Schrodi Steven J, Mukherjee Shubhabrata, Shan Ying, Tromp Gerard, Sninsky John J, Callear Amy P, Carter Tonia C, Ye Zhan, Haines Jonathan L, Brilliant Murray H, Crane Paul K, Smelser Diane T, Elston Robert C, Weeks Daniel E
Center for Human Genetics, Marshfield Clinic Research Foundation Marshfield, WI, USA.
Department of Medicine, School of Medicine, University of Washington Seattle, WA, USA.
Front Genet. 2014 Jun 2;5:162. doi: 10.3389/fgene.2014.00162. eCollection 2014.
Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications.
将基因研究结果转化应用于医学实践是人类遗传学备受期待的目标。本文旨在回顾和讨论遗传学在医学相关预测中的作用。生殖系遗传学预示着疾病的发作，因此可以提供增强实验室检查和临床特征的预后信号。因此，基于基因的预测模型对临床决策和治疗选择的影响可能是深远的。然而，鉴于（i）医学性状是由遗传和环境因素之间复杂的相互作用产生的，（ii）对常见疾病易感性的潜在遗传结构了解不足，以及（iii）可复制的易感等位基因合起来仅占疾病遗传度的适度比例，构建和实施具有高效用的遗传风险预测模型存在重大挑战。尽管存在这些挑战，但该领域仍在持续取得协同进展，不断有研究积累，识别出疾病易感基因型。已经发表了几种旨在预测疾病的统计方法。在此，我们总结疾病易感性图谱绘制和药物遗传学在风险预测方面的现状，描述用于构建和评估基于基因的预测模型的方法，并讨论其应用。
