Knott Sara A
School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.
Philos Trans R Soc Lond B Biol Sci. 2005 Jul 29;360(1459):1435-42. doi: 10.1098/rstb.2005.1671.
Regression has always been an important tool for quantitative geneticists. The use of maximum likelihood (ML) has been advocated for the detection of quantitative trait loci (QTL) through linkage with molecular markers, and this approach can be very effective. However, linear regression models have also been proposed which perform similarly to ML, while retaining the many beneficial features of regression and, hence, can be more tractable and versatile than ML in some circumstances. Here, the use of linear regression to detect QTL in structured outbred populations is reviewed and its perceived shortfalls are revisited. It is argued that the approach is valuable now and will remain so in the future.
回归分析一直是数量遗传学家的重要工具。通过与分子标记的连锁分析来检测数量性状基因座（QTL）时，人们提倡使用最大似然法（ML），这种方法可能非常有效。然而，也有人提出了线性回归模型，其性能与最大似然法相似，同时保留了回归分析的许多有益特性，因此在某些情况下可能比最大似然法更易于处理且用途更广。本文回顾了在结构化远交群体中使用线性回归来检测QTL的方法，并重新审视了其被认为的不足之处。本文认为该方法目前很有价值，并且在未来仍将如此。
