Kadarmideen Haja N, Li Yongjun, Janss Luc L G
Statistical Animal Genetics Group, Institute of Animal Science, Swiss Federal Institute of Technology, ETH Zentrum (UNS D7), Universitaetstrasse 65, Zurich 8092, Switzerland.
Genet Res. 2006 Oct;88(2):119-31. doi: 10.1017/S0016672306008391. Epub 2006 Sep 15.
An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15-100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20-80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.
一种用于复杂多基因疾病（作为二元性状）的区间数量性状基因座（QTL）定位方法被开发出来，该疾病表现出QTL与环境的相互作用（QEI），适用于远交群体的家系内分析。在上述背景下，主要目标是研究遗传模型的选择，并比较 liability 或广义区间定位（GIM）和线性回归区间定位（RIM）方法。使用了两种不同的遗传模型：一种具有主要QTL和QEI效应（QEI模型），另一种仅具有主要QTL效应（QTL模型）。通过RIM和GIM对30多种二元疾病数据以及6种连续数据进行了模拟和分析。使用表格值进行显著性检验，结果表明，RIM在检验相互作用时的错误检测率（FDR）增加，这归因于二元尺度上的尺度效应。GIM在检验相互作用时没有高FDR问题。使用经验阈值，实际上意味着RIM在检验相互作用时需要更高的阈值，可以修复RIM增加的FDR，但这种经验阈值必须针对每种情况推导得出，因为FDR的大小取决于二元尺度上的发病率。对于QEI模型，RIM在QTL方差和位置估计中仍比GIM存在更高的偏差（对真实值高估或低估15 - 100%）和更高的标准误差。因此，建议使用GIM进行具有QEI的疾病QTL定位。在存在QEI的情况下，当平均QTL效应较小时（在仅具有主要QTL的模型不太强大的情况下），包含QEI的模型检测QTL的能力更强（增加20 - 80%）。提出了自上而下的模型选择方法，即首先对QEI进行全面检验，然后随后简化模型。方法和结果将适用于人类、植物和动物的QTL定位实验。
