Composite Interval Mapping Of Quantitative Trait Loci In A Full-sib Family Of Forest Trees And Its Application In Populus

Composite Interval Mapping（CIM） has been widely accepted since it was proposed. It combines interval mapping with multiple regression. The basis of CIM is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. There have been several improved statistical methods developed based on composite interval mapping, but these methods can only be applied to inbred lines, such as F2 and backcross. Statistical methods for mapping QTLs in full-sib forest trees, in which the number of alleles and linkage phase can vary from locus to locus, are still not well estabilished.In this study, we extended composite interval mapping for mapping QTLs based on the segregation of different types of markers and QTLs in a full-sib family. The QTL segregation patterns were classified into three types: test cross（1:1 segregation）, F2 cross（1:2:1 segregation） and full cross（1:1:1:1 segregation）. The method provides simutaneous estimations of the QTL segregation pattern, QTL location and QTL effects.The results of simulations suggested that this model had high statistical power. The average estimates of the QTL positions and parameters were almost equal to the true values and the standard deviations were also small. When performing simulations, we first selected a few markers by forward regression and used those markers to control genetic background. There are four factors effecting the precision of mapping, including sample size, heritability, the number of selected markers and the size of the window. In practice, we need first to select some markers（by stepwise regression） and set appropriate window size to improve the precision of QTL mapping.A real example is presented to illustrate QTL mapping in full-sib forest trees based on two linkage maps of Populus deltoides Bartr. cv. ‘Lux’ I-69/55 and Populus simonii. Eight QTLs related to tree height were obtained, of which six from P.deltoides and two from P. simonii. They can explain 81.1% of the phenotypic variance. Seven QTLs related to DBH（diameter at breast height） were obtained, of which five from P.deltoides and two from P.simonii. They can explain 70.4% of the phenotypic variance.We developed an R language software pakage, FsQTLCIMmap₁.0. This pakage assumes three kinds of segregation pattern in a QTL and it can be implemented the statistical methods for QTL mapping in a full-sib family. FsQTLCIMmap₁.0 also provides a function that runs permutation tests to yield the genome-wide LR threshold and plots LR profiles along the linkage groups. R language is easy to learn and this package is convenient to use.