Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2010 Feb 9;5(2):e9039. doi: 10.1371/journal.pone.0009039.
Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods.
Based on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high.
Extensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved by accounting for the procedural complexity of CIM via an empirical approach, CIM-NPCI. Confidence intervals are a critical measure of QTL utility, but have received inadequate treatment due to a perception that QTL mapping is not sufficiently precise for procedural improvements to matter. Technological advances will continue to challenge this assumption, creating even more need for the current improvement to be refined.
改良的遗传分辨率和测序基因组的可用性使中等效应 QTL 的定位克隆在多个系统中成为现实，这强调了需要对 QTL 的定位置信区间（CI）进行精确和准确的推导。基于 QTL 似然曲线形状的支持区间（SI）方法已被证明足以用于标准区间作图，但尚未被证明适用于复合区间作图（CIM），CIM 是最常用的 QTL 作图方法之一。
基于为 Haley-Knott 回归方法映射 QTL 设计的非参数置信区间（NPCI）方法，开发了一种特定于 CIM 的方法（CIM-NPCI），以在分析自举重采样数据集时适当考虑背景标记的选择。在通过 CIM 进行的一系列模拟中，比较了 NPCI、SI 和 CIM-NPCI 方法的覆盖概率和区间宽度，其中在具有不同标记密度的染色体区域中模拟了四个遗传效应，同时将遗传率固定为 200 个孤立系群体的 0.6。CIM-NPCI 在这些条件下始终能捕获模拟的 QTL，而稍窄的 SI 和 NPCI 的失败率过高，尤其是在标记密度较高的基因组区域，这在真实研究中越来越常见。还研究了每种标记密度情况下已知的 CIM 倾向于将 QTL 峰值定位在标记处的偏倚效应。根据模拟效应相对于最近标记的位置对亚模拟的评估表明，CIM-NPCI 方法克服了这种偏差，这解释了标记密度较高时覆盖概率提高的原因。
本文通过大量模拟研究表明，通过经验方法（CIM-NPCI）考虑 CIM 的程序复杂性，可以极大地改进通常用于定位评估 CIM 结果的 QTL 置信区间方法。置信区间是 QTL 实用性的一个关键衡量标准，但由于人们认为 QTL 作图的精度不足以提高程序的精度，因此对其处理不够充分。技术进步将继续挑战这一假设，这将进一步需要改进目前的方法。
