Li Hongying, Huang Zhongwen, Gai Junyi, Wu Song, Zeng Yanru, Li Qin, Wu Rongling
Department of Statistics, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2007 Nov 28;2(11):e1245. doi: 10.1371/journal.pone.0001245.
Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms.
尽管体型的个体发育变化及其相关的异速生长已经被研究了一个多世纪，但对于其潜在的遗传和发育机制，人们基本上一无所知。造成这种无知的原因之一是缺乏一个概念框架来制定数据收集的实验设计和数据分析的统计模型。我们开发了一个框架模型，用于揭示异速生长个体发育变化的遗传机制。该模型将个体发育生长和异速生长的数学方面纳入到一个用于数量性状基因座（QTL）定位的最大似然框架中。作为一个定量平台，该模型允许测试许多具有生物学意义的假设，以探索调节个体发育和异速生长的QTL的多效性基础。已经进行了模拟研究和大豆实例的实际数据分析，以研究该模型的统计行为并验证其实际应用。所提出的统计模型将有助于研究复杂表型的遗传结构，从而更好地洞察生物体发育模式和过程的机制调节。
