Drosophila melanogaster resistance against the parasitoid wasp Leptopilina boulardi is under the control of a single gene (Rlb), with two alleles, the resistant one being dominant. Using strains bearing deletions, we previously demonstrated that the 55E2–E6; 55F3 region on chromosome 2R is involved in the resistance phenomenon. In this paper, we first restricted the Rlb containing region by mapping at the molecular level the breakpoints of the Df(2R)Pc66, Df(2R)P34 and Df(2R)Pc4 deficiencies, using both chromosomal in situ hybridization and Southern analyses. The resistance gene was localized in a 100 kb fragment, predicted to contain about 10 different genes. Male recombination genetic experiments were then performed, leading to identification of two possible candidates for the Rlb gene. Potential involvement of one of this genes, edl/mae, is discussed.

Chromosome segment substitution (CSS) lines have the potential for use in QTL fine mapping and map-based cloning. The standard t-test used in the idealized case that each CSS line has a single segment from the donor parent is not suitable for non-idealized CSS lines carrying several substituted segments from the donor parent. In this study, we present a likelihood ratio test based on stepwise regression (RSTEP-LRT) that can be used for QTL mapping in a population consisting of non-idealized CSS lines. Stepwise regression is used to select the most important segments for the trait of interest, and the likelihood ratio test is used to calculate the LOD score of each chromosome segment. This method is statistically equivalent to the standard t-test with idealized CSS lines. To further improve the power of QTL mapping, a method is proposed to decrease multicollinearity among markers (or chromosome segments). QTL mapping with an example CSS population in rice consisting of 65 non-idealized CSS lines and 82 chromosome segments indicated that a total of 18 segments on eight of the 12 rice chromosomes harboured QTLs affecting grain length under the LOD threshold of 2·5. Three major stable QTLs were detected in all eight environments. Some minor QTLs were not detected in all environments, but they could increase or decrease the grain length constantly. These minor genes are also useful in marker-assisted gene pyramiding.

Selective sweeps of variation caused by fixation of major genes may have a dramatic impact on the genetic gain from background polygenic variation, particularly in the genome regions closely linked to the major gene. The response to selection can be restrained because of the reduced selection intensity and the reduced effective population size caused by the increase in frequency of the major gene. In the context of a selected population where fixation of a known major gene is desired, the question arises as to which is the optimal path of increase in frequency of the gene so that the selective sweep of variation resulting from its fixation is minimized. Using basic theoretical arguments we propose a frequency path that maximizes simultaneously the effective population size applicable to the selected background and the selection intensity on the polygenic variation by minimizing the average squared selection intensity on the major gene over generations up to a given fixation time. We also propose the use of mating between carriers and non-carriers of the major gene, in order to promote the effective recombination between the major gene and its linked polygenic background. Using a locus-based computer simulation assuming different degrees of linkage, we show that the path proposed is more effective than a similar path recently published, and that the combination of the selection and mating methods provides an efficient way to palliate the negative effects of a selective sweep.

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.