2000 年 20 巻 2 号 p. 109-129
Genetic maps with many markers are now available for agronomic plants and animals. Statistical methods for detecting QTL using genetic maps were mainly developed for plants, because it is easier to generate inbred lines in plants. Segregating populations derived from crossing of two inbred lines, such as BC1 and F2, are the most adequate materials for QTL mapping. However, methods devised for plant data obtained from the line crossing cannot be applied to the analysis of data of animals, since they cannot fully take account of the more complex date structures of outcrossing animal populations, i. e. data on several families with relationships across families and unknown linkage phases in parents.
We report several methods for interval mapping of QTLs generally used in plants and animals, respectively. The following methods are described. (1) Maximum likelihood and least squares methods based on a simple regression model for plants and animals such as pigs, in which large full-sib families are available. (2) A method based on animal models in best linear unbiased prediction (BLUP) of breeding values for animals. (3) Sib-pair linkage tests, which was firstly introduced for human QTL mapping and is applicable to animals.
We elucidate how the difference in statistical models for QTL mapping between plants and animals corresponds to difference in data structures.