The Broad Institute, Cambridge, MA, USA.
Physiol Genomics. 2010 Jul 7;42(2):236-47. doi: 10.1152/physiolgenomics.00118.2009. Epub 2010 Apr 27.
Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. Despite this fact, it is rare that data are collected over time or in sequence in relevant studies of the determinants of these phenotypes. The costs and organizational sophistication necessary to collect repeated measurements or longitudinal data for a given phenotype are clearly impediments to this, but greater efforts in this area are needed if insights into human phenotypic expression are to be obtained. Appropriate data analysis methods for genetic association studies involving repeated or longitudinal measures are also needed. We consider the use of longitudinal profiles obtained from fitted functions on repeated data collections from a set of individuals whose similarities are contrasted between sets of individuals with different genotypes to test hypotheses about genetic influences on time-dependent phenotype expression. The proposed approach can accommodate uncertainty of the fitted functions, as well as weighting factors across the time points, and is easily extended to a wide variety of complex analysis settings. We showcase the proposed approach with data from a clinical study investigating human blood vessel response to tyramine. We also compare the proposed approach with standard analytic procedures and investigate its robustness and power via simulation studies. The proposed approach is found to be quite flexible and performs either as well or better than traditional statistical methods.
大多数（如果不是全部）人类表型都表现出时间、剂量依赖性或年龄效应。尽管如此，在决定这些表型的相关研究中，很少有随时间或按顺序收集数据的情况。对于给定的表型，收集重复测量或纵向数据所需的成本和组织复杂性显然是一个障碍，但如果要深入了解人类表型表达，就需要在这方面做出更大的努力。还需要适当的数据分析方法来进行涉及重复或纵向测量的遗传关联研究。我们考虑使用从一组个体的重复数据集中拟合函数获得的纵向谱，这些个体的相似性在具有不同基因型的个体组之间进行对比，以检验关于遗传对时变表型表达影响的假设。所提出的方法可以适应拟合函数的不确定性，以及跨时间点的加权因素，并且可以很容易地扩展到各种复杂的分析环境中。我们使用一项研究人类血管对酪胺反应的临床研究的数据来展示所提出的方法。我们还将所提出的方法与标准分析程序进行了比较，并通过模拟研究研究了其稳健性和功效。所提出的方法被发现非常灵活，其性能与传统统计方法一样好，甚至更好。
