Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model
Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model
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DOI：
10.1111/j.0006-341x.2004.00223.x
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发表时间：
2004-09-01
期刊：
影响因子：
1.9
通讯作者：
Casella, G
中科院分区：
文献类型：
--
作者：
Wu, RL;Ma, CX;Casella, G
结合生长发育控制机制已被证明是一个强大的工具，在定位数量性状基因座（QTL）的生长轨迹。建立了基于生长规律的QTL定位策略的理论框架。这个框架可以推广到任意数量的时间点，在那里增长的测量，并成为计算更容易处理，当方差平稳性的假设。然而，在实践中，由于规模效应，这一假设可能会违反年龄特异性生长性状。在这篇文章中，我们提出了一个新的统计模型定位生长QTL，它也解决了方差平稳性的问题，通过使用转换双方（TBS）模型提倡的卡罗尔和Ruppert（1984年，美国统计协会杂志79，321-328）。基于TBS的生长QTL定位模型不仅保持了生长模型原有的生物学特性，而且提高了参数估计的准确性和精度，提高了对生长分化QTL的检测能力。使用TBS为基础的模型，我们成功地映射到一个连锁群中的一个例子的林木生长轨迹的QTL。统计和生物学特性的估计，这个增长QTL的位置和效果进行了研究，使用Monte Carlo模拟研究。我们的模型理解的遗传结构的增长的影响进行了讨论。
The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association 79, 321-328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed.