With the availability of dense, highly informative marker maps, it has recently become feasible to map genes (Quantitative Trait Loci or QTL) accounting for part of the heritability of continuously distributed traits in experimental crosses as well as outbred populations. QTL mapping efforts have almost invariably revealed a limited number of loci with effects of a magnitude clearly departing from the predictions of the infinitesimal model (a model introduced to facilitate mathematical treatment of quantitative traits rather than to truly reflect their underlying biology). As most experimental designs would have limited detection power, which could lead to an overestimation of the identified gene effects, interpretation of results from QTL mapping studies must be viewed with caution. However, numerous independent confirmation studies leave little doubt that most quantitative traits indeed involve a limited suite of loci with major effect. This assertion seems to hold not only for QTL mapped in crosses between divergent lines, but— more importantly—for QTL segregating in outbred populations as well (for review, see Paterson 1995). Despite the sometimes unexpected magnitude of the identified QTL effects, the lack of simple correspondence between genotype and phenotype in complex trait analysis precludes the unambiguous identification of recombinant individuals. This may limit the achievable mapping resolution of QTL, posing a serious threat to the efficacy of positional (candidate) cloning for QTL considerably. QTL mapping efforts, whether performed in pedigrees or by exploiting linkage disequilibrium, are likely to leave geneticists with a portion of the genome that contains tens if not hundreds of genes and many DNA sequence polymorphisms to examine to identify the causal variant. Mutations causing monogenic inherited diseases are often destructive enough to leave little doubt about their causality. Even if the functional consequences of such mutations were less transparent, demonstration of a perfect correspondence between genotype and phenotype strongly implicates the corresponding gene, if not mutation. In the case of quantitative inheritance, the ambiguous genotype– phenotype relationship, as well as the possibly more subtle nature of the causal mutations, may complicate considerably the distinction between neutral and causative polymorphisms in the candidate region. Imaginative geneticists will certainly find a number of strategies to untangle this Gordian knot (e.g., Risch and Merikangas 1996). Increasingly, however, rodent models appear potentially to be one of the most valuable assets in QTL quests. This is illustrated vividly in an article by Hu and colleagues, dealing with comparative QTL mapping for Salmonella resistance in species as distantly related as mice and chicken (Hu et al., this issue) It is well established that genes contribute to individual differences in resistance to a variety of viral, bacterial, and parasitic pathogens (for review, see Malo and Skamene 1994; Hill 1996). A striking example of a monogenic disease resistance in man is the virtual immunity to HIV infection of individuals homozygous for a deletion in the CCR5 receptor (Liu et al. 1996; Samson et al. 1996). Genetic control of host resistance to infection is often more complex, however, and thought to involve multiple genes. This is, for instance, the case when studying resistance to Salmonella in poultry. In backcrosses obtained from susceptible and resistant chicken lines, survival time after inoculation behaves very much as a quantitative trait, suggesting the contribution of several QTL (Hu et al., this issue). The importance of Salmonella contamination of poultry products as a cause of food-borne disease in humans amply justifies efforts to identify the underlying QTL, which could lead to more efficient control strategies. Genetic resistance to Salmonella infection is also well documented in mice and it is known to be a multifactorial entity as well. However, judicious strain choice and refinement of the phenotype has allowed the dissection of murine resistance/susceptibility to Salmonella in a series of distinct entities, each segregating as simple Mendelian traits in specific matings (for review, see Malo and Skamene 1994). Two of these in particular have been the subject of considerable attention: Lps and Bcg. Measuring LPS (lipopolysaccharide, a major component of the outer membrane of Gram-negative bacteria)-induced spleen cell proliferation or liver CFU counts after inoculation with Salmonella in (resistant 2 C3H/HeJ)F1 2 C3H/HeJ backcrosses, reveals two nonoverlapping populations. This was interpreted as evidence for the segregation of a locus, Lps, with major effect on responsiveness to LPS and, concomitantly, regulation of preimmune susceptibility to infection with Gram-negative bacteria, including Salmonella. Using >1000 backcross individuals, the proposed Lps locus has been mapped recently to sub-centimorgan resolution on mouse chromosome 4 (Qureshi et al. 1996). Although the actual Lps gene has not been identified yet, it seems reasonable to anticipate its succesful positional cloning in the not-too-distant future. In a similar approach, using spleen CFU counts after infection with Mycobacterium bovis (BCG) as a discrete phenotype in 1000 (C57L/J 2 C57BL/ 6J)F1 2 C57BL/6J backcross progeny, the Bcg locus was mapped by linkage analysis to a 0.3-cM interval on mouse chromosome 1 (Malo et al. 1993). The Bcg locus (also known as Ity or Lsh) modulates the capacity of macrophages Insight/Outlook