Mixed model regression mapping for QTL detection in experimental crosses

This paper presents mixed model regression mapping (MMRM) as a method for mapping quantitative trait loci (QTL) in backcross and F2 data arising from crosses of inbred lines. It is related to interval mapping, composite interval mapping and other regression approaches but differs in that it tests for QTL presence in each linkage group before conditionally modeling QTL location.The three key ideas presented are to promote use of a Likelihood Ratio type of test for the presence of QTL in linkage groups before searching for QTL as a method of controlling false discovery rate, to present an alternative QTL profile to the LOD score for identifying the possible location of a QTL, and to promote the use of a local smoother to identify turning points in a profile based on evaluation at marker points rather than directly predicting all intermediate points.MMRM requires fitting a short series of models to locate and then evaluate putative QTL. Assuming marker covariates are allocated to linkage groups, MMRM first fits all the markers as independent random effects with common variance within the linkage groups. If there is no significant variance component associated with a linkage group, there is no evidence for a QTL associated with that group. Otherwise a QTL profile is predicted as a weighted sum of the marker BLUPs from which to postulate the most likely position of the QTL. A putative QTL covariate for that position is then calculated from flanking markers and added to the model. If this does not explain all the marker variance, the model is refined.Since MMRM is based on a linear mixed model, the model is easily extended to include extraneous sources of variation such as spatial variation in field experiments, to handle multiple QTL and to test for genotype by environment interactions. It is expounded using two simple examples analysed in the ASReml linear models software. Two simulation studies show that MMRM identifies QTL as reliably as but more directly than other common methods.