We report here the consensus target sequence of transposons Tc1, Tc3 and Tc5 of Caenorhabditis elegans. These sequences were obtained by molecular analysis of 1008 random new insertions which have not been exposed to natural selection. This analysis reveals consensus target sites slightly different from those previously reported, and confirms that the mariner elements Tc1 and Tc3 insert in sites which are not preferentially palindromic.

The MHC class II loci encoding cell surface antigens exhibit extremely high allelic polymorphism. There is considerable uncertainty in the literature over the relative roles of recombination and de novo mutation in generating this diversity. We studied class II sequence diversity and allelic polymorphism in two populations of Peromyscus maniculatus, which are among the most widespread and abundant mammals of North America. We find that intragenic recombination (or gene conversion) has been the predominant mode for the generation of allelic polymorphism in this species, with the amount of population recombination per base pair exceeding mutation by at least an order of magnitude during the history of the sample. Despite this, patchwork motifs of sites with high linkage disequilibrium are observed. This does not appear to be consistent with the much larger amount of recombination versus mutation in the history of the sample, unless the recombination rate is highly non-uniform over the sequence or selection maintains certain sites in linkage disequilibrium. We conclude that selection is most likely to be responsible for preserving sequence motifs in the presence of abundant recombination.

We have investigated the pattern of DNA sequence variation at the exuperantia2 locus in Drosophila pseudoobscura. This adds to the increasing dataset of genetic variation in D. pseudoobscura, a useful model species for evolutionary genetic studies. The level of silent site nucleotide diversity and the divergence from an outgroup Drosophila miranda are comparable with those for other X-linked loci. One peculiar pattern at the exu2 locus of D. pseudoobscura is a complete linkage disequilibrium between two SNPs, one of which is a replacement site. As a result, there are two distinct haplotype groups in our dataset. Based upon the comparisons with the outgroup sequences from D. miranda and Drosophila persimilis, we show that the newly derived haplotype group has lower diversity than the ancestral haplotype group. The pattern of protein evolution at exu2 shows some deviation from the neutral model. Together, these and other characteristics of the exu2 locus suggest the action of selection on the pattern of SNP variation, consistent with a partial selective sweep associated with the newly derived haplotype.

In the Australian red-claw crayfish Cherax quadricarinatus (vonMartens) (Decapoda, Parastacidae), a gonochoristic species, seven differentcombinations of intersex individuals (with both male and female genital openings)have been described. However, to date, the genetic basis for this phenomenon hasnot been investigated. This study was designed to test a simple chromosome-basedsex-determination model for C. quadricarinatus that assumes the male tobe the homogametic (ZZ) sex. According to our model, intersex individuals thatare functionally males are genetically females (WZ). Individual crosses wereperformed between intersex and female crayfish, with control crosses beingperformed between normal males and females. The control crosses yielded, in mostcases, the expected 1[ratio ]1 sex ratio in the F1 progeny. Crossesbetween intersex individuals and females yielded a 1[ratio ]3 (male[ratio ]female)sex ratio in most crosses. According to our hypothesis, one-third of the femalesproduced in a cross of a female with an intersex animal should be WW females. Thehypothesis was tested by crossing normal males with F1 females, whichwere progeny of intersex fathers. These crosses yielded almost 100% females, afinding that conforms to the above-suggested sex determination model for C.quadricarinatus and the female WZ genotype of intersex individuals.

During courtship, visual and chemical signals are often exchanged between the sexes. The proper exchange of such signals ensures intraspecific recognition. We have examined the genetic basis of interspecific differences in male mating behaviour and pheromone concentration between Drosophila simulans and D. sechellia by using Drosophila simulans/D. sechellia introgression lines. Our results show a majority of quantitative trait loci (QTLs) explaining variation in both male mating behaviour and pheromone concentration to be located on the third chromosome. One QTL found on the third chromosome explains variation in time needed to start courtship and copulation as well as time spent courting. The position of such QTL (approximately 84A–88B) with effects on courtship and copulation aspects of mating includes the candidate sex determination gene doublesex (84E5–6) and Voila (86E1–2), a gene that affects male courtship in D. melanogaster. One additional third chromosome QTL explained variation in 7-tricosene pheromone concentrations among males. The interval mapping position of this QTL (approximately 68E–76E) did not overlap with the position detected for differences in mating behaviour and the intervals did not include candidate genes previously identified as having an effect on D. melanogaster cuticular hydrocarbon production. We did not detect any directionality of the effect of Drosophila sechellia allele introgressions in male mating recognition.

Many diseases show dichotomous phenotypic variation but do not follow a simple Mendelian pattern of inheritance. Variances of these binary diseases are presumably controlled by multiple loci and environmental variants. A least-squares method has been developed for mapping such complex disease loci by treating the binary phenotypes (0 and 1) as if they were continuous. However, the least-squares method is not recommended because of its ad hoc nature. Maximum Likelihood (ML) and Bayesian methods have also been developed for binary disease mapping by incorporating the discrete nature of the phenotypic distribution. In the ML analysis, the likelihood function is usually maximized using some complicated maximization algorithms (e.g. the Newton–Raphson or the simplex algorithm). Under the threshold model of binary disease, we develop an Expectation Maximization (EM) algorithm to solve for the maximum likelihood estimates (MLEs). The new EM algorithm is developed by treating both the unobserved genotype and the disease liability as missing values. As a result, the EM iteration equations have the same form as the normal equation system in linear regression. The EM algorithm is further modified to take into account sexual dimorphism in the linkage maps. Applying the EM-implemented ML method to a four-way-cross mouse family, we detected two regions on the fourth chromosome that have evidence of QTLs controlling the segregation of fibrosarcoma, a form of connective tissue cancer. The two QTLs explain 50–60% of the variance in the disease liability. We also applied a Bayesian method previously developed (modified to take into account sex-specific maps) to this data set and detected one additional QTL on chromosome 13 that explains another 26% of the variance of the disease liability. All the QTLs detected primarily show dominance effects.

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20–40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.