outcomes remains less understood. Outcomes analyses using genetics are complicated by the intrinsic severity of the CHD lesion and interactions with conditionally dependent clinical variables.
Bayesian Networks were applied to describe the intertwined relationships between clinical variables, demography, and genetics in a cohort of children with single ventricle CHD.
As isolated variables, a damaging genetic variant in a gene related to abnormal heart morphology and prolonged ventilator support following stage I palliative surgery increase the probability of having a low Mental Developmental Index (MDI) score at 14 months of age by 1.9- and 5.8-fold, respectively. However, in combination, these variables act synergistically to further increase the probability of a low MDI score by 10-fold. The absence of a damaging variant in a known syndromic CHD gene and a shorter post-operative ventilator support increase the probability of a normal MDI score 1.7- and 2.4-fold, respectively, but in combination increase the probability of a good outcome by 59-fold.
Our analyses suggest a modest genetic contribution to neurodevelopmental outcomes as isolated variables, similar to known clinical predictors. By contrast, genetic, demographic, and clinical variables interact synergistically to markedly impact clinical outcomes. These findings underscore the importance of capturing and quantifying the impact of damaging genomic variants in the context of multiple, conditionally dependent variables, such as pre- and post-operative factors, and demography.
近期的大规模测序工作揭示了基因对先天性心脏病（CHD）病因的贡献；然而，基因对临床结局的相对影响仍鲜为人知。使用基因进行的结局分析因CHD病变的内在严重性以及与条件依赖性临床变量的相互作用而变得复杂。
应用贝叶斯网络来描述单心室CHD患儿队列中临床变量、人口统计学和基因之间的相互关系。
作为独立变量，与心脏形态异常相关的基因中的有害基因变异以及I期姑息性手术后延长呼吸机支持时间，分别使14个月大时智力发育指数（MDI）得分低的概率增加1.9倍和5.8倍。然而，综合起来，这些变量具有协同作用，可使MDI得分低的概率进一步增加10倍。已知综合征性CHD基因中不存在有害变异以及术后呼吸机支持时间较短，分别使MDI得分正常的概率增加1.7倍和2.4倍，但综合起来可使良好结局的概率增加59倍。
我们的分析表明，作为独立变量，基因对神经发育结局的贡献不大，类似于已知的临床预测因素。相比之下，基因、人口统计学和临床变量相互协同作用，对临床结局产生显著影响。这些发现强调了在多个条件依赖性变量（如术前和术后因素以及人口统计学）的背景下，捕捉和量化有害基因组变异影响的重要性。
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