Department of Epidemiology and Biostatistics, University of Florida, Gainesville, FL 32611, USA.
J Theor Biol. 2009 Nov 7;261(1):33-42. doi: 10.1016/j.jtbi.2009.07.020. Epub 2009 Jul 24.
Human height is an important trait from biological and social perspectives. Genes have been widely recognized to be involved in human body growth, but their detailed controlling mechanisms are poorly understood. Here, we present a computational model for functional mapping of quantitative trait loci (QTLs) that control trajectories of human height growth through an interactive network. The model integrates mathematical equations of human growth curves into the mixture model-based functional mapping framework, allowing the identification and mapping of individual QTLs responsible for the developmental pattern of human growth. The model was derived on a random sample of subjects from a natural population, for each of which molecular markers within candidate genes or throughout the entire genome are typed and height data from childhood to adulthood are collected. A series of testable hypotheses are formulated about the genetic control of developmental timing and duration at different stages. The model was used to characterize epistatic QTLs for height growth hidden in 548 Japanese girls which is a semi-real data set with simulated the marker genotypes. With an increasing availability of genetic polymorphic data, the model will have great implications for probing the genetic and developmental mechanisms of human body growth and its associated diseases.
人类身高在生物学和社会学方面都是一个重要的特征。基因已被广泛认为参与了人体生长，但它们的详细控制机制尚不清楚。在这里，我们提出了一个计算模型，用于通过交互网络对控制人类身高增长轨迹的数量性状基因座（QTL）进行功能映射。该模型将人类生长曲线的数学方程集成到基于混合模型的功能映射框架中，允许识别和映射负责人类生长发育模式的单个 QTL。该模型是从自然人群的随机样本中推导出来的，对于每个样本，候选基因或整个基因组内的分子标记都进行了分型，并且从儿童期到成年期的身高数据都进行了收集。还提出了一系列关于不同阶段发育时间和持续时间的遗传控制的可测试假设。该模型用于描述隐藏在 548 名日本女孩身高增长中的上位性 QTL，这是一个具有模拟标记基因型的半真实数据集。随着遗传多态性数据的日益普及，该模型将对探索人类身体生长及其相关疾病的遗传和发育机制具有重要意义。
