Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Clin Chem. 2011 Mar;57(3):475-81. doi: 10.1373/clinchem.2010.154005. Epub 2010 Dec 16.
Reference intervals that incorporate genetic information could reduce the misidentification of unusual test results caused by non-disease-associated genetic variation and increase the detection of results indicating underlying pathology. Subdividing reference groups by genetic effects, however, may lead to increased uncertainty around reference interval endpoints (because of the smaller subgroup sample sizes), thus offsetting any benefits.
We evaluated CLSI guidelines to develop a method appropriate for partitioning reference intervals on the basis of genetic variants with dominant or recessive effects. This method uses information available before reference samples are recruited, thus allowing a preliminary decision regarding partitioning to be made before sampling. We used this method to evaluate the example of Gilbert syndrome.
The decision point for partitioning occurs when the percentage of total variance attributable to a dominant or recessive genetic polymorphism exceeds 4%. Similarly, partitioning decision curves are presented based on difference in means between 2 subgroups, sample SD, and subgroup or allele frequency. Laboratory-specific partitioned reference intervals for Gilbert syndrome appear to be statistically warranted for white and African-American populations, but not for Asian populations.
We present a simple method to evaluate whether partitioning based on dominant or recessive genetic effects is statistically justified. Important limitations remain that, in many situations, will preclude integration of genetic, laboratory, and clinical information. As society moves toward personalized medicine, additional research is needed on how to evaluate patient normality while accounting for additive genetic, multigenic, and other multifactorial effects.
纳入遗传信息的参考区间可以减少由于非疾病相关遗传变异导致的异常检测结果的错误识别，并增加对潜在病理结果的检测。然而，通过遗传效应细分参考组可能会导致参考区间端点的不确定性增加（由于子组样本量较小），从而抵消任何益处。
我们评估了 CLSI 指南，以开发一种适用于基于具有显性或隐性效应的遗传变异来划分参考区间的方法。该方法使用在招募参考样本之前可用的信息，从而可以在采样之前对分区做出初步决策。我们使用该方法评估了吉尔伯特综合征的例子。
当归因于显性或隐性遗传多态性的总方差百分比超过 4%时，就会出现分区决策点。同样，根据 2 个子组之间的均值差异、样本标准差以及子组或等位基因频率，呈现分区决策曲线。对于白人和非裔美国人，吉尔伯特综合征的实验室特定分区参考区间似乎在统计学上是合理的，但对于亚洲人群则不然。
我们提出了一种简单的方法来评估基于显性或隐性遗传效应的分区是否在统计学上是合理的。在许多情况下，仍然存在重要的限制，这些限制将排除遗传、实验室和临床信息的整合。随着社会向个性化医疗发展，需要进一步研究如何在考虑加性遗传、多基因和其他多因素效应的情况下评估患者的正常情况。
